Nadeem Khanday | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Nadeem Khanday | Computer Science | Best Researcher Award

Assistant Professor from National Institute of Technology Srinagar, India

Dr. Nadeem Yousuf Khanday is an accomplished academic and researcher in Computer Science & Engineering, currently serving as an Assistant Professor at the School of Computer Science, UPES, Dehradun, India. With a strong academic foundation and a passion for advanced computing technologies, he has contributed extensively to the fields of artificial intelligence, machine learning, and deep visual learning. His research outputs include high-impact journal publications, international conference presentations, patents, and book chapters with globally recognized publishers. Dr. Khanday is deeply involved in exploring innovative AI techniques that address real-world challenges, including healthcare diagnostics, crop disease detection, cloud computing, and smart environments. He is also a certified GATE, UGC-NET, and JK-SET qualifier, emphasizing his academic excellence. Throughout his career, he has taught a variety of technical subjects and mentored students in core areas of computer science. He brings a balanced combination of research, teaching, and applied innovation to the academic domain. With a growing body of interdisciplinary work, Dr. Khanday continues to build his reputation as a future-oriented researcher contributing to both academia and industry. His deep commitment to scholarly excellence and emerging technologies positions him as a deserving candidate for recognition in prestigious research awards.

Professional Profile

Education

Dr. Nadeem Yousuf Khanday has pursued a rigorous academic trajectory in Computer Science & Engineering. He earned his Doctor of Philosophy (Ph.D.) from the prestigious National Institute of Technology (NIT), Srinagar, focusing on advanced computing technologies and artificial intelligence. Prior to his doctorate, he completed his Master of Technology (M.Tech) from Vivekananda Global University, Jaipur, where he achieved an outstanding CGPA of 9.69 in Computer Science & Engineering, demonstrating his academic strength and subject mastery. His undergraduate studies were conducted at Visvesvaraya Technological University (VTU), Belgaum, where he obtained a Bachelor of Engineering (B.E.) degree in Computer Science & Engineering with a commendable academic record. Dr. Khanday has also qualified national-level competitive exams including the Graduate Aptitude Test in Engineering (GATE) and University Grants Commission National Eligibility Test (UGC-NET), as well as JK-SET, qualifying him for Assistant Professorship roles in Indian universities. These qualifications reflect his high-level proficiency in the domain and commitment to continued academic growth. His academic background provides a strong foundation for his research endeavors, enabling him to tackle complex computing problems and advance the frontier of knowledge in artificial intelligence, machine learning, and computer vision.

Professional Experience

Dr. Nadeem Yousuf Khanday possesses diverse and dynamic professional experience across some of India’s reputed institutions. He is currently employed as a Regular Assistant Professor at the School of Computer Science (SoCS), UPES Dehradun since June 2023. Before this, he served as a Lecturer at the University of Kashmir, J&K, where he taught undergraduate and postgraduate computer science courses from March to June 2023. His earlier appointments include his tenure as an Assistant Professor (Contract) at NIT Srinagar from April 2017 to July 2018, and later as a Teaching Assistant (Research Scholar) from July 2018 to February 2023 at the same institute. These roles have helped him accumulate extensive experience in teaching core computer science courses such as Artificial Intelligence, Operating Systems, Data Structures, and Computer Architecture. Throughout his career, Dr. Khanday has skillfully blended teaching with hands-on research, working on projects related to visual learning, deep learning, and intelligent systems. His progressive journey from contract roles to full-time professorship demonstrates his steady academic development and increasing responsibilities. With significant academic leadership and research roles, he is well-positioned to lead innovative educational and research initiatives in AI and computing.

Research Interests

Dr. Nadeem Yousuf Khanday’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Computer Vision, with a particular focus on deep visual learning and few-shot learning models. He explores innovative solutions to computational challenges involving limited data samples, aiming to improve learning accuracy and cross-domain generalization. His research extends into practical domains such as healthcare diagnostics, agricultural disease prevention, cloud computing optimization, and smart IoT-based systems. Dr. Khanday has investigated topics including convolutional neural networks for COVID-19 prognosis, metric learning models for classification, and AI-driven smart farming using 5G networks. His recent work has integrated Large Language Models (LLMs) and Generative AI to enhance decision-making systems in medical and industrial contexts. His interdisciplinary approach combines theoretical models with real-world applications, contributing to sustainable development through intelligent computing. Dr. Khanday’s research aims not only to push academic boundaries but also to provide practical, scalable solutions for modern societal challenges. His continuous engagement with cutting-edge technologies and publication in top-tier journals solidify his status as a thought leader in visual intelligence and machine learning systems.

