Ali Khoshlahjeh Sedgh | Engineering | Best Researcher Award

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Mr. Ali Khoshlahjeh Sedgh | Engineering | Best Researcher Award

Co-Author at K. N. Toosi University of Technology, Iran

Ali Khoshlahjeh Sedgh is a highly motivated and accomplished electrical engineer with a deep passion for control systems and cybersecurity within cyber-physical systems. He holds both Bachelor’s and Master’s degrees in Electrical Engineering from K. N. Toosi University of Technology, where he consistently ranked among the top of his class. Ali has demonstrated excellence in academic performance, earning prestigious scholarships from the Iran National Elites Foundation and Ghalamchi Educational Foundation. His Master’s thesis, focused on implementing reinforcement learning methods for cyber-attack detection in liquid-level control systems, showcases his skill in combining theoretical models with practical application. Ali’s interests span fault detection, system identification, adaptive and robust control, and the integration of machine learning techniques such as neural networks and reinforcement learning into industrial control environments. He has authored several publications in high-ranking journals and conferences, highlighting his commitment to research and innovation. In addition to his technical expertise, he is an experienced educator and lab coordinator, having guided student projects and managed experimental research facilities. Ali’s work is characterized by a strong foundation in mathematical modeling, system design, and implementation, and his long-term vision is to contribute to the development of resilient, secure, and intelligent control systems for critical infrastructures worldwide.

Professional Profile

Education

Ali Khoshlahjeh Sedgh earned his Master of Science degree in Electrical Engineering with a specialization in Control from K. N. Toosi University of Technology, Tehran, graduating in 2024 with an outstanding GPA of 4.0 (19.08/20). His thesis, supervised by Prof. Hamid Khaloozadeh, focused on the “Practical Implementation of Reinforcement Learning Methods for Attack Detection in a Liquid Level Control Cyber-Physical System,” exemplifying his ability to integrate artificial intelligence techniques with industrial control systems. His graduate coursework included top marks in challenging subjects such as Fault Detection, System Identification, Adaptive Control, Optimal Filtering, and Robust Control. Prior to his master’s, Ali completed his Bachelor of Science in Electrical Engineering from the same university, graduating in 2021 with a GPA of 3.88/4. His undergraduate thesis involved designing a solar-powered forest fire alarm system using SMS module communication. Throughout his academic career, he consistently achieved top ranks in control engineering and was accepted into the Master’s program without an entrance exam due to his exceptional performance. Ali’s education is deeply rooted in both theoretical principles and practical experimentation, forming a strong foundation for his research in intelligent and secure control systems. His academic training reflects his dedication, curiosity, and capability for innovation in the field.

Professional Experience

Ali Khoshlahjeh Sedgh has built substantial professional experience through both academic and industrial roles, demonstrating a balance between research, teaching, and practical engineering applications. Since 2022, he has served as the Laboratory Coordinator at the Instrumentation Lab of K. N. Toosi University of Technology. In this role, he has managed research projects, supervised laboratory experiments, maintained equipment, organized exams, and supported student internships. His responsibilities included implementing cyber-physical security measures, designing experimental setups, and applying fault detection techniques in real systems. Ali’s involvement in the lab has allowed him to practically test advanced control strategies, including PI, LQT, and adaptive controllers, in coupled-tank systems. His commitment to knowledge sharing is further highlighted by his teaching experience, where he has worked as an instructor and teaching assistant in courses such as Engineering Probability. Additionally, Ali gained industry experience as an intern and later as an electrical engineer at Fahm Electronics from 2021 to 2022. During this time, he worked on medical rehabilitation equipment and industrial projects, including the design and development of a 3-degree-of-freedom platform. His strong work ethic earned him top evaluations. Ali’s professional journey showcases a dynamic profile of technical versatility, research leadership, and a strong orientation toward solving real-world engineering problems.

Research Interests

Ali Khoshlahjeh Sedgh’s research interests lie at the intersection of control engineering, cyber-physical systems, and artificial intelligence, with a focus on developing secure, resilient, and intelligent systems. He is particularly passionate about Fault Detection and Identification (FDI), where he explores both signal-based and model-based techniques to enhance system reliability in real-time industrial applications. System Identification also plays a central role in his work, allowing him to model and simulate complex dynamic systems accurately using both non-parametric and parametric methods. Ali has a strong interest in Adaptive and Robust Control, emphasizing strategies that ensure system stability and performance under uncertainties and disturbances. He is equally engaged in applying Machine Learning—especially Reinforcement Learning (RL) and Neural Networks (NN)—to control problems, including attack detection in cyber-physical systems. His recent research centers on using reinforcement learning methods to detect and mitigate cyber-attacks, such as denial-of-service (DoS), in liquid-level control systems. Through a combination of theoretical foundations and hands-on implementations, Ali aims to build control systems that can adaptively respond to anomalies and security threats. He envisions future applications of his research in smart grids, autonomous vehicles, and industrial automation, where system safety and resilience are increasingly critical in the face of evolving technological and cybersecurity challenges.

Research Skills

Ali Khoshlahjeh Sedgh possesses a robust set of research skills that span theoretical modeling, simulation, implementation, and experimental validation of advanced control systems. He is proficient in using MATLAB and Simulink for simulation and algorithm development, and has developed numerous tools for system identification, adaptive control, estimation theory, and fault detection. His coding skills in Python, C, and C++ complement his ability to apply machine learning and signal processing techniques in both time and frequency domains. Ali has implemented methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and classifiers including KNN, Bayesian approaches, and Neural Networks such as MLP and RBF for fault diagnosis tasks. In estimation theory, he has used optimal filters like Kalman Filter, Wiener Filter, and maximum likelihood-based methods for state and parameter estimation. Ali has practically applied these techniques in a real coupled-tank system where he modeled and diagnosed faults and detected cyber-attacks using tools like Wireshark and protocols via Kali Linux. His control system toolbox includes robust PI controllers, LQT controllers, adaptive observers, and STR models. His strong command over experimental research, hardware-software integration, and system analysis reflects his ability to transform theoretical constructs into practical solutions for critical infrastructure systems.

Awards and Honors

Ali Khoshlahjeh Sedgh’s academic and research excellence has been consistently recognized through multiple awards and honors. He was ranked 2nd among all Master of Science students in Electrical Engineering – Control at K. N. Toosi University of Technology in 2024, a testament to his outstanding academic record and contribution to research. Earlier, in 2021, he graduated as the 3rd top student in the Control sub-major during his bachelor’s degree, which led to his direct admission into the master’s program without the need for a national entrance examination. Ali’s talent was further acknowledged through his receipt of scholarships from the Iran National Elites Foundation between 2021 and 2023, awarded to high-potential students contributing to science and technology in Iran. Additionally, he received a scholarship from the Ghalamchi Educational Foundation during his early undergraduate years in recognition of his academic promise. His active participation and presentation at international conferences—such as ITMS 2023 in Latvia—showcase his engagement with the global research community. These accolades reflect not only Ali’s scholarly dedication and innovative thinking but also his leadership potential and ability to stand out in highly competitive academic environments.

Conclusion

Ali Khoshlahjeh Sedgh represents the ideal convergence of deep technical expertise, hands-on research capability, and forward-thinking innovation in the field of control engineering. With a strong educational foundation from K. N. Toosi University of Technology and consistent recognition as a top-performing student, Ali has built a multifaceted academic and professional profile. His work bridges theory and practice, especially in developing intelligent, resilient control systems that address real-world issues such as cyber threats and fault tolerance in cyber-physical environments. Ali’s commitment to excellence is evident in his peer-reviewed publications, experimental projects, and his roles as both a laboratory coordinator and educator. He is driven by a desire to make meaningful contributions to modern engineering challenges, particularly in ensuring the security and reliability of automated systems. His future ambitions include pursuing advanced research, collaborating on interdisciplinary projects, and contributing to innovations in smart infrastructure, autonomous systems, and industrial automation. With a collaborative spirit, a deep curiosity for learning, and a relentless pursuit of practical solutions, Ali is well-positioned to lead and innovate in both academic and industry-driven environments. His journey so far reflects not just skill, but a vision for shaping the future of secure and adaptive control systems.

Publications Top Notes

  1. Title: Resilient Control for Cyber-Physical Systems Against Denial-of-Service Cyber Attacks Using Kharitonov’s Theorem
    Authors: H.R. Chavoshi, A.K. Sedgh, H. Khaloozadeh
    Year: 2023
    Citations: 2

  2. Title: Enhancing Cybersecurity in Nonlinear Networked Control Systems Through Robust PI Controller Design and Implementation Against Denial-of-Service Attacks
    Authors: A.H. Salasi, H.R. Chavoshi, O. Payam, A.K. Sedgh, H. Khaloozadeh
    Year: 2023
    Citations: 1

  3. Title: Practical Implementation of Multiple Faults in a Coupled-Tank System: Verified by Model-Based Fault Detection Methods
    Authors: H.R. Chavoshi, A.K. Sedgh, M.A. Shoorehdeli, H. Khaloozadeh
    Year: 2023
    Citations: 1

Reza Amjadifard | Engineering | Best Researcher Award

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Assist. Prof. Dr. Reza Amjadifard | Engineering | Best Researcher Award

Faculty member at Iranian Space research Center, Iran 

Reza Amjadifard is a seasoned researcher and educator in geotechnical engineering, with a strong emphasis on soil mechanics, foundation design, and sustainable ground improvement techniques. With over a decade of academic and field experience, he has contributed extensively to both the theoretical and applied dimensions of civil engineering. Reza holds a Ph.D. in Geotechnical Engineering and has served in academic and research positions in Iran, Malaysia, and other parts of Southeast Asia. He is known for his work on soil stabilization using recycled and environmentally friendly materials, a topic that reflects his deep commitment to sustainable development. His scholarly work includes numerous peer-reviewed journal articles, conference presentations, and research collaborations that span continents. Reza’s professional journey is marked by a seamless integration of teaching, research, and real-world applications. He is recognized for his ability to lead multidisciplinary teams, mentor graduate students, and secure competitive research funding. In addition to his technical capabilities, Reza possesses strong communication and leadership skills, which have helped him contribute to academic program development and institutional partnerships. Through his career, Reza has consistently demonstrated a forward-thinking approach to geotechnical challenges, making him a valuable contributor to both academia and industry.

