Aditta Chowdhury | IoT (Internet of Things) Analytics | Research Excellence Award

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Mr. Aditta Chowdhury | IoT (Internet of Things) Analytics | Research Excellence Award

Chittagong University of Engineering and Technology | Bangladesh

Aditta Chowdhury is an electrical and electronic engineering researcher and academic whose work focuses on advancing biomedical signal processing, embedded systems, and sustainable energy technologies. He holds both a Bachelor of Science and a Master of Science in Electrical and Electronic Engineering from the Chittagong University of Engineering and Technology, graduating with top academic distinction. His professional journey includes teaching and research roles in reputed engineering institutions, where he has contributed to curriculum development, laboratory instruction, and collaborative research. His research experience spans FPGA-based biomedical signal processing, photoplethysmogram-driven cardiovascular and metabolic disease detection, multimodal physiological signal analysis, plasmonic biosensing, microgrid feasibility assessment, and low-power VLSI system design. He has published extensively in high-impact journals and international conferences, demonstrating expertise in hardware–software co-design, machine learning applications in healthcare, and emerging sensor technologies. His work has resulted in several innovative solutions, including cardiovascular disease classifiers, hypertension detection systems, EOG-based eye-movement processors, and microgrid sustainability assessments. He has received multiple academic scholarships and awards recognizing his exceptional academic performance and research accomplishments. Driven by a commitment to impactful scientific contribution, he aims to continue developing intelligent, efficient, and accessible engineering solutions through interdisciplinary research and innovation.

Profile : Google Scholar

Featured Publications

Chowdhury, A., Das, D., Eldaly, A.B.M., Cheung, R.C.C., & Chowdhury, M.H. (2024). “Photoplethysmogram-based heart rate and blood pressure estimation with hypertension classification.” IPEM–Translation, 100024.

Joy, J.D., Rahman, M.S., Rahad, R., Chowdhury, A., & Chowdhury, M.H. (2024). “A novel and effective oxidation-resistant approach in plasmonic MIM biosensors for real-time detection of urea and glucose in urine for monitoring diabetic and kidney disease.” Optics Communications, 573, 131012.

Chowdhury, A., Das, D., Hasan, K., Cheung, R.C.C., & Chowdhury, M.H. (2023). “An FPGA implementation of multiclass disease detection from PPG.”
IEEE Sensors Letters, 7(11), 1–4.

Islam, M.A., Chowdhury, A., Jahan, I., & Farrok, O. (2024). “Mitigation of environmental impacts and challenges during hydrogen production.”
Bioresource Technology, 131666.

Chowdhury, A., Das, D., Cheung, R.C.C., & Chowdhury, M.H. (2023). “Hardware/software co-design of an ECG–PPG preprocessor: A qualitative and quantitative analysis.”
Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering, 9.

Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

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Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.

Helen Thouless | Qualitative Research | Research Excellence Award

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Dr. Helen Thouless | Qualitative Research | Research Excellence Award

St MAry’s University Twickenham | United Kingdom

Dr. Helen Thouless is a Senior Lecturer in Primary Mathematics at St Mary’s University, Twickenham, with a PhD in Learning Sciences from the University of Washington (2014), where her dissertation examined whole-number place-value understanding in children with dyslexia. She also holds a Masters in Teaching (University of Washington, 2001) and a BA in Psychology (Reed College, 1995). With extensive experience in both US and UK schools, she has taught primary mathematics, special education, and early years settings, including international experience in Tanzania. Her research focuses on mathematics learning for children with learning difficulties, early-years patterning, and inclusive pedagogy, employing methodologies such as clinical interviews, observations, video analysis, and action research. She has authored books including The Power of Pattern: Patterning in the Early Years and co-edited Enabling Mathematics Learning of Struggling Students. Dr. Thouless has contributed to numerous peer-reviewed publications on early mathematics, pattern recognition, and special education, with an h-index of 1, 5 published documents, and 3 citations across key works. She supervises PhD and EdD students, contributes to teacher education programs, and holds leadership roles in educational organizations promoting mathematics inclusion. Her work bridges research and practice, aiming to make mathematics accessible, conceptually rich, and engaging for all learners.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Ockelford, A., McCarthy, S., Gifford, S., Thouless, H., Kirk, S., & Thorpe, M. (2025). “Towards a new taxonomy of pattern-making in the visuo-spatial domain in early childhood based on zygonic theory and the Sounds of Intent framework of musical development.” Music & Science.