Research Skills

Dr. Nadeem Yousuf Khanday possesses a strong portfolio of research skills that span multiple domains in computing. He is proficient in developing machine learning algorithms, deep learning architectures, and advanced image processing models for varied applications. His expertise includes designing few-shot learning frameworks, enhancing cross-domain classification performance, and deploying convolutional neural networks for medical image analysis and smart diagnostics. He has hands-on experience with AI-based anomaly detection, visual segmentation systems, and cloud environment optimization using hybrid fuzzy and swarm intelligence methods. Dr. Khanday is also skilled in patent writing, having developed innovative systems for crop disease detection and motorcycle safety. His publication record reflects his ability to effectively communicate complex methodologies, backed by data-driven validation and practical implementation. Additionally, his collaboration in multi-author projects and book chapters indicates strong academic teamwork and interdisciplinary engagement. His teaching and research experiences across different institutions have also honed his ability to mentor students and lead academic discussions. Equipped with technical, analytical, and conceptual research skills, Dr. Khanday continues to contribute impactful and scalable innovations across emerging fields like generative AI, IoT systems, and smart computing.

Awards and Honors

Dr. Nadeem Yousuf Khanday has received various forms of recognition for his scholarly achievements and research excellence. Notably, he has qualified multiple national-level eligibility exams, such as GATE, UGC-NET, and JK-SET, highlighting his academic distinction and competency to teach at the university level. In 2023, he was awarded recognition for his impactful contributions to AI-driven visual understanding and applications, as reflected in his high-impact publications and patents. His patent work, including an apparatus for auto-detection of crop diseases and motorcycle safety systems, has been acknowledged for its potential technological and societal value. Dr. Khanday’s research has also gained visibility through SCOPUS- and SCI-indexed publications with top journals like Computer Science Review and Neural Computing and Applications. His invited book chapters published by Taylor and Francis, Springer Nature, and Cambridge University Press underline his reputation among international academic publishers. Furthermore, he has presented at international conferences in Europe and Asia, receiving acclaim for his work on machine vision, fuzzy systems, and cloud intelligence. These accolades reflect both his individual excellence and collaborative impact within the research community.

Conclusion

Dr. Nadeem Yousuf Khanday exemplifies the profile of a high-caliber academician and innovative researcher with notable achievements in the fields of artificial intelligence, deep learning, and computer vision. Through a strong foundation in computer science education and a wealth of research experience, he has consistently contributed to advancing both theory and practice. His multidisciplinary research in healthcare, smart agriculture, and intelligent systems, along with a growing list of high-impact publications, patents, and book contributions, sets him apart as a forward-thinking scholar. His teaching experience across reputed Indian institutions and his ability to combine pedagogy with practical applications further enhance his value to academia. Dr. Khanday’s commitment to solving real-world problems using machine learning and AI tools not only enhances academic discourse but also promotes sustainable innovation. His emerging collaborations, international conference participation, and national recognitions affirm his credibility and future potential. In light of his qualifications, scholarly output, and research relevance, he stands as a highly deserving candidate for the Best Researcher Award, with the capacity to influence the global research community and contribute significantly to technological advancement

  1. Covariance-based Metric Model for Cross-domain Few-shot Classification and Learning-to-generalization
    📘 Journal: Applied Intelligence, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  2. Learned Gaussian ProtoNet for Improved Cross-domain Few-shot Classification and Generalization
    📘 Journal: Neural Computing and Applications, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  3. Deep Insight: Convolutional Neural Network and Its Applications for COVID-19 Prognosis
    📘 Journal: Biomedical Signal Processing and Control, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  4. Taxonomy, State-of-the-art, Challenges and Applications of Visual Understanding: A Review
    📘 Journal: Computer Science Review, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

Yijun Xiao | Computer Science | Best Researcher Award

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Mr. Yijun Xiao | Computer Science | Best Researcher Award

China University of Petroleum (East China), China 

Yijun Xiao is a highly motivated and innovative Ph.D. candidate at the China University of Petroleum (East China), known for his groundbreaking research at the intersection of computer science and molecular biology. His academic journey reflects a trajectory of excellence, transitioning from a master’s degree at Dalian University of Technology to advanced doctoral research focused on DNA computing and molecular neural networks. His recent work on programmable DNA-based molecular biocomputing circuits, published in Advanced Science, highlights his dedication to solving complex computational problems using biological substrates. Xiao’s research contributions are recognized internationally, with several publications in SCI-indexed journals and presentations at prestigious conferences like the IEEE Smart World Congress and the International Conference on Industrial Artificial Intelligence. He is not only a productive researcher but also a contributor to academic discourse through editorial roles in high-impact journals. With four patents and six journal articles to his name, his academic footprint is notable for a researcher at this stage. Xiao exemplifies the profile of a next-generation scientist poised to lead in the development of unconventional and bio-inspired computing technologies, making significant strides in non-silicon computing solutions with real-world applications in life sciences and bioinformatics.