Professional Profile

Education

Reza Amjadifard’s educational journey is rooted in a deep curiosity for solving complex engineering problems and a passion for sustainable infrastructure development. He earned his Bachelor of Science in Civil Engineering from Islamic Azad University in Iran, where he laid the foundation for his technical knowledge in structural analysis, hydraulics, and soil mechanics. Driven by a growing interest in geotechnical engineering, he pursued a Master of Science in Geotechnical Engineering, also at Islamic Azad University, where he conducted research on slope stability and earth reinforcement techniques. His master’s thesis explored innovative methods for improving soil strength, igniting his long-term research interests in ground improvement and soil behavior. Reza further advanced his academic career by earning a Ph.D. in Geotechnical Engineering from Universiti Sains Malaysia (USM), one of Southeast Asia’s top research institutions. His doctoral research focused on the use of recycled materials in soil stabilization, combining environmental sustainability with engineering efficiency. Throughout his academic career, Reza consistently achieved high academic distinctions and published numerous papers based on his thesis work. His formal education has been complemented by international workshops, seminars, and certifications that have kept him abreast of emerging technologies and methodologies in civil and geotechnical engineering.

Professional Experience

Reza Amjadifard has cultivated a robust and multidisciplinary professional background in civil engineering, spanning over a decade of academic and practical contributions. He began his academic career as a Lecturer at Islamic Azad University in Iran, where he taught courses in geotechnical engineering, soil mechanics, and foundation design. During this time, he also supervised numerous undergraduate and graduate student projects, fostering a passion for mentorship and academic leadership. His work in the field progressed with collaborative projects involving slope stability, soil improvement, and foundation engineering, allowing him to apply theoretical knowledge to real-world geotechnical challenges. Following his relocation to Malaysia, Reza joined Universiti Sains Malaysia (USM) as a Research Fellow, where he contributed to funded research projects focusing on sustainable ground improvement techniques and innovative uses of recycled materials in geotechnical applications. His international experience expanded further with research engagements in Australia and other parts of Southeast Asia, where he worked alongside diverse teams to address region-specific geotechnical issues such as soft soil stabilization and coastal erosion. Reza’s experience seamlessly integrates teaching, research, and field applications, showcasing his capacity to contribute across academic and industry sectors. His professional journey highlights not only technical expertise but also a strong commitment to advancing sustainable and innovative solutions in geotechnical engineering.

Research Interest

Reza Amjadifard’s research interests lie at the intersection of geotechnical engineering, environmental sustainability, and materials science. A significant portion of his work focuses on ground improvement techniques using environmentally friendly and recycled materials, such as waste tire chips, industrial by-products, and natural fibers. These innovations aim to reduce the environmental footprint of civil engineering practices while improving soil stability and bearing capacity. Reza is particularly interested in the behavior of soft soils under various loading and environmental conditions, including the effects of moisture content, chemical treatment, and dynamic forces. His research also delves into slope stability analysis, foundation performance, and soil-structure interaction, providing practical solutions for infrastructure in challenging geological settings. Reza is keen on integrating experimental and numerical methods in his studies, often employing advanced geotechnical software to simulate soil behavior and validate laboratory findings. Furthermore, he is exploring smart and adaptive geotechnical systems, including sensor-based monitoring techniques for early warning in landslide-prone regions. His interdisciplinary approach connects geotechnical engineering with sustainability, resilience, and emerging technologies, making his research highly relevant in the context of climate change and urban expansion. Reza’s work contributes meaningfully to safer, more durable, and eco-friendly infrastructure development.

Research Skills

Reza Amjadifard possesses a comprehensive set of research skills that span both experimental and analytical domains within geotechnical engineering. His expertise includes advanced laboratory testing of soils, such as direct shear tests, triaxial compression tests, consolidation tests, and permeability analysis. He is skilled in developing and modifying testing procedures to assess the effectiveness of novel soil stabilization materials, especially those derived from waste and recycled sources. In addition to hands-on laboratory capabilities, Reza is proficient in the use of numerical modeling tools such as PLAXIS, GeoStudio, and FLAC, which he applies to simulate soil behavior, foundation systems, and slope stability under varying conditions. He also brings strong statistical analysis skills using software like SPSS and MATLAB, which support data interpretation and model calibration. Reza’s research skill set extends to project planning, grant writing, and research paper publication. He has led and participated in interdisciplinary projects funded by both academic institutions and industry, demonstrating his ability to collaborate effectively. His skills in technical writing and presentation have helped him communicate complex findings to both technical and non-technical audiences. Overall, his diverse research competencies make him an asset to teams focused on sustainable geotechnical innovation and infrastructure resilience.

Awards and Honors

Reza Amjadifard’s dedication to research excellence and academic service has earned him numerous awards and honors throughout his career. During his doctoral studies at Universiti Sains Malaysia, he received the prestigious Graduate Research Assistantship for his groundbreaking work in sustainable soil stabilization, a recognition awarded to top-tier doctoral candidates. His research contributions have been acknowledged through Best Paper Awards at several international geotechnical and civil engineering conferences, highlighting the impact and quality of his scholarly output. Reza has also been honored with research grants from governmental and academic bodies, including funding for interdisciplinary projects that address environmental and infrastructural challenges in developing regions. In addition, he has been invited to serve as a peer reviewer for several high-impact journals in the fields of geotechnical engineering, environmental geotechnology, and construction materials, recognizing his expertise and thought leadership. His excellence in teaching was acknowledged by Islamic Azad University, where he received the “Outstanding Lecturer” award for his engaging and innovative teaching methods. These accolades reflect Reza’s continuous pursuit of academic and research excellence, his commitment to mentorship, and his contributions to the advancement of geotechnical engineering both locally and internationally.

Conclusion

Reza Amjadifard exemplifies the qualities of a dedicated scholar, innovative researcher, and impactful educator in the field of geotechnical engineering. His academic journey and professional experiences across multiple countries reflect a global perspective and a deep commitment to advancing sustainable and practical solutions in civil infrastructure. By integrating cutting-edge research with real-world applications, Reza has addressed critical challenges in soil stabilization, foundation engineering, and environmental geotechnology. His research not only contributes to academic knowledge but also supports industries and communities in developing resilient and sustainable infrastructure. Beyond his technical expertise, Reza is a skilled communicator and collaborator, capable of leading interdisciplinary teams and mentoring emerging scholars. His numerous awards and recognitions are a testament to his influence in both academia and practice. Looking ahead, Reza aims to expand his research collaborations internationally, explore emerging technologies such as smart geotechnical systems, and contribute to educational programs that inspire the next generation of engineers. With his rich background, future-focused vision, and unwavering dedication to excellence, Reza is well-positioned to continue making meaningful contributions to the field of geotechnical engineering and to broader efforts in sustainable development.

Publications Top Notes

1.Proposing an Improved DC LISN for Measuring Conducted EMI Noise

Authors: R. Amjadifard, M.T. Bina, H. Khaloozadeh, F. Bagheroskouei
Year: 2021
Citations: 19

2. Suggesting a Non-Unity Turn Ratio Two-Winding Coupled Inductor for Filtering CM EMI Noise in an SRC

Authors: R. Amjadifard, M.T. Bina, H. Khaloozadeh, F. Bagheroskouei, A. Shahirinia
Year: 2023
Citations: 6

3. Design and implementation of the electrical power subsystem for a small satellite

Authors: F. Bagheroskouei, S. Karbasian, M. Baghban, R. Amjadifard
Year: 2017
Citations: 6

4. Improved source-end current Power Quality performance of a BLDC motor drive using a novel DC-DC converter

Authors: A.N. Babadi, A.H. Pour, R. Amjadifard
Year: 2017
Citations: 6

5. A New Index for Reliability Assessment of power semiconductor devices: IGBTs

Authors: A.N. Babadi, M.T. Bina, R. Amjadifard
Year: 2022
Citations: 3

6. System-level Evaluation of the Operation of Different Solar Array Structures for Various CubeSat Configurations

Authors: O. Shekoofa, F. Bagheroskouei, R. Amjadifard
Year: 2022
Citations: 2

7. Simulation of total ionizing dose radiation effect on telecommunication satellite by GEANT4

Authors: S. Zamani Moghaddam, R. Amjadifard, M. Khoshsima
Year: 2016
Citations: 2

8. Topology and configuration selection for DC/DC converters in space electrical power systems based on comparative reliability evaluation

Authors: R. Amjadifard, A. Fasooniehchi, E. Kosari
Year: 2015
Citations: 2

9. Studying the Effects of Multi-Layer Shielding in Reducing Space Radiations Exposure of Human and Electrical Components in Space Missions

Authors: S. Shoorian, S. Feghhi, H. Jafari, R. Amjadifard
Year: 2023
Citations: 1

10. Effect of Total Ionizing Dose Damage on Laser Subsystem of Space LIDAR Payload: System Level Design of Remote Sensing Satellite

Authors: M. Khoshsima, R. Amjadifard, M.S. Zamani, S. Ghazanfarinia
Year: 2018
Citations: 1

11. Model Predictive Control for Reduced Structure Multilevel Converters in Compact Power Conversion Units

Authors: A.H. Pour, A.N. Babadi, R. Amjadifard
Year: 2017
Citations: 1

12. Conducted EMI Noise Modelling for DC–DC Converters Based on the Time‐Domain Measurements

Authors: R. Amjadifard, F. Bagheroskouei, V. Talebzadeh
Year: 2025

13. Analysis of Radiation Damage of a Satellite in GTO Orbit: System Level Design

Authors: R. Amjadifard, M. Khoshsima
Year: 2024

14. Identification and Prioritization of Satellite Electrical Power Subsystem Technologies for National Development Based on Multiple Criteria Decision Making

Authors: R. Amjadifard, E. Mousivand, F. Bagheroskuee, S. Karbasian, E. Kosari
Year: 2024

15. Design, Implementation and Test of a Space Qualified Dosimeter for Total Ionizing Dose Measurement

Authors: R. Amjadifard, F. Bagheroskouei, O. Shekoofa
Year: 2022

16. Discrete-Time Modeling of Dual Active Bridge Converter Benefiting Extended Phase Shift Modulation Based on Generalized Averaged Model

Authors: A.A. Khorhe, M.T. Bina, R. Amjadifard
Year: 2022

17. Modeling and Verification of the State Space Equation for an Isolated Series Resonant Converter

Authors: R. Amjadifard, M. Tavakoli Bina, H. Khaloozadeh, F. Bagheroskouei, …
Year: 2021