Thouless, H., Xin, Y. P., & Tzur, R. (2022). Enabling mathematics learning of struggling students: International perspectives.

Thouless, H., Borthwick, A., & Gifford, S. (2021). The power of pattern: Patterning in the early years.

Thouless, H., Gifford, S., Moses, K., & James, R. (2020). “Reasoning about patterns.” Mathematics Teaching.

Thouless, H., & Gifford, S. (2019). “Dotty triangles.” For the Learning of Mathematics.

Daxiong Ji | Data Analysis | Research Excellence Award

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Dr. Daxiong Ji | Data Analysis | Research Excellence Award

Zhejiang University | China

Dr. Daxiong Ji is an Associate Professor at the Institute of Marine Electronics and Intelligent Systems, Ocean College, Zhejiang University, and a Senior Member of IEEE. He earned his Ph.D. in Pattern Recognition and Intelligent Systems from the University of Chinese Academy of Sciences and a B.S. in Automation from Wuhan Polytechnic University. Dr. Ji has held visiting scholar positions at the University of Melbourne, University of Plymouth, and University of Victoria, collaborating internationally on advanced marine robotics research. His expertise spans automatic control, artificial intelligence applications, autonomous marine robotics, electrical engineering, and intelligent systems. He has supervised numerous undergraduate, master’s, and doctoral students, leading courses on Marine Robot Design, Marine Intelligent Systems, and Professional Practices. Dr. Ji has received multiple honors, including the First Prize of Zhejiang Provincial Teaching Achievement Award, recognition as an Excellent Moral Education Mentor, and awards for scientific and technological achievements from the Chinese Academy of Sciences. He has led and participated in numerous national and international research projects, focusing on autonomous navigation, fault diagnosis, and data-driven control of underwater vehicles. Dr. Ji is also an accomplished inventor with multiple patents in underwater robotics and autonomous systems. His research has been widely published in top journals, reflecting significant contributions to marine intelligent systems.

Profiles : Orcid | Google Scholar

Featured Publications

Ji, D.; Ogbonnaya, S.G.; Hussain, S.; Hussain, A.F.; Ye, Z.; Tang, Y.; Li, S. Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles. Electronics, 2025, 14(3), 489.

Xu, L.; Ji, D. Online Fault Diagnosis Using Bioinspired Spike Neural Network. IEEE Transactions on Industrial Informatics, 2024.

Zhou, J.; Ye, Z.; Zhao, J.; Ji, D.; Peng, Z.; Lu, G.; Tadda, M.A.; Shitu, A.; Zhu, S. Multi-detector and Motion Prediction-Based High-Speed Non-Intrusive Fingerling Counting Method. Biosystems Engineering, 2024, 218, 1–15.

Ji, D.; Wang, R. Path Following of QAUV Using Attitude-Velocity Coupling Model. Ocean Engineering, 2024, 293, 116885.

Ji, D.; Cheng, H.; Zhou, S.; Li, S. Dynamic Model-Based Integrated Navigation for a Small and Low-Cost Autonomous Surface/Underwater Vehicle. Ocean Engineering, 2023, 273, 114091.

Samaneh Saeedinia | Engineering | Best Researcher Award

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Prof.Dr. Samaneh Saeedinia | Engineering | Best Researcher Award

Iran University of Science and Technology | Iran

Dr. Samaneh Alsadat Saeedinia is an accomplished researcher and engineer specializing in Control Electrical Engineering, Computational Neuroscience, Robotics, and Artificial Intelligence. She earned her Ph.D. from Iran University of Science and Technology (IUST), where her thesis focused on designing and stabilizing intelligent decision-making systems to support the treatment of adult focal epilepsy, receiving an excellent grade. She also holds a Master’s degree in Control Electrical Engineering from IUST and a Bachelor’s degree from Imam Khomeini International University. Dr. Saeedinia has extensive experience in signal processing, machine learning, spiking neural networks, EEG and MRI data analysis, and adaptive control systems. She has contributed to numerous high-impact publications in IEEE Transactions, Scientific Reports, and other international journals. Her research interests include computational modeling, intelligent control, data fusion, robotics path planning, and neuroinformatics. Beyond academia, she has led industrial projects in electrical power systems, automation, and instrumentation. She has received multiple awards, including the Khwarizmi International Award and top ranks in academic performance. Dr. Saeedinia is also an experienced educator and mentor, guiding students in advanced control systems, neural networks, and data analysis. Her work bridges engineering, neuroscience, and artificial intelligence, aiming to develop innovative solutions for healthcare and robotic applications.