Professional Profile

Education

Yijun Xiao earned his Master’s degree in Computer Science and Technology from Dalian University of Technology in 2023. This educational foundation equipped him with in-depth knowledge in algorithm design, artificial intelligence, and computational modeling. Currently, he is pursuing a Ph.D. at the China University of Petroleum (East China), where he focuses on interdisciplinary research involving computer science, molecular biology, and systems engineering. His doctoral work is centered around DNA computing, biochemical reaction networks, and the development of molecular controllers capable of solving high-level computational problems. The transition from a traditional computing background to a molecular computing framework reflects his adaptability and willingness to explore unconventional approaches to computing. His academic journey demonstrates a clear progression in specialization, from general computer science toward highly niche domains such as biochemical neural networks. Xiao’s education not only highlights strong academic performance but also his ability to integrate knowledge from multiple domains—a critical asset in research-intensive environments. With training grounded in both theoretical foundations and experimental research, Xiao is academically equipped to lead cutting-edge work in computational biology, unconventional computing, and interdisciplinary problem-solving.

Professional Experience

Although still in the early stages of his academic career, Yijun Xiao has demonstrated extensive professional engagement through his research and publication work. As a doctoral candidate, his primary professional responsibility involves conducting high-level scientific research that bridges computer science with biochemistry and molecular biology. He has played a lead role in designing and modeling programmable DNA-based biocomputing circuits that solve partial differential equations—an ambitious and novel application of bio-computation. His involvement in multiple international conferences, such as the IEEE Smart World Congress and the International Conference on Industrial Artificial Intelligence, reflects both his presentation skills and his readiness to contribute to global academic discourse. In addition to his research roles, he has participated in editorial duties for major journals like Advanced Science, IEEE Transactions on Nanobioscience, and IEEE Access, suggesting peer recognition of his scientific rigor and subject matter expertise. Furthermore, Xiao has authored and co-authored six SCI-indexed journal articles and has filed four patents, demonstrating both scholarly and applied research contributions. His professional experience, although rooted in academia, already exhibits a maturity and productivity that align with established researchers, signaling his readiness for broader leadership roles in future academic or research-intensive industry positions.

Research Interest

Yijun Xiao’s primary research interests lie in the domains of DNA computing, biochemical reaction networks, molecular controllers, and unconventional computing systems. His work focuses on leveraging the intrinsic parallelism of molecular systems to address computational problems that are traditionally solved using electronic and silicon-based technologies. One of his central interests involves the design and implementation of programmable DNA-based circuits capable of solving partial differential equations—a feat that merges molecular biology with complex mathematical modeling. He is particularly fascinated by the prospect of developing non-silicon-based computational architectures that mimic biological systems. This interest extends to synthetic biology, where his research could pave the way for bio-hybrid computing devices that function in tandem with natural biological processes. Xiao’s interdisciplinary curiosity drives him to explore how biomolecular substrates can be used not only for information storage and processing but also for autonomous control within chemical environments. His long-term goal is to create biocompatible computing systems that can be embedded in real-life biological contexts such as smart therapeutics, biosensing, and environmental diagnostics. The novelty and real-world applicability of his interests set him apart as a visionary in the rapidly evolving field of molecular and bio-inspired computing.

Research Skills

Yijun Xiao possesses an exceptional range of research skills that complement his interdisciplinary focus. His technical skills span computational modeling, algorithmic development, and system simulations, particularly within the context of DNA computing and biochemical reaction networks. He is adept at designing molecular circuits that perform logical and mathematical operations at the nanoscale. His experimental skills include working with DNA strands, implementing synthetic biochemical networks, and testing molecular controllers in simulated environments. Xiao is also proficient in data analysis, statistical modeling, and simulation tools, all of which are critical for validating theoretical models in biochemical systems. In addition to laboratory and computational capabilities, he demonstrates strong academic writing and peer-review skills, evidenced by his publications in high-impact journals and editorial responsibilities. He also exhibits strong collaborative skills, as seen in his partnerships with researchers from institutions like Dalian University. These collaborations have enabled him to broaden his methodological toolkit and approach problems from diverse scientific perspectives. His fluency in interdisciplinary communication allows him to translate complex concepts across domains, a rare and valuable skill in modern scientific research. Overall, Xiao’s research skills reflect a harmonious blend of theory, experimentation, and communication.