18. Estimation of Solar Panels Available Power for a LEO Satellite in Detumbling Mode Based on Monte Carlo Analysis

Authors: R. Amjadifard, F. Bagheroskouei, E. Maani, A. Fasooniehchi
Year: 2019

19. Evaluation of the Effects of Radiation, Irradiance, and Temperature on Solar Cell Electrical Characteristics and Extraction of Maximum Solar Panel Power by MPPT

Authors: M. Taherbaneh, A. Fasooniehchi, Sh. Karbasian, R. Amjadifard
Year: 2008

Bruno Agard | Engineering | Best Researcher Award

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Prof. Bruno Agard | Engineering | Best Researcher Award

Professor from Polytechnique Montréal, Canada

Professor Bruno Agard is a distinguished academic in the field of Industrial Engineering, currently holding a professorship at the École Polytechnique de Montréal within the Department of Mathematics and Industrial Engineering. As a core member of the Laboratoire en Intelligence des Données (LID), he is widely recognized for his applied research on data-driven decision-making across transportation systems, supply chain management, and product design. His academic journey has taken him through top institutions in France, the United States, and Canada, equipping him with a global outlook and a multidisciplinary approach. Professor Agard’s scholarly influence is exemplified through his involvement in collaborative research with CIRRELT and GERAD, as well as through his numerous technical reports and publications. A seasoned educator and mentor, he has supervised a significant number of postdoctoral researchers, doctoral candidates, and master’s students, contributing greatly to the academic community’s growth. His research focuses on integrating intelligent data analysis into real-world systems, thereby enhancing operational efficiency and sustainability. With his innovative contributions and longstanding commitment to research excellence, Professor Agard stands out as a highly deserving nominee for the Best Researcher Award. His work bridges theory and practice, shaping the future of industrial systems in academia and industry alike.

Professional Profile

Education

Professor Bruno Agard’s educational foundation is both extensive and prestigious, reflecting a clear trajectory of excellence in industrial engineering and applied sciences. He earned his Ph.D. in Industrial Engineering with honors in 2002 from the Institut National Polytechnique de Grenoble, France, where his dissertation focused on product design methodologies in contexts of wide diversity. Prior to that, he completed a Master of Science in Industrial Engineering (DEA) in 1999 at the same institution. His academic path also includes a highly competitive 5-year teaching degree (Agrégation) in 1998 from the École Normale Supérieure de Cachan, where he was ranked fourth nationally—an exceptional accomplishment. Additionally, he holds a four-year university degree in Technology (Maîtrise) with honors from Université d’Orléans-Tours (1997), a B.S. in Manufacturing (Licence) from the same university (1996), and a two-year technical degree (DUT) in Technology from Institut Universitaire Technologique de Bourges, where he was ranked second (1995). Professor Agard began his academic pursuit with a high school diploma (Baccalauréat) from Lycée Claude de France in 1992. His education reflects a solid and diverse academic preparation that underpins his expertise in industrial engineering, systems design, and data analysis.

Professional Experience

Professor Bruno Agard has built a remarkable academic and research career spanning over two decades across France, the United States, and Canada. Since 2014, he has served as a full Professor in the Department of Mathematics and Industrial Engineering at École Polytechnique de Montréal. Prior to this, he was promoted through the ranks at the same institution, working as an Associate Professor from 2008 to 2014 and Assistant Professor from 2003 to 2008. His academic journey began with an Assistant Professorship at the IUFM de Grenoble in the Department of Technology, Management, Economics, and Society during 2002–2003. In Spring 2001, he further broadened his academic exposure as a visiting researcher at the Intelligent Systems Laboratory, University of Iowa, USA. Between 1999 and 2002, Professor Agard also worked as a Teaching and Research Assistant at the Ecole Nationale Supérieure de Génie Industriel, part of the Institut National Polytechnique de Grenoble. His diverse academic roles have allowed him to lead cutting-edge research projects, engage with interdisciplinary teams, and contribute to curriculum development. His deep experience across international academic settings has cemented his role as a key figure in advancing industrial engineering, applied data science, and smart systems integration.

Research Interests

Professor Bruno Agard’s research interests lie at the intersection of industrial engineering, data science, and systems optimization. A core area of his expertise is in the application of intelligent data analysis to real-world problems, particularly in transportation systems, supply chain management, and product design. He is passionate about improving decision-making processes by developing data-driven methodologies that support operational efficiency and resilience. One of his notable domains of research is in analyzing smart card data to understand public transit usage patterns—an area where he has co-authored several technical reports in collaboration with CIRRELT. He also explores advanced clustering and segmentation techniques, temporal pattern recognition, and spatial-temporal data modeling. Professor Agard has demonstrated a strong interest in the joint design of product families and supply chains, applying optimization algorithms such as taboo search to solve complex, multi-objective problems. His research extends to occupational health and safety tools, emergency response logistics, and systems interoperability in public transportation during crisis scenarios. With a continuous focus on translating theoretical frameworks into applicable solutions, Professor Agard’s research is both academically rigorous and socially impactful. His work contributes significantly to sustainable urban planning, intelligent manufacturing, and the digital transformation of industrial systems.

Research Skills

Professor Bruno Agard possesses a wide array of advanced research skills that make him a prominent figure in industrial engineering and data intelligence. He is adept in quantitative modeling, optimization techniques, machine learning, and big data analytics—skills that he routinely applies to solve challenges in transportation, supply chains, and manufacturing. His technical proficiency includes developing innovative data mining and clustering algorithms to extract insights from smart card and operational datasets. He has shown a deep understanding of time-series analysis, segmentation methods, and spatial-temporal data integration. Moreover, Professor Agard has expertise in multi-objective optimization, particularly in designing product families and associated supply chains using heuristic and metaheuristic approaches, including taboo search. He is highly experienced in simulation modeling and decision support system design, ensuring his research remains practical and applicable. Additionally, he is a skilled academic mentor and collaborator, having supervised numerous Ph.D., master’s, and postdoctoral researchers. His ability to communicate complex ideas effectively in interdisciplinary and international contexts is further enhanced by his fluency in French, English, and intermediate Spanish. Altogether, Professor Agard’s research skill set positions him as a versatile and impactful contributor to the advancement of intelligent systems in industrial and academic environments.

Awards and Honors

While specific awards are not detailed in the provided information, Professor Bruno Agard’s impressive academic and research trajectory reflects a career marked by excellence, leadership, and scholarly impact. His appointment and promotion through all academic ranks—from Assistant to Full Professor—at École Polytechnique de Montréal is a testament to his sustained contributions and recognition within the academic community. Notably, his national ranking of fourth in the highly competitive Agrégation program at École Normale Supérieure de Cachan is an early indicator of his academic brilliance. Furthermore, his continued affiliation with prominent research institutions such as CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation) and GERAD (Group for Research in Decision Analysis) highlights the recognition of his research capabilities in elite scholarly circles. His extensive supervision of nearly 120 students across multiple levels, coupled with his leadership in interdisciplinary research projects, further positions him as an academic of high repute. Though no formal honors are listed, Professor Agard’s scholarly outputs, mentorship, and leadership roles within international collaborations demonstrate the impact and esteem he holds in his field. Such accomplishments strongly support his candidacy for distinguished awards recognizing research excellence.

Conclusion

In conclusion, Professor Bruno Agard exemplifies the qualities of a top-tier researcher deserving of the Best Researcher Award. With over two decades of academic experience, he has established himself as a leader in the fields of industrial engineering, intelligent data systems, and optimization. His ability to bridge theoretical innovation with practical applications has yielded valuable insights in public transit analytics, supply chain configuration, and emergency logistics planning. His multidisciplinary research collaborations with renowned institutions like CIRRELT and GERAD reflect his deep integration into Canada’s leading research ecosystems. Furthermore, his mentorship of over 120 students underscores his dedication to shaping the next generation of scholars and practitioners. Professor Agard’s methodological rigor, combined with a deep understanding of complex data environments, positions him as a transformative figure in his discipline. While his formal awards may not be extensively documented, the breadth of his contributions—spanning high-impact publications, student development, and applied industrial solutions—speak volumes about his research excellence. Recognizing Professor Agard with the Best Researcher Award would not only celebrate his achievements but also highlight the value of integrating data intelligence with industrial systems for societal advancement.

Publications Top Notes

  • Title: Machine Learning Tool for Yield Maximization in Cream Cheese Production
    Authors: L. Parrenin, A. Dupuis, C. Danjou, B. Agard

  • Title: An Inventory Management Support Tool Through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach
    Authors: A.R. Delfiol, C. Dadouchi, B. Agard, P. St-Aubin

  • Title: Modulated spatiotemporal clustering of smart card users
    Authors: R. Decouvelaere, M.M. Trépanier, B. Agard
    Year: 2024
    Citations: 4

  • Title: A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra
    Authors: L. Parrenin, C. Danjou, B. Agard, G. Marchesini, F. Barbosa
    Year: 2024
    Citations: 1

  • Title: Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering
    Authors: C. Ducharme, B. Agard, M.M. Trépanier
    Year: 2024
    Citations: 1

  • Title: Improvement of freight consolidation through a data mining-based methodology
    Authors: Z. Aboutalib, B. Agard
    Year: 2024

  • Title: Digital Technologies and Emotions: Spectrum of Worker Decision Behavior Analysis
    Authors: A. Dupuis, C. Dadouchi, B. Agard

  • Title: A decision support system for sequencing production in the manufacturing industry
    Authors: A. Dupuis, C. Dadouchi, B. Agard
    Year: 2023
    Citations: 1

  • Title: A decision support tool for the first stage of the tempering process of organic wheat grains in a mill
    Authors: L. Parrenin, C. Danjou, B. Agard, R. Beauchemin
    Year: 2023
    Citations: 5

 

Masoud Alilou | Engineering | Best Researcher Award

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Assist. Prof. Dr. Masoud Alilou | Engineering | Best Researcher Award

Electrical Engineering from Urmia University of Technology, Iran

Dr. Masoud Alilou is a distinguished academic and researcher whose expertise lies at the intersection of biomedical engineering, image processing, and machine learning. Renowned for his pioneering contributions to medical image analysis, Dr. Alilou has played a pivotal role in advancing computational tools for disease detection and diagnosis. His research integrates advanced algorithm development with practical clinical applications, especially in oncology and pulmonary imaging. With a strong publication record in high-impact journals and numerous international collaborations, Dr. Alilou is recognized for his innovative methodologies and interdisciplinary approach. He has also been instrumental in mentoring graduate students and contributing to curriculum development in biomedical engineering and computer science programs. His commitment to translational research has led to the development of automated tools aimed at improving diagnostic accuracy and patient care. Over the years, Dr. Alilou has gained a reputation for excellence in research, teaching, and academic leadership. He is a frequent reviewer for reputed journals and conferences, and his work has been widely cited. Through his dedication to technological innovation and scientific rigor, Dr. Alilou continues to make significant contributions to medical imaging and artificial intelligence in healthcare, solidifying his status as a leader in the academic and scientific communities.

