Majid Dashti Barmaki | Engineering | Best Researcher Award

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Mr. Majid Dashti Barmaki | Engineering | Best Researcher Award

Water Resource Expert from Kharazmi University of Tehran, Iran

Majid Dashti Barmaki is a distinguished Iranian hydrogeologist with over 15 years of combined academic, industrial, and research experience. he has consistently contributed to the understanding and development of groundwater systems, karst hydrogeology, water quality assessment, and geospatial modeling. With advanced degrees in geology and hydrogeology from prestigious Iranian institutions such as Shiraz University and Kharazmi University, Dr. Barmaki has developed a rich body of work encompassing both theoretical insight and practical application. His scholarly output includes over 20 peer-reviewed international and national articles in journals from Springer, Taylor & Francis, Elsevier, Wiley, and the Geological Society of London. His work bridges geology, environmental science, and engineering, highlighting an interdisciplinary approach. He has also provided expert consultancy in water resources projects such as tunnel hydrogeology, aquifer balance, and land subsidence analysis. Through workshops, university lectures, and research mentoring, he has shaped many young professionals in Earth sciences. Dr. Barmaki’s continuous efforts to improve groundwater sustainability in arid regions, coupled with his active role in environmental planning, position him as a significant contributor to the hydrogeological field. His expertise is currently sought globally as he seeks opportunities to further his impact in academic and industrial research worldwide.

Professional Profile

Education

Majid Dashti Barmaki has an extensive academic background in Earth Sciences, with a focused specialization in hydrogeology. He earned his Bachelor’s degree in Geology from Shiraz University in 2006, where he laid the foundational knowledge of Earth systems. He pursued his Master of Science degree at Kharazmi University (formerly Tarbiat Moallem University) in Tehran from 2007 to 2010, focusing on Hydrogeology. His MSc thesis applied GIS and remote sensing techniques to assess karst groundwater potential in anticline structures, reflecting his early interest in integrating geospatial tools with hydrogeological modeling. Continuing at Kharazmi University, he obtained his Ph.D. in Karst Hydrogeology from 2011 to 2019. His doctoral research examined karst development factors affecting the Asmari Formation in the Zagros Mountains, one of Iran’s critical aquifer systems. His education is not only academically sound but also application-oriented, as demonstrated by his emphasis on GIS, statistical analysis, and environmental assessment in groundwater sciences. This strong academic trajectory, enriched with practical thesis topics, has prepared him for high-level consultancy, teaching, and research roles in both national and international hydrogeology-focused projects.

Professional Experience

Dr. Majid Dashti Barmaki brings over 15 years of diverse professional experience as a hydrogeologist, academic, and consultant. His career spans collaborations with leading Iranian consulting engineering companies such as Fannavaran Tarh-e-Jamea, Sahel Consulting Engineers, Dezab, Rayab, and others, where he specialized in water resource assessments for tunnel projects, land subsidence studies, and environmental monitoring. Notably, he served as a key hydrogeology expert for major tunnel excavation projects, evaluating groundwater inflow, hydrological impacts, and tunnel pressure loads. From 2012 to 2025, he also worked in national-level research and implementation projects related to groundwater sustainability, illegal well monitoring, and surface water drainage systems. His academic involvement includes teaching several university courses such as surveying, statistics for geology, map reading, and soil geography. He has also conducted workshops on GIS applications at Kharazmi University. His long-standing fieldwork expertise is evident in the detailed hydrogeological profiles he has generated and the policy-relevant environmental assessments he has contributed to. His leadership in multi-year projects showcases his capability to manage complex hydrological systems and deliver data-driven solutions. Through his multifaceted experience, Dr. Barmaki has effectively bridged academic theory with engineering practice in Iran’s most critical water management initiatives.

Research Interest

Dr. Majid Dashti Barmaki’s research interests lie at the intersection of groundwater hydrogeology, karst systems, environmental geochemistry, and spatial modeling. A central theme of his work is the assessment and management of karst aquifers, particularly within the Zagros region, one of Iran’s most hydrogeologically significant landscapes. He is deeply invested in understanding how geological structures influence groundwater flow and the vulnerability of aquifers to pollution and overexploitation. His research extends to groundwater quality evaluation using health risk assessments, pollution indices, and fuzzy logic systems. He has also explored fractal geometry and geostatistical kriging methods to identify groundwater potential zones. Another significant domain of his research includes the integration of remote sensing and GIS techniques with hydrogeological mapping to model water resources and assess land subsidence risks. His academic output reflects a multidisciplinary approach, combining environmental science, engineering, and computer-based modeling. His more recent studies have addressed environmental impacts from mining and tunneling operations, with a focus on sustainable water resource planning. Overall, Dr. Barmaki’s research aims to create actionable insights that support water security, climate resilience, and ecological sustainability in arid and semi-arid regions.