Awards and Honors

Although specific awards and honors have not been listed in the current nomination, Yijun Xiao’s publication record and involvement in high-impact journals suggest implicit recognition of his work. His article in Advanced Science—a prestigious international journal—indicates that his research meets the highest standards of innovation and scholarly contribution. Furthermore, the fact that he serves in editorial capacities for journals such as IEEE Transactions on Nanobioscience and IEEE Access is a significant mark of honor, especially for a Ph.D. candidate. These roles are typically reserved for researchers with demonstrated subject-matter expertise and strong academic judgment. Xiao has also been selected to present at esteemed international conferences like the IEEE Smart World Congress and the International Conference on Industrial Artificial Intelligence, which reflects peer recognition of the novelty and relevance of his work. His patent filings further emphasize the originality of his ideas and their potential for real-world application. While not formal awards, these accomplishments reflect an ongoing stream of recognition from the global academic and research community. As his career progresses, he is poised to receive formal accolades and fellowships that match the significance of his contributions.

Conclusion

Yijun Xiao represents the ideal profile of a next-generation researcher whose work is at the forefront of interdisciplinary science. His commitment to advancing DNA computing and molecular neural networks is both ambitious and impactful, addressing fundamental challenges in computational complexity using innovative biological models. Despite being in the early phase of his academic career, his productivity, publication quality, and international engagement far exceed typical expectations for a doctoral candidate. His research not only contributes theoretical value but also opens doors to practical applications in non-silicon-based computing and synthetic biology. With four patents and six SCI-indexed journal publications, he has already laid a strong foundation for an influential academic and research career. His future potential is further enhanced by his editorial experience, collaborative nature, and ability to lead projects that intersect multiple disciplines. Moving forward, expanding his work into industrial partnerships and broader scientific collaborations will further solidify his standing. Overall, Yijun Xiao is not only suitable for the Best Researcher Award but is a compelling candidate who exemplifies excellence, innovation, and future leadership in cutting-edge research domains.

Publications Top Notes

  1. Title: Programmable DNA‐Based Molecular Neural Network Biocomputing Circuits for Solving Partial Differential Equations
    Authors: Yijun Xiao, Alfonso Rodríguez‐Patón, Jianmin Wang, Pan Zheng, Tongmao Ma, Tao Song
    Year: 2025
    Journal: Advanced Science
  2. Title: Cascade PID Control Systems Based on DNA Strand Displacement With Application in Polarization of Tumor-Associated Macrophages
    Authors: Hui Xue, Hui Lv, Yijun Xiao, Xing’An Wang
    Year: 2023
    Journal: IEEE Access
  3. Title: Implementation of an Ultrasensitive Biomolecular Controller for Enzymatic Reaction Processes With Delay Using DNA Strand Displacement
    Authors: Yijun Xiao, Hui Lv, Xing’An Wang
    Year: 2023
    Journal: IEEE Transactions on NanoBioscience
  4. Title: Performance Verification of Smith Predictor Control Using IMC Scheme via Chemical Reaction Networks and DNA Strand Displacement Reaction
    Authors: Jingwang Yao, Hui Lv, Yijun Xiao
    Year: 2023
    Conference: 2023 IEEE Smart World Congress (SWC)
  5. Title: Synthetic Biology and Control Theory: Designing Synthetic Biomolecular Controllers by Exploiting Dynamic Covalent Modification Cycle with Positive Autoregulation Properties
    Authors: Yijun Xiao, Hui Lv, Xing’an Wang
    Year: 2023
    Journal: Applied Sciences
  6. Title: Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation
    Authors: Yijun Xiao, Hui Lv, Xing’an Wang
    Year: 2022
    Conference: 2022 4th International Conference on Industrial Artificial Intelligence (IAI)

Igor Sitnik | Computer Science | Best Researcher Award

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Prof. Igor Sitnik | Computer Science | Best Researcher Award