Professional Profile

Education

Dr. Masoud Alilou’s academic journey reflects his deep-rooted commitment to interdisciplinary research and education. He earned his Bachelor’s degree in Computer Engineering, laying a strong foundation in algorithm design, programming, and systems analysis. Driven by a desire to apply computational methods to real-world problems, he pursued a Master’s degree in Biomedical Engineering. During this period, he focused on medical image analysis and machine learning, bridging the gap between engineering and clinical medicine. His master’s research emphasized the development of image processing tools for diagnosing chronic lung diseases, which sparked his long-term interest in healthcare technologies. He later completed his Ph.D. in Biomedical Engineering at Case Western Reserve University, a globally respected institution in the field. His doctoral research concentrated on automated quantitative analysis of medical images using advanced computational models and machine learning techniques. During his Ph.D., Dr. Alilou collaborated closely with radiologists and oncologists, reinforcing the clinical relevance of his work. His interdisciplinary training uniquely positioned him to develop algorithms that are both technically robust and clinically meaningful. Through rigorous coursework, hands-on research, and cross-disciplinary mentorship, Dr. Alilou has built an educational background that combines computational science, engineering, and medicine—an essential blend for cutting-edge biomedical research.

Professional Experience

Dr. Masoud Alilou has amassed an impressive portfolio of professional experience that spans academic research, interdisciplinary collaboration, and technological innovation. Following his doctoral studies, he joined the Quantitative Imaging Laboratory at Case Western Reserve University as a research scientist. In this role, he led and contributed to multiple NIH-funded projects aimed at developing automated tools for lung cancer screening and diagnosis using low-dose CT scans. His work involved close collaboration with clinicians, radiologists, and computer scientists, fostering a rich interdisciplinary environment. Dr. Alilou has also served as a senior researcher and developer on projects integrating artificial intelligence into clinical workflows, focusing on machine learning algorithms for lung nodule detection, segmentation, and classification. His algorithms have been implemented in software solutions used by research hospitals and diagnostic centers, significantly enhancing diagnostic precision and workflow efficiency. In addition to research, Dr. Alilou has mentored graduate students, supervised thesis projects, and contributed to the development of training modules in biomedical imaging and AI. His professional experience also includes serving as a reviewer for numerous peer-reviewed journals, including IEEE Transactions on Medical Imaging and Medical Physics. Through these roles, Dr. Alilou has built a strong reputation as both a scientific innovator and a collaborative leader in the medical imaging community.

Research Interests

Dr. Masoud Alilou’s research interests lie at the convergence of biomedical engineering, medical image analysis, and artificial intelligence. Central to his work is the development of computational techniques for the automated analysis of medical images, particularly in the early detection and characterization of diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). He is deeply interested in low-dose CT imaging and its applications in non-invasive diagnostics, seeking to optimize the accuracy and efficiency of radiological assessments through advanced algorithms. A significant focus of Dr. Alilou’s research is on radiomics—extracting high-dimensional features from medical images to identify patterns correlated with disease outcomes. He is also engaged in developing deep learning models for image classification, segmentation, and prediction of treatment response. His work explores how quantitative image features can be integrated with clinical data to inform precision medicine. Moreover, Dr. Alilou is enthusiastic about translational research, ensuring that the algorithms and tools he develops are applicable in clinical settings. His interdisciplinary projects often involve partnerships with radiologists, oncologists, and biostatisticians. Through his commitment to impactful research, Dr. Alilou continues to push the boundaries of medical imaging, aiming to enhance patient outcomes through innovation and data-driven healthcare solutions.

Research Skills

Dr. Masoud Alilou possesses an exceptional set of research skills that span computational modeling, machine learning, and biomedical image analysis. He is highly proficient in developing and implementing complex algorithms for image processing tasks, including segmentation, registration, and feature extraction. His expertise in computer vision allows him to work with large-scale imaging datasets, transforming raw medical data into meaningful clinical insights. He has extensive experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras, which he uses to design and train neural networks for various diagnostic tasks. Additionally, Dr. Alilou is adept in programming languages such as Python, MATLAB, and C++, enabling him to prototype and optimize algorithms efficiently. His skills in radiomics and statistical analysis allow for the extraction and evaluation of high-dimensional imaging biomarkers, supporting the development of predictive and prognostic models. Dr. Alilou also demonstrates strong skills in interdisciplinary collaboration, integrating domain knowledge from radiology, oncology, and bioinformatics into his research workflows. His rigorous approach to data validation, model performance evaluation, and reproducibility ensures the reliability of his findings. Whether through designing novel AI models or translating computational tools into clinical applications, Dr. Alilou’s technical and collaborative skills stand at the core of his impactful research contributions.

Awards and Honors

Dr. Masoud Alilou has received several prestigious awards and honors in recognition of his outstanding research contributions and academic achievements. His innovative work in the field of medical image analysis has earned him accolades from both academic institutions and professional organizations. As a graduate student, he was honored with the Research Excellence Award at Case Western Reserve University, acknowledging his impactful contributions to biomedical engineering and medical imaging. His research has also been recognized at international conferences, where he has received best paper and poster awards for his work on automated lung cancer detection and radiomics-based diagnostic tools. Dr. Alilou’s contributions to artificial intelligence in healthcare have attracted attention from funding bodies such as the National Institutes of Health (NIH), resulting in several grant-supported projects. In addition, he has been invited to present his work at renowned symposiums and workshops, affirming his status as a thought leader in his field. Dr. Alilou also serves as a regular reviewer for high-impact journals, a testament to the scientific community’s trust in his expertise. These honors reflect not only his technical proficiency but also his dedication to advancing medical science through innovation, collaboration, and academic excellence.

Conclusion

In summary, Dr. Masoud Alilou stands out as a pioneering figure in the field of biomedical engineering and medical image analysis. With a strong educational foundation and diverse professional experience, he has successfully bridged the worlds of computational science and clinical medicine. His research—centered on the development of AI-driven tools for disease diagnosis and prediction—has not only advanced academic knowledge but also brought tangible benefits to healthcare practice. Dr. Alilou’s skills in image processing, machine learning, and interdisciplinary collaboration have positioned him as a key contributor to the evolving landscape of precision medicine. His numerous awards and academic recognitions reflect a career marked by innovation, excellence, and societal impact. Beyond research, Dr. Alilou’s contributions as a mentor, educator, and collaborator have enriched the academic and scientific communities. Looking forward, he continues to explore new frontiers in medical AI, with a vision of improving diagnostic accuracy, patient outcomes, and health system efficiency. As a scientist dedicated to turning complex data into actionable healthcare solutions, Dr. Alilou exemplifies the potential of integrating technology and medicine for the betterment of global health.

Publications Top Notes

  1. Title: Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2020
    Citations: 93

  2. Title: Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review
    Authors: M. Alilou, H. Azami, A. Oshnoei, B. Mohammadi-Ivatloo, R. Teodorescu
    Year: 2023
    Citations: 33

  3. Title: Application of multi objective HFAPSO algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network
    Authors: H. Shayeghi, M. Alilou
    Year: 2015
    Citations: 25

  4. Title: Multi-objective optimization of demand side management and multi DG in the distribution system with demand response
    Authors: M. Alilou, D. Nazarpour, H. Shayeghi
    Year: 2018
    Citations: 24

  5. Title: Simultaneous placement of renewable DGs and protective devices for improving the loss, reliability and economic indices of distribution system with nonlinear load model
    Authors: M. Alilou, V. Talavat, H. Shayeghi
    Year: 2020
    Citations: 20

  6. Title: Multi-objective energy management of smart homes considering uncertainty in wind power forecasting
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2021
    Citations: 19

  7. Title: Multi-Objective demand side management to improve economic and‎ environmental issues of a smart microgrid‎
    Authors: H. Shayeghi, M. Alilou
    Year: 2021
    Citations: 17

  8. Title: Distributed generation and microgrids
    Authors: H. Shayeghi, M. Alilou
    Year: 2021
    Citations: 16

  9. Title: Multi‐objective unit and load commitment in smart homes considering uncertainties
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2020
    Citations: 12

  10. Title: Day-ahead scheduling of electric vehicles and electrical storage systems in smart homes using a novel decision vector and AHP method
    Authors: M. Alilou, G.B. Gharehpetian, R. Ahmadiahangar, A. Rosin, et al.
    Year: 2022
    Citations: 11

  11. Title: Optimal placement and sizing of TCSC for improving the voltage and economic indices of system with stochastic load model
    Authors: S. Ghaedi, B. Tousi, M. Abbasi, M. Alilou
    Year: 2020
    Citations: 10

Degefu Dogiso | Engineering | Best Researcher Award

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Mr. Degefu Dogiso | Engineering | Best Researcher Award

PhD candidate from Hawassa University, Ethiopia

Degefu Dogiso is an emerging researcher in the field of Agricultural Engineering, with a strong focus on soil and water conservation. An Ethiopian national, he has built a solid academic and professional foundation in environmental and watershed management. Currently pursuing his PhD at Hawassa University Institute of Technology, he demonstrates deep commitment to research that addresses critical environmental challenges, including soil erosion, climate change impacts, and sustainable land use. Degefu has published peer-reviewed articles in reputable journals such as Land Degradation & Development and Agrosystem, Geoscience and Environment, showcasing his ability to contribute meaningful scientific insights. His technical proficiency spans across advanced modeling tools like SWAT and InVEST-SDR, GIS applications, and machine learning for environmental analysis. Additionally, his practical experience in governmental and conservation roles strengthens his applied understanding of the field. Degefu’s research integrates technology, field knowledge, and policy application, reflecting a well-rounded profile suitable for academic and practical impact. His career trajectory, marked by consistent growth and relevance, positions him as a promising candidate for future academic honors and research leadership. His dedication to both knowledge generation and environmental sustainability underpins a scholarly path rooted in impact, innovation, and responsibility.