Profile : Google Scholar

Featured Publications

Saeedinia, S.A., Jahed-Motlagh, M.R., Tafakhori, A., & Kasabov, N.K. (2024). “Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine” in Scientific Reports, 14(1), 10667.

Mohaghegh, M., Saeedinia, S.A., & Roozbehi, Z. (2023). “Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles” in Frontiers in Robotics and AI, 10, 1226028.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2024). “Dynamic-structured reservoir spiking neural network in sound localization” in IEEE Access, 12, 24596–24608.

Saeedinia, S.A., & Tale Masouleh, M. (2022). “The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision” in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2025). “Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks” in IEEE Access.

Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

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Prof. Dr. Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

University of Silesia | Poland

Prof. Dr. Piotr Porwik is a full professor in the Institute of Computer Science at the University of Silesia in Katowice, specializing in engineering and technical sciences. He earned his M.Sc. in computer science from the University of Silesia in 1978, his Ph.D. in 1985, and completed his habilitation in 2006 at AGH University of Science and Technology in Krakow. His research spans machine learning, biometrics, image processing, biomedical imaging, and spectral methods for Boolean functions. Across his career, he has authored 81 scientific documents that have attracted 1,003 citations from 783 citing documents, and he holds an h-index of 17. He has published extensively in journals, international conferences, books, and book chapters, while also supervising Master’s and Ph.D. students. Prof. Porwik has served in major academic leadership roles, including Deputy Dean and Director of the Institute, and played a key role in shaping academic publishing as Editor-in-Chief of the Journal of Medical Informatics and Technologies. His achievements have been recognized through numerous distinctions, including the Bronze Cross of Merit and multiple awards from the President of the University of Silesia for scientific accomplishments. His work continues to advance biometric security, classifier development, and biomedical informatics, underscoring his long-standing academic influence and research innovation.

Profiles : Scopus | Orcid

Featured Publications

Wrobel, K., Porwik, P., & Orczyk, T. (2025). “Evaluation of the Effectiveness of Ranking Methods in Detecting Feature Drift in Artificial and Real Data” in (Book) / Lecture Notes in?

Mensah Dadzie, B., & Porwik, P. (2025). “Feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis” in (Book)

Porwik, P., Orczyk, T., Wrobel, K., & Mensah Dadzie, B. (2025). “A Novel Method for Drift Detection in Streaming Data Based on Measurement of Changes in Feature Ranks” in Journal of Artificial Intelligence and Soft Computing Research, (DOI: 10.2478/jaiscr-2025-0008).

Porwik, P., Orczyk, T., & Japkowicz, N. (2024). “Supervised and Unsupervised Analysis of Feature Drift in a New Type of Detector” in Preprint (Research Square)

Porwik, P., Orczyk, T., & Doroz, R. (2022). “A Stable Method for Detecting Driver Maneuvers Using a Rule Classifier” in Lecture Notes in Computer Science

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

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Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

Profile : Google Scholar

Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.

Mittar Pal | Engineering | Women Researcher Award

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Dr. Mittar Pal | Engineering | Women Researcher Award

Deenbandhu Chhotu Ram University of Science and Technology | India

Dr. Mittar Pal is an experienced academic and researcher in Electronics & Communication Engineering with over twelve years of teaching and research experience, specializing in renewable energy systems and AI‑based engineering applications. He holds a B.Tech in ECE (2003), M.Tech in Nanoscience & Technology (2013) and completed his Pre‑PhD in ECE (2019) from renowned Indian institutions, and recently earned his PhD on barrier identification and prioritisation for solar photovoltaic system deployment in dairy farming in India (2023). His work on techno‑economic analysis of photovoltaic systems in dairy farming and optimization of utility‑integrated solar PV systems has appeared in SCI and Scopus‑indexed outlets, and he has supervised numerous B.Tech and M.Tech projects while contributing to NBA documentation and curriculum development. He is UGC‑NET qualified for Electronic Science (Assistant Professor). His research interests span signal & system theory, digital electronics, power electronics, renewable solar energy modelling (using MATLAB, HOMER Pro, SAM, RETScreen), AI/ML, generative AI, IoT and pattern recognition. With a growing scholarly profile (h‑index ~ 5 and citation count ~5 as per publicly listed profiles), he continues to publish, present at international conferences, and drive innovation in teaching and research. His dedication to transforming traditional dairy farming through solar PV integration underscores his commitment to sustainability and engineering education.