Leading Researcher from Joint Institute for Nuclear Research, Russia

Igor M. Sitnik is a distinguished physicist known for his pioneering contributions to nuclear and particle physics. With a research career spanning over five decades, he has played a central role in the analysis and interpretation of complex experimental data, particularly in the fields of light nuclei reactions and polarization phenomena. Sitnik has been instrumental in leading experimental collaborations at premier research institutions such as the Joint Institute for Nuclear Research (JINR) in Dubna and Jefferson Lab (JLab) in the United States. His career is marked by scientific rigor, collaborative leadership, and a commitment to advancing knowledge in subatomic physics. Having received multiple first-class JINR awards, he is recognized by his peers for excellence and innovation in experimental physics. His work has not only contributed valuable insights into nuclear structures and reaction mechanisms but also to the development of computational tools that enhance data interpretation in high-energy physics. With several highly cited publications, including one with over 900 citations, Sitnik remains a respected authority in his domain. His contributions continue to influence experimental design, data processing, and the theoretical understanding of fundamental particles, making him a deserving candidate for top honors in scientific achievement.

Professional Profile

Education

Igor M. Sitnik graduated from the Physics Department of Moscow State University in 1964, a renowned institution known for its rigorous training in fundamental and applied sciences. His education at one of the most prestigious universities in Russia provided him with a strong foundation in theoretical and experimental physics. During his formative academic years, he cultivated a deep interest in nuclear and subatomic physics, which would later define the focus of his professional career. His undergraduate studies were rooted in classical mechanics, quantum theory, electrodynamics, and statistical mechanics—courses that equipped him with analytical tools necessary for advanced research. His time at Moscow State University also introduced him to early computational methods and data analysis techniques, which he later expanded upon through decades of research. While no specific postgraduate degrees are mentioned, Sitnik’s career trajectory suggests extensive post-degree specialization and hands-on training in experimental nuclear physics and detector technology. His continuous professional development through participation in international collaborations and technical projects reflects a lifetime commitment to learning and scientific inquiry. The academic rigor and mentorship he received during his education played a significant role in shaping his methodical approach to research and long-term contributions to physics.

Professional Experience

Igor M. Sitnik has had a long and impactful career as a researcher, leader, and innovator in the field of nuclear and particle physics. Since the 1970s, he has been responsible for off-line analysis in his group at the Joint Institute for Nuclear Research (JINR) in Dubna. In the 1970s and 1980s, he led groundbreaking studies on the breakup reactions of light nuclei on various targets, a body of work that earned him the prestigious 1st JINR Prize in 1989. Moving into the 1990s, Sitnik shifted his focus to polarization phenomena, for which he also received the 1st JINR Prize in 1997. During this period, he served as co-spokesman for Proposal LNS 249 at Saturne-2 (JINR), underscoring his leadership role in international experimental collaborations. In the late 1990s, he became the spokesman for the “ALPHA” spectrometer project in Dubna. Most recently, he has been actively involved in studying the proton electric-to-magnetic form factor ratio (Gep/Gmp) at Jefferson Lab in the USA, with portions of this research conducted in Dubna, culminating in the 1st JINR Prize in 2020. His professional journey reflects a consistent dedication to experimental excellence, leadership in high-profile projects, and innovation in nuclear science.

Research Interests

Igor M. Sitnik’s research interests are centered around nuclear and particle physics, with a specific focus on reaction dynamics, polarization effects, and form factor studies. In the early stages of his career, he was deeply involved in investigating the breakup reactions of light nuclei, exploring how nuclear interactions change with varying target materials. This line of inquiry provided insights into nuclear structure and reaction mechanisms. In the subsequent decades, he expanded his interests to include polarization phenomena, examining spin-dependent interactions and their implications in nuclear scattering processes. These studies have practical applications in understanding fundamental nuclear forces and contribute to precision modeling in theoretical physics. More recently, Sitnik has engaged in form factor measurements at Jefferson Lab (JLab), particularly the ratio of electric to magnetic form factors of the proton (Gep/Gmp). This research is essential for understanding the internal structure of protons and has implications for quantum chromodynamics. Additionally, Sitnik has demonstrated a strong interest in data analysis methodologies, developing a minimization program in the 2010s for handling complex, multi-variable datasets. His ability to integrate experimental design with computational analysis defines his holistic and innovative approach to research in modern nuclear physics.