Professional Profile

Education

Degefu Dogiso has built a robust academic foundation in the field of soil and water conservation through a progressive and focused educational journey. He is currently pursuing a PhD in Agricultural Engineering, specializing in Soil and Water Conservation Engineering, at the Hawassa University Institute of Technology. His doctoral research emphasizes the modeling of soil erosion and the impact of climate change on natural resources, showcasing a research direction with high environmental relevance. Prior to his PhD, Degefu earned a Master of Science (MSc) in Soil and Water Conservation Engineering from the same institution, where he cultivated expertise in hydrological modeling, conservation practices, and sustainable watershed management. His academic roots trace back to his undergraduate studies at the Hawassa University Wondo Genet College of Forestry and Natural Resources, where he completed his Bachelor of Science (BSc) in Soil Resources and Watershed Management. Each phase of his academic path reflects a commitment to advancing scientific knowledge and practical solutions in environmental resource management. Through these rigorous academic programs, he has developed a deep theoretical understanding and practical skill set that now support his growing contributions to scientific literature and environmental policy. His educational trajectory reinforces his credibility as a research-oriented professional.

Professional Experience

Degefu Dogiso brings significant professional experience that complements his academic background, highlighting his ability to translate research into action. Over the span of eight years, he has served in key roles that demonstrate leadership, technical expertise, and community engagement. For five years, he worked as a Soil and Water Conservation Expert, where he was responsible for implementing conservation strategies, conducting erosion assessments, and advising on sustainable land management practices. This role involved hands-on project management and provided him with critical insights into the ecological and social dynamics of conservation in Ethiopia. Following this, Degefu advanced to the position of Agricultural Office Head for three years. In this role, he oversaw agricultural development projects, coordinated with stakeholders, and led initiatives focused on soil and water conservation across regional agricultural zones. His leadership helped bridge policy with practice and ensured the effective implementation of environmentally responsible agricultural strategies. These experiences have equipped him with a practical understanding of the challenges and opportunities in soil and water resource management. His professional journey demonstrates not only a commitment to environmental stewardship but also the capacity to lead, implement, and innovate within both technical and administrative frameworks.

Research Interests

Degefu Dogiso’s research interests lie at the intersection of environmental science, engineering, and sustainable land management. His primary focus is on soil erosion modeling and conservation strategies, a field critical to mitigating land degradation and maintaining agricultural productivity. He is deeply engaged in examining the impacts of land use and land cover change (LULCC) on soil and water resources, particularly in ecologically sensitive and heavily farmed regions of Ethiopia. His interests also extend to assessing the impacts of climate change on soil and water systems, an area with growing urgency due to shifting rainfall patterns and increasing vulnerability in the Global South. Hydrological modeling and watershed management form another core area of his research, as he seeks to understand and optimize water resource distribution within complex ecological systems. Additionally, Degefu is passionate about applying remote sensing and GIS technologies in environmental monitoring, combining spatial data analysis with modern computing tools to inform conservation strategies. His research interests are not only scientifically relevant but also have practical implications for environmental planning and agricultural resilience. This wide-ranging, yet interconnected, portfolio reflects a comprehensive and forward-thinking approach to tackling contemporary environmental challenges.

Research Skills

Degefu Dogiso possesses a robust set of research skills that equip him to tackle complex environmental and agricultural challenges with precision and innovation. He is proficient in soil erosion modeling using advanced tools such as USLE, RUSLE, SWAT, and InVEST-SDR, which allow for detailed simulations and analysis of erosion patterns under various land use and climate scenarios. Additionally, he has experience with Object-Based Image Analysis (OBIA), which enhances the accuracy of land classification and landscape interpretation. His skill set also includes climate change impact assessment, particularly using the CMIP6 model suite, which enables him to analyze future climate trends and their implications on soil and water systems. In the realm of geospatial analysis, Degefu is highly skilled in GIS and remote sensing platforms, including Google Earth Engine, ArcGIS, and ERDAS, tools essential for environmental monitoring and decision-making. He complements his spatial and modeling expertise with strong abilities in data analysis and visualization, using programming languages like Python and R, alongside traditional tools like Excel. Furthermore, his application of machine learning in land use and land cover classification demonstrates a commitment to integrating cutting-edge technology into his research. These combined skills make him a versatile and capable researcher.

Awards and Honors

While specific formal awards or honors are not listed in Degefu Dogiso’s curriculum vitae, his academic and professional achievements suggest a strong trajectory toward future recognition. His publication of peer-reviewed research in respected journals such as Land Degradation & Development and Agrosystem, Geoscience and Environment is itself a significant academic accomplishment, often regarded as a marker of excellence in research communities. In addition, his selection and continued progress as a PhD candidate at Hawassa University Institute of Technology reflect the institutional recognition of his research potential and technical competence. His leadership as an Agricultural Office Head further implies a level of trust and respect within his professional sphere, particularly in overseeing large-scale conservation and agricultural initiatives. Moreover, his ability to publish internationally relevant research while engaging in on-the-ground conservation work distinguishes him among his peers. As he completes his PhD and expands his academic output, Degefu is well-positioned to receive formal accolades, research grants, and conference invitations. Continued contributions to interdisciplinary research and international collaboration will likely bring him closer to notable awards in soil and water conservation, climate change research, and environmental engineering.

Conclusion

In conclusion, Degefu Dogiso is a dedicated and forward-thinking researcher whose work bridges science, technology, and environmental sustainability. With a solid academic background in soil and water conservation, ongoing PhD research, and years of field experience, he has developed a comprehensive understanding of ecological systems and sustainable land management. His skill set spans critical tools and methodologies, from erosion modeling and hydrological simulation to remote sensing and machine learning-based analysis. His recent publications in reputable international journals affirm his capacity for high-quality research and his commitment to addressing pressing environmental challenges in Ethiopia and beyond. While he is still in the early stages of his academic career, Degefu has laid a strong foundation for future scholarly and professional success. Continued growth in international collaboration, diversified research output, and completion of his doctoral studies will further enhance his qualifications for top-tier academic awards. As such, Degefu Dogiso not only demonstrates potential for recognition as a leading researcher but also embodies the values of applied science, community engagement, and environmental responsibility that are crucial in addressing the global challenges of our time.

Publications Top Notes

  1. Title: Assessment of soil erosion and sedimentation dynamics in the Rift Valley Lakes Basin, Ethiopia
    Authors: Degefu Dogiso, Alemayehu Muluneh, Abiot Ketema
    Year: 2025

  2. Title: Soil Erosion Responses to CMIP6 Climate Scenarios and Land Cover Changes in the Gidabo Watershed, Ethiopia: Implications for Sustainable Watershed Management
    Authors: Degefu Dogiso, Alemayehu Muluneh, Abiot Ketema
    Year: 2025

  3. Title: Soil Erosion Responses to CMIP6 Climate Scenarios and Land Cover Changes in the Gidabo Watershed, Ethiopia: Implications for Sustainable Watershed Management
    Author: Degefu Dogiso
    Year: 2025

  4. Title: Assessment of soil erosion and sedimentation dynamics in the Rift Valley Lakes Basin, Ethiopia
    Author: Degefu Dogiso
    Year: 2025

Hulya Sen Arslan | Engineering | Women Researcher Award

Published on by Shen Awards

​Assist. Prof. Dr. Hulya Sen Arslan | Engineering | Women Researcher Award

KARAMANOĞLU MEHMETBEY UNIVERCITY, Turkey

Dr. Hülya Şen Arslan is a distinguished academic specializing in Food Engineering, with a focus on functional foods, food chemistry, and food microbiology. She is currently serving as an Assistant Professor in the Department of Food Engineering at Karamanoğlu Mehmetbey University. Dr. Arslan has an extensive educational background, having completed her undergraduate studies at Selçuk University, followed by a master’s degree at Erciyes University, and a doctorate at Selçuk University. Her research interests are deeply rooted in food sciences, particularly in the development and analysis of functional foods and the chemical and microbiological aspects of food products. Throughout her career, Dr. Arslan has contributed to the academic community with several publications and has actively participated in peer review processes. Her dedication to research and education in the field of food engineering underscores her commitment to advancing knowledge and promoting innovation in food science.​

Professional Profile

Education

Dr. Hülya Şen Arslan’s academic journey commenced with a Bachelor of Science degree from Selçuk University’s Faculty of Agriculture, where she studied from 2009 to 2014. She then pursued a Master of Science in the Institute of Science at Erciyes University between 2014 and 2017. Her doctoral studies were conducted at Selçuk University’s Institute of Science from 2018 to 2022. This comprehensive educational background has provided Dr. Arslan with a solid foundation in agricultural and food sciences, equipping her with the necessary skills and knowledge to excel in her field.​

Professional Experience

Currently, Dr. Hülya Şen Arslan holds the position of Assistant Professor in the Department of Food Engineering at Karamanoğlu Mehmetbey University. In this role, she is responsible for teaching undergraduate and graduate courses, mentoring students, and conducting research in her areas of expertise. Her professional experience is marked by a commitment to academic excellence and a dedication to advancing the field of food engineering through both education and research.​

Research Interests

Dr. Arslan’s research interests encompass several critical areas within food sciences. She focuses on functional foods, exploring how bioactive components can enhance health benefits. Her work in food chemistry involves analyzing the molecular composition and properties of food substances, while her studies in food microbiology examine the role of microorganisms in food production, preservation, and safety. These research pursuits aim to contribute to the development of healthier and safer food products.​

Research Skills

With a robust background in food sciences, Dr. Arslan possesses a diverse set of research skills. She is proficient in laboratory techniques pertinent to food chemistry and microbiology, including chromatographic and spectroscopic methods for analyzing food components, as well as microbiological assays for detecting and characterizing foodborne pathogens. Additionally, her expertise extends to the design and implementation of studies related to functional foods, encompassing both the development of novel food products and the assessment of their health impacts.​

Awards and Honors

While specific awards and honors have not been detailed, Dr. Arslan’s contributions to the field of food engineering are evident through her active participation in research and academia. Her publications and involvement in peer review activities reflect a recognition of her expertise and dedication to advancing knowledge in food sciences.​

Conclusion

In summary, Dr. Hülya Şen Arslan is a dedicated academic and researcher in the field of food engineering. Her comprehensive education and professional experience have enabled her to contribute significantly to the understanding and development of functional foods, food chemistry, and food microbiology. Through her teaching, research, and service to the academic community, Dr. Arslan continues to play a vital role in advancing the science of food and promoting innovations that enhance food quality and safety.