Profile: Scopus 

Featured Publications

Mittarpal. (2018). “Modeling and Simulation of Solar Photovoltaic Module using Matlab-Simulink.” International Journal for Research in Engineering Application & Management .

Mittarpal. (2018). “Modeling Design and Simulation of Photovoltaic Module with Tags Using Simulink.” International Journal of Advance & Innovative Research.

Mittarpal, & Asha. (2018). “Simulation and Modeling of Photovoltaic Module Using Simscape.” National Conference on Advances in Power, Control & Communication Systems .

Mittarpal, Naresh Kumar, & Priyanka Anand, Sunita. (2017). “Realization of Flip Flops Using LabVIEW and MATLAB.”

Mittarpal, Naresh Kumar, & Sunita Rani. (2017). “Realisation of Digital Circuits Systems Using Embedded Function on MATLAB.” International Journal of Engineering and Technology.

Muhammad Saddam Khokhar | Data Representation | Best Researcher Award

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Dr. Muhammad Saddam Khokhar | Data Representation | Best Researcher Award

Yangzhou University | China

Dr. Muhammad Saddam Khokhar is an accomplished academic and researcher specializing in generative artificial intelligence and computer vision. With over a decade of teaching, research, and leadership experience, he has made significant contributions to artificial intelligence, quantum computing, and deep learning. His expertise spans across developing innovative AI-driven solutions, managing academic programs, and mentoring research scholars at undergraduate, postgraduate, and doctoral levels. As an assistant professor and former department chairman, he has been instrumental in advancing artificial intelligence education, accreditation processes, and research-driven innovation in multiple institutions internationally.

Professional Profiles

ORCID

GOOGLE SCHOLAR

Education

Dr. Khokhar earned his Doctorate in Computer Application Technology from Jiangsu University, China, where his dissertation focused on nonlinear optimization and dimensional reduction using advanced Spearman correlation analysis integrated with deep learning models. His research was recognized with an excellent dissertation award for its innovative methodologies. He also holds a Master’s degree in Computer Science and Information Technology with a focus on machine learning, data mining, and software project management, as well as a Bachelor’s degree in Software Engineering, where he developed strong expertise in artificial intelligence, software architecture, and advanced programming techniques.

Experience

With extensive academic and professional experience, Dr. Khokhar has served in multiple teaching and research roles, including Assistant Professor at the College of Artificial Intelligence, Yangzhou University, and Postdoctoral Fellow at the College of Software Engineering, focusing on generative AI projects. He has also chaired the Department of Artificial Intelligence at Dawood University of Engineering and Technology and contributed to curriculum development, laboratory design, and accreditation processes for AI and quantum computing programs. Earlier in his career, he held lecturer positions at leading Pakistani universities and worked as a software engineer, gaining industry insights that enriched his academic pursuits.

Research Interests

His research primarily focuses on generative artificial intelligence, computer vision, deep learning, and nonlinear optimization techniques. Dr. Khokhar has developed innovative models leveraging Spearman correlation analysis for medical image processing, robotic vision, traffic surveillance, and multi-camera monitoring systems. His recent projects explore lightweight generative adversarial networks (IoTGAN), hybrid attention-driven generative frameworks, and blockchain-based secure election systems. He has also actively contributed to discussions on artificial general intelligence, metaverse technologies, and ethical implications of AI in modern societies through various publications and invited talks.

Awards

Dr. Khokhar has been recognized with several prestigious awards for his contributions to innovation, research, and academic excellence. These include the Best Innovation Award for developing a revolutionary AI platform for gene editing and medical technology, multiple academic achievement awards during his doctoral studies, and accolades in application development and research competitions. His work has been published in high-impact journals and presented at international conferences, showcasing his commitment to advancing cutting-edge AI research and practical solutions for global challenges.

Publications

Muhammad Saddam Khokhar, Misbah Ayoub, Zakria, Abdullah Lakhan. (2025). “Advanced Nonlinear Optimization of Low- and High-Resolution Medical Images Using Adaptive Deep Spearman Correlation Analysis (D-SCA) for Pattern Sequence Recognition.” Applied Soft Computing.

Muhammad Saddam Khokhar. (2025). “Energy Harvesting in IoT, Fog, and Blockchain–Thermoelectric Cooling Innovations.”