Research Skills

Igor M. Sitnik possesses a robust set of research skills that span experimental design, data analysis, computational modeling, and scientific communication. His early work in nuclear reaction dynamics required meticulous experimental planning, including the selection of beam-target configurations and detector setups. Sitnik’s responsibility for off-line analysis within his group highlights his proficiency in processing and interpreting large volumes of experimental data—skills that are essential in high-energy and nuclear physics research. He has demonstrated expertise in statistical analysis and error minimization, evident from the development of a custom minimization program for multi-set tasks. This computational tool showcases his aptitude for programming and algorithmic optimization, allowing for efficient parameter fitting in complex physical models. In collaborative settings, Sitnik has frequently held leadership roles, which underline his ability to manage interdisciplinary teams and guide long-term research projects. His high citation counts indicate a strong capability in publishing impactful findings and presenting them to the scientific community. Whether through experimental rigour, theoretical insight, or data processing innovation, Sitnik’s research skills reflect a well-rounded and highly competent physicist who has contributed significantly to advancing experimental techniques and analytical methodologies in his field.

Awards and Honors

Over the course of his esteemed career, Igor M. Sitnik has been the recipient of several top-tier scientific honors, most notably the 1st JINR Prize, which he has been awarded three times. The first was in 1989 for his extensive work on the breakup reactions of light nuclei, a cornerstone study in nuclear reaction physics. His second 1st JINR Prize was awarded in 1997 for his pivotal research on polarization phenomena in nuclear interactions. This body of work marked an important advancement in understanding spin-dependent processes. The third award came in 2020, recognizing his significant contributions to the study of the Gep/Gmp ratio—a key metric in probing the internal structure of the proton—conducted in part at Jefferson Lab (JLab) and partially in Dubna. These repeated honors from a leading international research institution testify to the lasting impact and high quality of Sitnik’s research. In addition to formal awards, his publication record includes several high-impact papers, one of which has been cited over 900 times, indicating broad recognition by the global physics community. His accolades place him among the most respected experimental nuclear physicists in the post-Soviet scientific world.

Conclusion

Igor M. Sitnik stands out as an exemplary researcher in the field of nuclear and particle physics. His decades-long contributions span pioneering experimental work, leadership in major international collaborations, and the development of advanced data analysis tools. With a career marked by three prestigious 1st JINR Prizes, he has consistently demonstrated a high level of scientific excellence and innovation. His impactful research on nuclear reactions, polarization phenomena, and proton structure has significantly advanced our understanding of subatomic processes. Sitnik’s ability to bridge theoretical insight with practical implementation through software development for data analysis highlights his multidimensional expertise. His research has not only yielded highly cited publications but has also contributed to shaping experimental protocols and analytical methods in modern physics. Though there are opportunities for enhanced mentorship and broader dissemination of his recent work, Sitnik’s legacy is firmly established. He continues to be a vital figure in the scientific community, with a body of work that exemplifies dedication, intellectual rigor, and collaborative spirit. These achievements make him a worthy and compelling candidate for the Best Researcher Award and solidify his position as a leader in advancing the frontiers of nuclear science.

Publications Top Notes

1. The Final Version of the 5D Histogram Package NORA

  • Author: I.M. Sitnik

  • Journal: Computer Physics Communications

  • Year: 2024

2. Debugging the FUMILIM Minimization Package

  • Authors: I.M. Sitnik, I.I. Alexeev, D.V. Nevsky

  • Journal: Computer Physics Communications

  • Year: 2024

  • Citations: 2

3. 5D Histogram Package NORA

  • Author: I.M. Sitnik

  • Journal: Computer Physics Communications

  • Year: 2023

4. Charge Exchange dp→(pp)n Reaction Study at 1.75 A GeV/c by the STRELA Spectrometer

  • Authors: S.N. Basilev, Y.P. Bushuev, S.A. Dolgiy, I.V. Slepnev, J. Urbán

  • Journal: European Physical Journal A

  • Year: 2021

  • Citations: 2

5. The Final Version of the FUMILIM Minimization Package

  • Authors: I.M. Sitnik, I.I. Alexeev, O.V. Selugin

  • Journal: Computer Physics Communications

  • Year: 2020

  • Citations: 9

6. Results of Measurements of the Analyzing Powers for Polarized Neutrons on C, CH₂ and Cu Targets for Momenta Between 3 and 4.2 GeV/c