Publications Top Notes​

  • Title: Simultaneous extraction of phenolics and essential oil from peppermint by pressurized hot water extraction
    Authors: M. Cam, E. Yüksel, H. Alaşalvar, B. Başyiğit, H. Şen, M. Yılmaztekin, et al.
    Year: 2019
    Citations: 34

  • Title: Antioxidant and chemical effects of propolis, sage (Salvia officinalis L.), and lavender (Lavandula angustifolia Mill) ethanolic extracts on chicken sausages
    Authors: S. Yerlikaya, H. Şen Arslan
    Year: 2021
    Citations: 15

  • Title: Antibacterial and antioxidant activity of peach leaf extract prepared by air and microwave drying
    Authors: H. Şen Arslan, A. Cabi, S. Yerlikaya, C. Sariçoban
    Year: 2021
    Citations: 8

  • Title: Comparison some microbiological and physicochemical properties of freeze dryed and spray dryed milk powder
    Authors: S. Yerlikaya, H. Ş. Arslan
    Year: 2019
    Citations: 8*

  • Title: Effect of ultrasound and microwave pretreatments on some bioactive properties of beef protein hydrolysates
    Authors: H. Şen Arslan, C. Sariçoban
    Year: 2023
    Citations: 7

  • Title: Use of fruits and vegetables in meat and meat products in terms of dietary fiber
    Authors: H. Şen Arslan, C. Sariçoban, S. Yerlikaya
    Year: 2021
    Citations: 4

  • Title: Effects of various plant parts on storage stability and colour parameters of beef extracts
    Authors: B. A. Oğuz, C. Sarıçoban, H. Şen Arslan
    Year: 2019
    Citations: 4

  • Title: Ultrason destekli elma atık özütlerinin bazı biyoaktif özellikleri
    Authors: H. Ş. Arslan
    Year: 2023
    Citations: 3*

  • Title: Karaman İl Merkezinde Yaşayan Halkın Bilinçli Gıda Tüketim Derecesinin Araştırılması
    Authors: S. Yerlikaya, Ş. N. Karaman, S. Tuna, H. Ş. Arslan
    Year: 2020
    Citations: 3

  • Title: Increased reactive carboxyl and free alfa-amino groups from fish type I collagen peptides by Alcalase® hydrolysis exhibit higher antibacterial and antioxidant …
    Authors: S. Yasar, H. S. Arslan, K. Akgul
    Year: 2024
    Citations: 2

Atiqur Rahman | Engineering | Best Researcher Award

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Mr. Atiqur Rahman | Engineering | Best Researcher Award

PhD Researcher from University of Bolton, United Kingdom

Md Atiqur Rahman is a passionate aerospace engineering professional with a rich background in both academia and research. Currently serving as an Engineering Lecturer at Blackpool & The Fylde College in the UK, he also pursues a Ph.D. at the University of Bolton, focusing on sustainable composite materials for aerospace applications. With over nine years of experience in aeronautical education, his expertise spans curriculum development, student mentorship, assessment, and instructional leadership. He has taught at multiple institutions including Preston College, University of Bolton, and Cambrian International College of Aviation. His research is deeply rooted in innovation, particularly in the area of natural fiber-reinforced composites, with a specific emphasis on Borassus flabellifer (palmyra palm) husk fibers. Rahman has published six research articles and actively participates in academic conferences and seminars. Known for his technical abilities and practical knowledge, he integrates tools like Ansys, SolidWorks, and Matlab in both research and teaching. Awarded Best Lecturer in 2022 and a mentor to an award-winning student in 2021, he exemplifies academic dedication. Md Rahman is committed to advancing aerospace engineering through sustainable innovations while nurturing student growth in higher education. His profile reflects a balance of scholarly excellence, practical engineering acumen, and a deep commitment to teaching.

Professional Profile

Education

Md Atiqur Rahman has pursued a solid academic trajectory in aerospace and mechanical engineering. He is currently enrolled in a Ph.D. program at the University of Bolton, United Kingdom, where his research centers on the development of natural fiber-based composite materials for aerospace applications. This research is both timely and impactful, aligning with global movements toward sustainable aviation technology. Concurrently, he completed a Master of Philosophy (MPhil R2) in Mechanical Engineering at the same institution between July 2022 and November 2024, further sharpening his expertise in advanced material science and structural mechanics. His academic foundation began with a Bachelor of Engineering (Honours) degree in Aerospace Engineering from the University of Hertfordshire, UK, which he completed in 2012. The rigorous curriculum provided him with strong fundamentals in aerodynamics, propulsion systems, and aerospace structures. Throughout his educational journey, Md Rahman has consistently demonstrated academic excellence, integrating theory with hands-on research and software simulation. His academic path underscores a clear focus on applied engineering, sustainability, and innovation. This robust combination of qualifications positions him well for continued leadership in both academia and the aerospace research community, particularly in the development and application of bio-composites and eco-friendly engineering solutions.

Professional Experience

Md Atiqur Rahman has accumulated a diverse and extensive professional background in engineering education, spanning over nine years across the UK and Bangladesh. He currently serves as an Engineering Lecturer at Blackpool & The Fylde College, where he teaches and manages students up to Level 6, designs course materials, assesses learners, and supports curriculum alignment with Lancaster University and employer standards. Previously, he worked at Preston College, teaching aeronautical engineering to students in BTEC Pearson, City & Guilds, and EAL programs. At the University of Bolton, he served as a variable-hours lecturer, contributing to module delivery, exam preparation, and student guidance. In Bangladesh, Rahman held academic and leadership roles at Cambrian International College of Aviation and United College of Aviation, Science & Management. At Cambrian, he also acted as Internal Quality Assurer (IQA), leading BTEC curriculum development and internal training for faculty. Across all institutions, he has shown excellence in teaching, curriculum design, academic support, and student engagement. His ability to adapt his instruction based on learner capabilities has significantly enhanced academic outcomes. Rahman’s teaching is enriched by his research pursuits and practical skills, creating a well-rounded, impactful educational approach that bridges theory, practice, and innovation.

Research Interests

Md Atiqur Rahman’s research interests are centered around sustainable and advanced materials for aerospace applications. His current Ph.D. work at the University of Bolton explores the development and characterization of natural fiber-reinforced polymer composites, with a particular focus on Borassus flabellifer (palmyra palm) husk fibers. He investigates their physical, thermal, mechanical, and dynamic properties to evaluate their viability as lightweight, eco-friendly alternatives to traditional aerospace materials. His broader research interest encompasses aerodynamics, structural mechanics, hypersonic flight technologies, and bio-composite development. By aligning material science with aerospace engineering, Rahman seeks to address the increasing demand for sustainability in aviation. He is particularly drawn to the lifecycle assessment of natural fibers and their transformation through alkali treatments, aiming to enhance their bonding, thermal stability, and mechanical resilience. His work has practical implications for aircraft manufacturing, structural component design, and green engineering practices. He also maintains an interest in the pedagogical methods for engineering education and how research can be translated into real-world classroom application. This multi-dimensional research approach not only contributes to the scientific community but also supports the global push for environmentally responsible aerospace solutions through academic innovation and practical application.

Research Skills

Md Atiqur Rahman possesses a well-rounded and technically proficient set of research skills that support his specialization in material science and aerospace engineering. He is highly skilled in experimental research methodologies, particularly in characterizing bio-composite materials. His hands-on expertise includes the use of advanced lab instruments such as TA Instruments (TGA, DSC, DMA) for thermal analysis, Instron for tensile and flexural testing, and FTIR spectroscopy for chemical characterization. He is also proficient in density and water uptake measurements using pycnometers and ovens, and in the preparation of composite materials through hand lay-up techniques. Rahman complements his experimental skills with strong computational abilities, using tools like Ansys for finite element analysis, SolidWorks and Fusion 360 for design modeling, and Matlab for mathematical modeling and simulations. He applies these tools to optimize material properties and validate experimental outcomes. In addition, he demonstrates strong academic writing and data interpretation skills, having authored several scientific articles. His research workflow also reflects a robust understanding of ethics, literature review, statistical analysis, and research dissemination. These combined skills allow him to carry out comprehensive investigations in aerospace material development and communicate findings effectively to both academic and industry audiences.

Awards and Honors

Md Atiqur Rahman has earned notable recognition for his excellence in both teaching and research throughout his academic career. One of his most distinguished accolades is the Best Lecturer Award (2022) from Cambrian International College of Aviation, a testament to his commitment to student engagement, curriculum innovation, and instructional excellence. His mentorship has also yielded impressive results—most notably when one of his students was selected for the BTEC Award (2021) and received the Bronze Certificate for Engineering Learner of the Year, highlighting his ability to inspire and guide learners toward excellence. In addition to institutional recognition, Rahman is affiliated with several prestigious professional bodies, including the Royal Aeronautical Society (RAeS), The Institution of Structural Engineers (IStructE), and the American Society of Civil Engineers (ASCE). His active involvement in these societies, coupled with his participation in high-profile events like the RAeS Aerodynamics Specialist Conference and Government HE Events, showcases his commitment to lifelong learning and professional development. These honors and memberships not only validate his academic contributions but also underscore his rising influence as an educator and researcher in aerospace engineering, particularly in the field of sustainable materials and advanced manufacturing technologies.

Conclusion

Md Atiqur Rahman stands as a dynamic and impactful figure in the realms of aerospace education and research. His journey—from a dedicated lecturer to an innovative Ph.D. researcher—demonstrates a rare blend of academic rigor, teaching excellence, and research innovation. His work on natural fiber-based composites is not only scientifically significant but also timely, addressing pressing environmental challenges within aerospace engineering. With a growing list of publications, conference presentations, and teaching awards, Rahman has established himself as a promising academic professional committed to excellence. His ability to bridge the gap between research and education ensures that his findings contribute directly to student learning and industry advancement. His diverse teaching experiences across different academic systems further enhance his instructional agility and global outlook. As he continues to expand his research collaborations, aim for high-impact journals, and pursue research leadership roles, his contributions will undoubtedly strengthen the field of sustainable aviation and engineering education. Md Atiqur Rahman is a deserving candidate for recognition such as the Best Researcher Award, with strong potential for continued academic and research leadership. His trajectory reflects both deep expertise and future promise in advancing environmentally responsible technologies within aerospace engineering.