Research Skills

Dr. Majid Dashti Barmaki possesses a comprehensive set of research skills that span hydrogeology, environmental assessment, GIS-based analysis, and statistical modeling. His expertise includes groundwater flow modeling, aquifer characterization, and water quality analysis using advanced techniques such as fuzzy inference systems, fractal analysis, and risk indexing. He is proficient in using hydrogeological software like PhreeQC, Aq-Qa, Aquachem, RockWare, Aquifer Test, and GMS, which are essential for conducting water chemistry evaluations and aquifer simulations. His geospatial analysis skills are advanced, with capabilities in ArcGIS, ENVI, ILWIS, Global Mapper, and SASPlanet. He has employed multi-criteria decision-making models such as AHP and fuzzy logic to evaluate groundwater vulnerability and land subsidence. He is also experienced in laboratory hydrogeochemistry, data visualization, and the development of hydrogeological cross-sections. Beyond technical tools, he is adept at conducting fieldwork, designing surveys, managing databases, and preparing high-quality research reports and environmental assessments. His ability to combine statistical tools like SPSS with field-based insights allows for holistic environmental and geological interpretations. These diverse research competencies have enabled him to contribute to multi-institutional projects and publish in leading international journals in the geosciences and environmental engineering fields.

Awards and Honors

While Dr. Majid Dashti Barmaki’s professional profile does not list formal awards, his distinguished achievements and contributions reflect recognition through impactful roles and scholarly publications. His articles have appeared in prestigious journals such as Arabian Journal of Geosciences, Geocarto International, Water and Environment Journal, and Quarterly Journal of Engineering Geology and Hydrogeology, showcasing international peer-reviewed validation of his research. His invited participation in multiple national geological conferences in Iran—such as the Geological Society of Iran’s annual meetings—demonstrates academic endorsement and thought leadership. He has also led and contributed to high-stakes national and industrial projects under the Iranian Ministry of Energy and regional water authorities. These responsibilities entrusted to him reflect implicit recognition of his expertise in hydrogeology and environmental planning. Additionally, his teaching roles, workshops, and project supervision illustrate institutional trust in his academic capabilities. Although he may not have been conferred with traditional awards or honors, his consistent involvement in nationally significant water resource studies and publication in high-impact journals affirms his status as a respected researcher and professional in the field of hydrogeology.

Conclusion

Dr. Majid Dashti Barmaki stands out as a highly competent and impactful researcher in the field of hydrogeology. His academic journey—from undergraduate geology to a Ph.D. in karst hydrogeology—has been marked by a strong commitment to applied science, innovation, and sustainable groundwater management. With over a decade of professional engagement in both academic and industry environments, he has consistently addressed key water resource challenges in Iran. His research, which integrates field studies, GIS tools, statistical models, and advanced hydrogeological software, is practical, data-driven, and environmentally conscious. His publication record in leading journals and active participation in major national water and environmental projects reflect a deep expertise and a collaborative spirit. He has also contributed to knowledge transfer through teaching, workshops, and mentoring, proving himself as an educator and thought leader. As global water security becomes an increasing concern, professionals like Dr. Barmaki—who combine technical depth with societal relevance—are essential. His readiness to engage in international collaborations and continue his impactful work on a global scale makes him a deserving candidate for any recognition or opportunity aimed at honoring excellence in hydrogeological research and sustainable development.

Publications Top Notes

  1. Hydrogeochemical characteristics of cold and warm (hot) springs in the Mahallat geothermal region, Iran
    Authors: Majid Dashti Barmaki, Davar Ebrahimi, Zahra Yazdani Noori
    Journal: Quarterly Journal of Engineering Geology and Hydrogeology
    Year: 2025

  2. Land subsidence susceptibility mapping using Analytical Hierarchy Process (AHP) and Certain Factor (CF) models at Neyshabur plain, Iran
    Authors: Rezaei M., Yazdani Noori Z., Dashti Barmaki M.
    Journal: Geocarto International
    Year: 2020

  3. Use of fractal dimensions analysis in geographic information system and remote-sensing techniques to identify groundwater prospective zones in the Anar-Dashtegol anticline, Iran
    Authors: M. Dashti Barmaki, M. Rezaei, S. Madadi
    Journal: Carbonates and Evaporites
    Year: 2020

  4. Water chemistry and water quality pollution indices of heavy metals: a case study of Chahnimeh Water Reservoirs, Southeast of Iran
    Authors not fully listed
    Journal: International Journal of Energy and Water Resources
    Year: 2020

  5. Comparison of surface and interior karst development in Zagros Karst Aquifers, Southwest Iran
    Authors: Majid Dashti Barmaki
    Journal: Journal of Cave and Karst Studies
    Year: 2019

  6. Qanat, a technique appropriate for extracting water from hard rock terrains: the case study of Bilvar district, Kurdistan, Iran
    Author: Majid Dashti Barmaki
    Journal: International Journal of Hydrology Science and Technology
    Year: 2017

  7. Extracting of prospective groundwater potential zones using remote sensing data, GIS, and a probabilistic approach in Bojnourd basin, NE of Iran
    Authors: Majid Dashti Barmaki
    Journal: Arabian Journal of Geosciences
    Year: 2017

  8. Recognition of karst hydrology and water resources interaction in Kazerun Karstic Zones, South of Iran
    Authors: Majid Dashti Barmaki
    Journal: Arabian Journal of Geosciences
    Year: 2016

  9. Groundwater contamination analysis using Fuzzy Water Quality index (FWQI): Yazd province, Iran
    Authors: Majid Dashti Barmaki
    Journal: Geopersia
    Year: 2013

  10. Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd province, Iran
    Authors: Majid Dashti Barmaki
    Journal: International Journal of Computer Applications
    Year: 2012

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

 

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.