  • Authors: I.M. Sitnik, S.N. Basilev, Y.P. Bushuev, J. Urbán, J. Mušinský

  • Type: Conference Paper

7. Measurement of Neutron and Proton Analyzing Powers on C, CH, CH₂ and Cu Targets in the Momentum Region 3–4.2 GeV/c

  • Authors: S.N. Basilev, Y.P. Bushuev, O.P. Gavrìshchuk, J. Urbán, J. Mušinský

  • Journal: European Physical Journal A

  • Year: 2020

  • Citations: 5

8. Technical Supplement to “Polarization Transfer Observables in Elastic Electron-Proton Scattering at Q² = 2.5, 5.2, 6.8 and 8.5 GeV²”

  • Authors: A.J.R. Puckett, E.J. Brash, M.K. Jones, B.B. Wojtsekhowski, S.A. Wood

  • Journal: Nuclear Instruments and Methods in Physics Research Section A

  • Year: 2018

 

 

Tejasva Maurya | Computer Science | Best Researcher Award

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Mr. Tejasva Maurya | Computer Science | Best Researcher Award

Shri Ramswaroop Memorial University, India

Tejasva Maurya is a dedicated researcher specializing in artificial intelligence, deep learning, and data science. With a strong academic background in computer science and engineering, he has made significant contributions to AI-driven solutions in smart traffic management, healthcare applications, and natural language processing. His work focuses on applying advanced machine learning models to real-world challenges, particularly in image processing, sentiment analysis, and human-computer interaction. Tejasva has published research in reputable journals and book chapters, showcasing his expertise in AI and its interdisciplinary applications. He has also gained valuable industry experience through internships in data science and analytics, working on projects that optimize machine learning models and enhance data-driven decision-making. His technical proficiency includes programming in Python, deep learning frameworks like PyTorch, and working with Hugging Face models for NLP and computer vision tasks. With multiple achievements in AI research, including a Scopus-indexed publication and competition awards, Tejasva continues to push the boundaries of innovation in artificial intelligence. His long-term goal is to contribute groundbreaking research in AI while bridging the gap between theoretical advancements and practical implementations.

Professional Profile

Education

Tejasva Maurya is currently pursuing a Bachelor of Technology in Computer Science and Engineering at Shri Ramswaroop Memorial University, where he has developed a strong foundation in programming, machine learning, and AI-driven applications. His coursework has provided extensive exposure to algorithms, data structures, deep learning, and computer vision techniques. Prior to his undergraduate studies, he completed his Intermediate education under the CBSE Board in 2021, securing an impressive 88.88%, which highlights his academic excellence and analytical abilities. His passion for artificial intelligence and research was evident early on, leading him to explore AI-related projects and specialized training in machine learning. Throughout his education, he has engaged in practical AI applications, contributing to his ability to develop innovative solutions in deep learning, NLP, and computer vision. His university studies have been complemented by self-driven research initiatives and internships, allowing him to apply theoretical knowledge to real-world problems. Tejasva’s continuous learning approach and commitment to AI research position him as an emerging talent in the field of artificial intelligence.

Professional Experience

Tejasva Maurya has gained substantial industry experience through internships and research projects in data science and machine learning. As a Data Scientist Intern at DevTown (June 2023 – December 2023), he worked on developing and optimizing deep learning models using PyTorch for real-world applications, focusing on NLP, image classification, and generative adversarial networks (GANs). He was responsible for designing data pipelines, preprocessing data, and conducting exploratory data analysis, ensuring the models were efficient and accurate. Additionally, Tejasva worked as a Data Analyst Trainee at MedTourEasy (August 2023 – August 2023), where he specialized in data visualization and statistical analysis. His role involved extracting actionable insights from large datasets using Python and Tableau and collaborating with different teams to implement data-driven strategies. His professional experience has strengthened his ability to apply AI techniques to practical problems, enhancing his understanding of machine learning implementation in different sectors. Through these roles, he has built strong analytical skills and technical expertise, preparing him for more advanced research in artificial intelligence and data science.

Research Interests

Tejasva Maurya’s research interests lie in artificial intelligence, deep learning, natural language processing, and computer vision. His primary focus is on developing AI-driven solutions for real-world applications, including smart traffic management, healthcare technology, and human-computer interaction. His work in vehicle classification using deep learning demonstrates his expertise in YOLO-based object detection models and their application in traffic surveillance and smart city planning. Additionally, he is keen on sentiment analysis and speech processing, contributing to AI models that improve text-to-speech (TTS) synthesis and NLP-based insights. His interest in federated learning for agricultural applications highlights his commitment to interdisciplinary research, exploring AI’s role in optimizing farming techniques and market stability. Tejasva is also exploring artificial emotional intelligence for psychological and mental health assessments, aiming to create AI models that assist in mental health diagnosis and emotional analysis. With a strong foundation in machine learning and AI, he aims to bridge the gap between theoretical advancements and practical AI implementations, driving innovation in multiple domains.