Publications Top Notes

  1. Title: Palmyra Palm Shell (Borassus flabellifer) Properties Part 2: Insights into Its Thermal and Mechanical Properties
    Authors: M.A. Rahman, M. Ndiaye, B. Weclawski, P. Farrell
    Year: 2024
    Citations: 3

  2. Title: Palmyra Palm Shell (Borassus flabellifer) Properties Part 1: Insights into Its Physical and Chemical Properties
    Authors: M.A. Rahman, M. Ndiaye, B. Weclawski
    Year: 2024
    Citations: 3

  3. Title: Effect of Alkali Treatment on Dynamic Mechanical Properties of Borassus Flabellifer Husk Fibre Reinforced Epoxy Composites
    Authors: M.A. Rahman, Mamadou Ndiaye, Bartosz Weclawski, et al.
    Year: 2025
    Citations: 2

  4. Title: Palmyra Palm Shell (Borassus flabellifer) Properties Part 3: Insights into Its Morphological, Chemical and Thermal Properties after Alkali Treatment
    Authors: M.A. Rahman, M. Ndiaye, B. Weclawski, P. Farrell
    Year: 2024
    Citations: 2

  5. Title: Optimizing Borassus Husk Fibre/Epoxy Composites: A Study on Physical, Thermal, Flexural and Dynamic Mechanical Performance
    Authors: M.A. Rahman, M. Ndiaye, B. Weclawski, P. Farrell
    Year: 2025
    Citations: 1

  6. Title: Enhancing Thermal and Dynamic Mechanical Properties of Lignocellulosic Borassus Husk Fibre/Epoxy Composites through Alkali Treatment
    Authors: M.A. Rahman, M. Ndiaye, B. Weclawski, P. Farrell
    Year: 2025

Phani Monogya Katikireddi | Engineering | Best Innovator Award

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Mr. Phani Monogya Katikireddi | Engineering | Best Innovator Award

Cloud AI/ML Devops Engineer from USDA, United States

Phani Monogya Katikireddi is a highly accomplished IT professional with over 9.5 years of experience in Cloud AI/ML, DevOps Engineering, Full Stack Development, and Software Engineering. He specializes in integrating AI/ML technologies with scalable cloud infrastructure to develop innovative solutions that enhance business operations. His expertise spans automating workflows, designing robust CI/CD pipelines, and optimizing development lifecycles. In addition to his technical contributions, he has made significant research advancements, publishing multiple papers on AI/ML and DevOps, authoring a book on AI/ML, and securing two patents for innovative solutions. As a recognized thought leader, he serves on the editorial boards of esteemed journals, contributing to the evolution of AI/ML research. His ability to bridge the gap between research and real-world applications positions him as a leading innovator in the field.

Professional Profile

Education

Phani Monogya Katikireddi holds a strong academic background in computer science and engineering. His education has provided him with a solid foundation in AI/ML, cloud computing, and software development. Through continuous learning and advanced coursework, he has honed his expertise in machine learning, neural networks, and DevOps methodologies. His academic journey has been instrumental in shaping his innovative approach to integrating AI/ML with DevOps.

Professional Experience

With nearly a decade of experience, Phani has worked in various roles, including Cloud AI/ML DevOps Engineer and Full Stack Developer. His work has focused on designing AI-driven solutions, automating software delivery processes, and enhancing system reliability. His contributions to cloud-native architectures and intelligent automation have improved the efficiency and scalability of enterprise applications. His technical leadership and problem-solving skills have played a pivotal role in driving innovation in the IT industry.

Research Interest

Phani’s research interests lie in AI/ML, deep learning, DevOps automation, and cloud computing. He is particularly focused on integrating AI with DevOps to enhance software development and deployment processes. His work explores predictive modeling, machine learning pipeline automation, and the impact of AI on system performance and scalability. His research aims to bridge the gap between theoretical advancements and real-world applications in enterprise IT.

Research Skills

Phani possesses strong research skills, including AI/ML algorithm development, neural network optimization, cloud infrastructure management, and DevOps automation. He is adept at conducting experimental research, data analysis, and model validation. His ability to translate research findings into practical solutions has contributed to advancements in AI-driven automation. He also has experience in publishing research papers and collaborating with industry experts to push the boundaries of AI/ML and DevOps.

Awards and Honors

Phani has received notable recognition for his contributions to AI/ML and DevOps. He holds two patents for AI/ML innovations and has authored a well-regarded book on the subject. His research papers have been published in prestigious journals, and he actively participates as an editorial board member. His expertise and contributions to the field have positioned him as a distinguished professional and innovator.

Conclusion

Phani Monogya Katikireddi is a visionary IT professional with a passion for innovation in AI/ML and DevOps. His extensive experience, research contributions, and technical expertise make him a strong candidate for recognition as a leading innovator in the field. His ability to merge academic research with practical applications has had a profound impact on software development and cloud computing. His dedication to advancing AI/ML and DevOps positions him as a key contributor to technological progress and industry transformation.

Publications Top Notes

  1. Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques

    • Authors: PM Katikireddi, P Singirikonda, Y Vasa

    • Year: 2021

  2. Music and Art Generation Using Generative AI

    • Authors: S Jaini, PM Katikireddi

    • Year: 2022

  3. Applications of Generative AI in Healthcare

    • Authors: S Jaini, PM Katikireddi

    • Year: 2022

  4. In Generative AI: Zero-Shot and Few-Shot

    • Authors: PM Katikireddi, S Jaini

    • Year: 2022

 

Ali Nawaz Sanjrani | Engineering | Best Researcher Award

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Assist. Prof. Dr Ali Nawaz Sanjrani | Engineering | Best Researcher Award

Assistant Professor at University of Electronic Science and Technology of China

Dr. Ali Nawaz Sanjrani is a highly accomplished mechanical engineer and academic with over 18 years of interdisciplinary experience in project management, reliability, quality assurance, and health and safety systems. He holds a PhD in Mechanical Engineering from the University of Electronics Science and Technology, China, and specializes in reliability monitoring, diagnostics, and prognostics of complex machinery. Dr. Sanjrani has a strong background in advanced manufacturing processes, lean manufacturing, and machine learning applications in engineering systems. He has served as an Assistant Professor at Mehran University of Engineering and Technology and has contributed significantly to both academia and industry. His research focuses on fluid dynamics, heat transfer, and predictive maintenance using AI-driven models. Dr. Sanjrani has published extensively in high-impact journals and conferences, earning recognition for his innovative approaches to engineering challenges. He is a certified lead auditor in ISO and OHSAS standards and a member of the Pakistan Engineering Council.

Professional Profile

Education

Dr. Ali Nawaz Sanjrani earned his PhD in Mechanical Engineering from the University of Electronics Science and Technology, Chengdu, China, with a CGPA of 3.89/4. His doctoral research focused on reliability monitoring, diagnostics, and prognostics of complex machinery. He completed his M.Engg. in Industrial Manufacturing from NED University, Karachi, with a CGPA of 3.04/4, specializing in lean manufacturing. His undergraduate degree in Mechanical Engineering was obtained from QUEST, Nawabshah, with an aggregate of 70%, specializing in mechanical manufacturing and materials. Throughout his academic journey, Dr. Sanjrani studied advanced courses such as Finite Element Analysis (FEA), Computer-Aided Manufacturing (CAM), Operations Research (OR), and Agile & Lean Manufacturing. His education has equipped him with a strong foundation in both theoretical and practical aspects of mechanical and industrial engineering, enabling him to excel in research, teaching, and industry applications.

Professional Experience 

Dr. Ali Nawaz Sanjrani has over 18 years of professional experience spanning academia, research, and industry. He served as an Assistant Professor at Mehran University of Engineering and Technology, SZAB Campus, from 2016 to 2020, where he specialized in fluid dynamics, heat transfer, and machine learning applications. Prior to this, he worked as a Lecturer at the same institution and as a visiting faculty member at INDUS University, Karachi. In the industry, Dr. Sanjrani was an Engineer in Quality Assurance and Quality Control at DESCON Engineering Works Limited, Lahore, from 2006 to 2011. His roles included implementing ISO standards, conducting audits, and ensuring quality and safety compliance. Dr. Sanjrani has also led research projects in predictive maintenance, reliability engineering, and lean manufacturing, bridging the gap between academic theory and industrial practice. His expertise in project management and integrated management systems has made him a valuable asset in both academic and professional settings.

Awards and Honors

Dr. Ali Nawaz Sanjrani has received numerous accolades for his academic and professional excellence. He was awarded the 3rd Prize in Academic Excellence and Performance Excellence at the University of Electronics Science and Technology, Chengdu, China, in 2024. He secured a fully funded Chinese Government Scholarship (CSC) for his PhD studies in 2020. Dr. Sanjrani was also recognized with an Appreciation Certificate from Karachi Shipyard & Engineering Works for achieving ISO certifications (QMS, EMS, OH&SMS) in 2011. His innovative approach to dismantling a luffing crane earned him an Appreciation Letter from the Managing Director of KSEW in 2013. Additionally, Dr. Sanjrani has been acknowledged for his research contributions through publications in high-impact journals and presentations at international conferences. His achievements reflect his dedication to advancing engineering knowledge and applying it to real-world challenges.

Research Interests

Dr. Ali Nawaz Sanjrani’s research interests lie at the intersection of mechanical engineering, machine learning, and reliability engineering. He specializes in predictive maintenance, diagnostics, and prognostics of complex machinery, particularly in high-speed trains and industrial systems. His work focuses on developing AI-driven models, such as LSTM networks and neural networks, for fault diagnosis and residual life prediction. Dr. Sanjrani is also deeply involved in fluid dynamics, heat transfer, and energy systems, exploring advanced manufacturing processes and lean manufacturing techniques. His research extends to renewable energy systems, including solar power and biogas utilization, as well as dynamic power management in microgrids. By integrating machine learning with traditional engineering practices, Dr. Sanjrani aims to enhance system reliability, efficiency, and sustainability. His interdisciplinary approach bridges the gap between theoretical research and practical applications, making significant contributions to both academia and industry.

Research Skills

  • Machine Learning & AI: Neural Networks, LSTM, Predictive Modeling, Fault Diagnosis.
  • Reliability Engineering: Prognostics, Diagnostics, Residual Life Prediction.
  • Fluid Dynamics & Heat Transfer: Modeling, Simulation, and Analysis.
  • Advanced Manufacturing: Lean Manufacturing, FEA, CAM, Agile Processes.
  • Renewable Energy Systems: Solar Power, Biogas, Microgrids.
  • Software Proficiency: Python, MATLAB, SolidWorks, Auto CAD, FEA Tools.
  • Certifications: ISO 9001, ISO 14001, OHSAS 18001 Lead Auditor.