Research Skills

Tejasva Maurya possesses advanced research skills in machine learning, deep learning, and AI model development. His technical expertise includes Python programming, with proficiency in PyTorch, scikit-learn, NumPy, and OpenCV for implementing AI-based solutions. He has hands-on experience in computer vision techniques, including real-time object detection, image segmentation, and gesture-based human-computer interaction, leveraging tools like Mediapipe and Haar Cascades. In natural language processing (NLP), he is skilled in text processing, speech-to-text, and fine-tuning transformer models using Hugging Face frameworks. His research methodology includes data preprocessing, model fine-tuning, hyperparameter optimization, and performance evaluation using metrics like mAP and F1-score. He is proficient in working with large-scale datasets and has successfully published research on vehicle classification, federated learning, and AI-based healthcare applications. Additionally, he has experience in GANs and diffusion models, focusing on synthetic media generation and speech dataset augmentation. His ability to integrate AI solutions across different fields demonstrates his versatility as a researcher and innovator.

Awards and Honors

Tejasva Maurya has received multiple accolades for his contributions to AI research and innovation. One of his most notable achievements is publishing a Scopus-indexed journal article, “Real-Time Vehicle Classification Using Deep Learning—Smart Traffic Management,” in Engineering Reports (Wiley), which underscores the real-world impact of his research. He has also co-authored multiple book chapters in prestigious publishers like Nova Science, Wiley, and Bentham Science, covering AI applications in healthcare, federated learning, and artificial emotional intelligence. His research has been recognized for its contribution to intelligent traffic systems, patient-centric healthcare, and AI-powered decision-making. In addition to his research achievements, he secured 1st position in KIMO’s-Edge’ 23 Technology Competition, a testament to his problem-solving skills and technical expertise. His consistent excellence in AI research and project development has positioned him as an emerging leader in the field of artificial intelligence, with a strong track record of achievements.

Conclusion

Tejasva Maurya is a promising researcher in artificial intelligence, with expertise in deep learning, NLP, and computer vision. His strong academic foundation, technical proficiency, and impactful research make him a strong contender for recognition as a leading researcher in AI. With multiple publications, real-world AI applications, and industry experience, he has demonstrated both theoretical knowledge and practical problem-solving abilities. While he has made significant contributions, focusing on publishing in high-impact AI conferences, securing patents, and expanding interdisciplinary collaborations would further enhance his research portfolio. His dedication to bridging AI theory with real-world applications highlights his potential to contribute groundbreaking advancements in artificial intelligence.

Publications Top Notes

  1. Title: Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management
    Authors: Maurya, T., Kumar, S., Rai, M., Saxena, A.K., Goel, N., and Gupta, G.
    Publication: Engineering Reports, 7: e70082 (2025)
    DOI: https://doi.org/10.1002/eng2.70082

  2. Title: Patient Centric Healthcare
    Authors: Maurya, T., Kumar, S., Rai, M., Saxena, A.K.
    Book: Harnessing the Power of IoT-Enabled Machine Learning in Healthcare Applications
    Editors: Mritunjay Rai, Ravindra Kumar Yadav, Neha Goel, and Maheshkumar H. Kolekar

  3. Title: Integrating Artificial Intelligence and Deep Learning in Classification and Taking Care of DFU
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K., Pandey, J.K.
    Book: Machine Learning-Based Decision Support Systems for Diabetic Foot Ulcer Care
    Editors: Mritunjay Rai, Jay Kumar Pandey, and Abhishek Kumar Saxena

  4. Title: Federated Learning-Based Approach for Crop Recommendation and Market Stability in Agriculture
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K.
    Book: Federated Learning for Smart Agriculture and Food Quality Enhancement
    Editors: Padmesh Tripathi, Bhanumati Panda, Shanthi Makka, Reeta Mishra, S. Balamurugan, and Sheng-Lung Peng

  5. Title: Artificial Emotional Intelligence for Psychological State and Mental Health Assessment
    Authors: Kumar, S., Maurya, T., Rai, M., Saxena, A.K.
    Book: Artificial Emotional Intelligence: Fundamentals, Challenges and Applications
    Editors: Padmesh Tripathi, Krishna Kumar Paroha, Reeta Mishra, and S. Balamurugan