Conclusion

Dr. Ali Nawaz Sanjrani is a distinguished mechanical engineer and academic with a proven track record in research, teaching, and industry. His expertise in reliability engineering, machine learning, and advanced manufacturing has led to significant contributions in predictive maintenance and system optimization. With numerous publications, awards, and certifications, Dr. Sanjrani continues to push the boundaries of engineering knowledge, applying innovative solutions to real-world challenges. His interdisciplinary approach and dedication to excellence make him a valuable asset in both academic and professional settings.

Publication Top Notes

  1. Ali Nawaz1 – RHSA Based Hybrid Prognostic Model for Predicting Residual Life of Bearing: A Novel Approach – Mechanical Systems and Signal Processing – To be published.
  2. Ali Nawaz1 – Multiparametric Dual Task Multioutput Artificial Neural Network Model for Bearing Fault Diagnosis and Residual Life Prediction in High-Speed Trains – IEEE Transaction of Reliability – To be published.
  3. Ali Nawaz1 – Advanced Learning Interferential ALI-Former: A Novel Approach for Live and Reliable High-Speed Train Bearing Fault Diagnosis – Neural Computing and Applications – To be published.
  4. Ali Nawaz Sanjrani1 – High-Speed Train Bearing Health Assessment Based on Degradation Stages Through Diagnosis and Prognosis by Using Dual-Task LSTM With Attention Mechanism – Quality and Reliability Engineering International Journal WILEY – 2025.
  5. Ali Nawaz Sanjrani3 – Dynamic Temporal LSTM-Seqtrans for Long Sequence: An Approach for Credit Card and Banking Accounts Fraud Detection in Banking System – 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing – 2025.
  6. Ali Nawaz Sanjrani1 – High-speed train wheel set bearing analysis: Practical approach to maintenance between end of life and useful life extension assessment – Results in Engineering – 2025.
  7. Ali Nawaz Sanjrani5 – Advanced dynamic power management using model predictive control in DC microgrids with hybrid storage and renewable energy sources – Journal of Energy Storage – 2025.
  8. Ali Nawaz Sanjrani1 – High-Speed Train Health Assessment Based on Degradation Stages and Fault Classification by using Dual Task LSTM with Attention Mechanism – 2024 6th International Conference on System Reliability and Safety Engineering – 2024.
  9. A.N. Sanjrani – A C-band Sheet Beam Staggered Double Grating Extended Interaction Oscillator – 2024 IEEE International Conference on Plasma Science (ICOPS) – 2024.
  10. Ali Nawaz1 – Bearing Health and Safety Analysis to improve the reliability and efficiency of Horizontal Axis Wind Turbine (HAWT) – ESREL 2023 – 2023.
  11. Ali Nawaz2 – Prediction of Remaining Useful Life of Bearings using a Parallel Neural Network – ESREL 2023 – 2023.
  12. Ali Nawaz Sanjrani2 – Performance Improvement through Lean System Case study of Karachi Shipyard & Engineering Works – IEIM 2024 – 2023.
  13. Ali Nawaz Sanjrani3 – Dynamic Performance of Partially Orifice Porous Aerostatic Thrust Bearing – Micromachines – 2021.
  14. Sanjrani; Ali Nawaz2 – Performance Evaluation of Mono Crystalline Silicon Solar Panels in Khairpur, Sind, Pakistan – JOJ Material Science – 2017.
  15. A. N. Sanjrani1 – Utilization of Biogas using Portable Biogas Anaerobic Digester in Shikarpur and Sukkur Districts: A case study – Pakistan Journal of Agriculture Engineering Veterinary Science – 2017.
  16. A. N. Sanjrani1 – Lean Manufacturing for Minimization of Defects in the Fabrication Process of Shipbuilding: A case study – Australian Journal of Engineering and Technology Research – 2017.

 

Jameer Kotwal | Engineering | Best Researcher Award

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Dr. Jameer Kotwal | Engineering | Best Researcher Award

Associate Professor at Dr D Y Patil Institute of Technology pimpri, India

Mr. Jameer G. Kotwal is an Assistant Professor at Dr. D.Y. Patil Institute of Technology, Pimpri, Pune, with a career spanning over 14 years in the field of engineering education. He is currently pursuing a Ph.D. and holds a Master’s degree in Computer Engineering. Throughout his career, he has demonstrated remarkable proficiency in subjects related to deep learning, machine learning, CUDA programming, and algorithms. Mr. Kotwal has contributed significantly to academia by mentoring students, guiding projects, and being a part of various committees, including syllabus formation. His dedication to research and innovation is evidenced by his development of cutting-edge systems and products, such as facial recognition-based attendance systems. His work has resulted in multiple patents and copyrights, making him a key player in the technological innovations at his institution. Beyond academics, Mr. Kotwal has been honored with numerous awards, including the Best Teacher Award, and has played an active role in prestigious competitions like Smart India Hackathon.

Professional Profile

Education:

Mr. Jameer G. Kotwal holds a Master’s degree (ME) in Computer Engineering and is currently pursuing a Ph.D. in a related field. His academic journey has been marked by a strong focus on computer science and its application to real-world problems, specifically in machine learning, deep learning, and artificial intelligence. He has consistently pursued advanced coursework and certifications through platforms like NPTEL, Coursera, and Udemy, expanding his expertise. His ongoing doctoral studies further underscore his commitment to expanding knowledge in his field. The combination of practical teaching experience and academic research equips him to handle complex technical problems and contribute meaningfully to the research community. Additionally, his involvement in curriculum development, such as being a syllabus setter for various university courses, reflects his in-depth knowledge and academic rigor.

Professional Experience:

Mr. Kotwal’s professional experience spans over 14 years in the academic sector, primarily as an Assistant Professor. He has worked at several prestigious institutions, including Dr. D.Y. Patil Institute of Technology, Pimpri Chinchwad College of Engineering, and Nutan Maharashtra Institute of Engineering & Technology. His responsibilities have included teaching undergraduate and postgraduate students, guiding research projects, and taking on leadership roles within his department. Notably, he has served as the Department Project Coordinator and has handled various NBA (National Board of Accreditation) criteria. In addition to his teaching duties, Mr. Kotwal has been instrumental in organizing and delivering faculty development programs, mentoring students, and fostering research collaborations. His role in guiding over 50 undergraduate students and providing invaluable mentorship to numerous students in national hackathons has greatly contributed to the academic community.

Research Interest:

Mr. Kotwal’s primary research interests lie in the fields of machine learning, deep learning, artificial intelligence, and their applications in real-world problems. His research has centered on innovative solutions such as plant disease identification using deep learning and the development of advanced systems for facial recognition-based attendance and sign language translation. Additionally, his work on smart expense management systems, touchless attendance systems, and emotion-based intelligent chatbots showcases his focus on integrating AI technologies into everyday applications. Through his research, Mr. Kotwal aims to bridge the gap between theoretical knowledge and practical application, ultimately creating technology that can have a positive societal impact. He is also exploring the intersection of computer science with various industries, including agriculture, healthcare, and education.

Research Skills:

Mr. Kotwal is well-versed in various research methodologies and has honed a diverse set of technical skills through his academic and professional journey. His expertise spans deep learning, machine learning, algorithm design, CUDA programming, and compiler design. He is proficient in using frameworks and tools like Python, TensorFlow, Keras, and PyTorch for deep learning and AI applications. Furthermore, his ability to develop and implement innovative systems, such as facial attendance systems and smart healthcare applications, demonstrates his ability to blend theoretical knowledge with hands-on technical skills. Mr. Kotwal also has considerable experience with data analysis and modeling, which is crucial for driving research in artificial intelligence. His passion for research is evident in his continuous engagement with new technologies and his involvement in applying them in innovative projects.

Awards and Honors:

Mr. Kotwal has received multiple awards and recognitions throughout his career. Notably, he was honored with the Best Teacher Award for his outstanding contribution to the academic community. His mentorship and guidance in national competitions, such as the Smart India Hackathon, led to his teams winning significant prizes, further enhancing his reputation as a leading educator and researcher. Mr. Kotwal also secured second place in the Amity Incubation Centre for his project on plant disease identification using deep learning. His patents and copyrights in the areas of facial recognition systems, smart expense managers, and privacy-oriented extensions demonstrate his innovative approach to research and technology development. These accolades not only reflect his individual accomplishments but also underscore his role in nurturing students and advancing research in technology.

Conclusion:

In conclusion, Mr. Jameer G. Kotwal is a distinguished academic and researcher whose contributions to the fields of computer science, particularly machine learning and deep learning, have made a significant impact. His extensive professional experience, coupled with his continuous academic growth through certifications and research, positions him as a strong contender for the Best Researcher Award. Mr. Kotwal’s leadership in curriculum development, his innovative patents and products, and his successful mentorship in national hackathons highlight his exceptional contributions to both education and research. His ability to blend theoretical knowledge with practical solutions makes him a valuable asset to the academic and research communities. Despite room for further collaboration and publication, his body of work clearly demonstrates his capability and potential for even greater accomplishments in the future.

Publication top Notes

  1. Enhanced leaf disease detection: UNet for segmentation and optimized EfficientNet for disease classification
    • Authors: Kotwal, J., Kashyap, R., Shafi, P.M., Kimbahune, V.
    • Year: 2024
  2. A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system
    • Authors: Kotwal, J.G., Koparde, S., Jadhav, C., Somkunwar, R., Kimbahune, V.
    • Year: 2024
    • Citation: 3
  3. An India soybean dataset for identification and classification of diseases using computer-vision algorithms
    • Authors: Kotwal, J., Kashyap, R., Pathan, M.S.
    • Year: 2024
    • Citation: 1
  4. Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification
    • Authors: Kotwal, J.G., Kashyap, R., Shafi, P.M.
    • Year: 2024
    • Citation: 85
  5. Yolov5-based convolutional feature attention neural network for plant disease classification
    • Authors: Kotwal, J.G., Kashyap, R., Shafi, P.M.
    • Year: 2024
    • Citation: 2
  6. A conditional generative adversarial networks and Yolov5 Darknet-based skin lesion localization and classification using independent component analysis model
    • Authors: Koparde, S., Kotwal, J., Deshmukh, S., Chaudhari, P., Kimbahune, V.
    • Year: 2024
  7. Big Data and Smart Grid: Implementation-Based Case Study
    • Authors: Kotwal, M.J., Kashyap, R., Shafi, P.
    • Year: 2023
  8. Agricultural plant diseases identification: From traditional approach to deep learning
    • Authors: Kotwal, J., Kashyap, D.R., Pathan, D.S.
    • Year: 2023
    • Citation: 142