Muhammad Saddam Khokhar. (2025). “Health Coin: Revolutionizing Global Healthcare with Green Blockchain Technology.”

Zakria Zakria, Jianhua Deng, Rajesh Kumar, Muhammad Saddam Khokhar, Jingye Cai, Jay Kumar. (2022). “Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Abdullah Lakhan, Qurat-ul-ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, Syed Aziz Shah, Nadeem Anjum, Qammer Hussain Abbasi, et al. (2021). “Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network.” Electronics.

Abdullah Lakhan, Mazhar Ali Dootio, Tor Morten Groenli, Ali Hassan Sodhro, Muhammad Saddam Khokhar. (2021). “Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks.” Electronics.

Conclusion

Dr. Muhammad Saddam Khokhar embodies a dynamic blend of academic rigor, innovative research, and leadership in the field of artificial intelligence. His career reflects a commitment to advancing AI education, developing impactful technologies, and contributing to global scientific knowledge. Through his research, publications, and mentorship, he continues to inspire the next generation of AI professionals while fostering interdisciplinary collaborations that bridge technology with societal advancement.

Mikhail Zabezhaylo | Data Mining | Worldwide Innovation in Research Analytics Award

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Prof Dr. Mikhail Zabezhaylo | Data Mining | Worldwide Innovation in Research Analytics Award

Federal Research Center “Informatics and Management” of the Russian Academy of Sciences | Russia

Author Profile

Orcid

Scopus

Early Academic Pursuits 🎓

Mikhail I. Zabezhaylo, born in 1956, graduated from the Moscow Institute of Physics and Technology (MIPT) in 1979, specializing in Applied Mathematics and Artificial Intelligence (AI). Under the guidance of Prof. D.A. Pospelov and Prof. V.K. Finn, he earned his Ph.D. in Mathematical Cybernetics in 1983 at MIPT, marking the beginning of his deep academic foundation in theoretical computer science.

Professional Endeavors 💼

Dr. Zabezhaylo has held significant positions at major scientific institutions in the USSR and Russia, starting with the All-Union Institute of Scientific and Technical Information (VINITI) and later at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences (FRC CSC RAS). His career spans roles like Senior Researcher, Associate Professor, and Chief Researcher, and he currently leads the Laboratory “Intelligent Data Analysis”.

Contributions and Research Focus on  Data Mining 🔬

Dr. Zabezhaylo’s research primarily revolves around Artificial Intelligence (AI), data mining, information systems, and decision support systems. He has been involved in high-profile projects with major organizations such as Gazprom, Russian Railroads, and Siemens, focusing on real-world AI applications. His contributions include leading significant R&D projects, like the Skolkovo Foundation’s “Software Defined Networks” mega-grant.

Impact and Influence 🌍

With 180+ scientific publications, Dr. Zabezhaylo has had a major impact on AI and data analysis methodologies, shaping both theoretical and applied computer science. His work is recognized globally, influencing industries, academia, and governmental bodies. His ability to bridge theory and practice has made him a thought leader in AI-powered systems.

Academic Citations 📚

Dr. Zabezhaylo’s work is highly cited, with over 180 publications referenced in major academic journals and conferences. His research on artificial intelligence, decision support systems, and data analysis continues to be a valuable resource for scholars and professionals alike.

Technical Skills 🖥️

Dr. Zabezhaylo is proficient in mathematical modeling, AI, data mining, and decision support systems. His skills extend beyond theory, as he is adept at leading large-scale R&D projects and collaborating with commercial and governmental entities, applying his expertise in real-world solutions.

Teaching Experience 🧑‍🏫

Dr. Zabezhaylo has over 20 years of teaching experience at MIPT, Moscow State University, and other prominent universities. As a professor, he has lectured and conducted seminars on Artificial Intelligence, shaping the future of AI research and training the next generation of AI professionals.

Legacy and Future Contributions 🔮

Dr. Zabezhaylo’s legacy in AI and computer science is firmly established. He continues to lead advancements in data analysis and AI technologies through his work at FRC CSC RAS. His future contributions will likely continue to drive innovation in AI systems and their applications across diverse sectors.

 Notable Publications  📑

On Some Current Myths about Modern Artificial Intelligence

Authors: Zabezhailo, M.I., Mikheyenkova, M.A., Finn, V.K.

Journal: Pattern Recognition and Image Analysis

Year: 2024

On the Problem of Explaining the Results of Intelligent Data Analysis

Authors: Zabezhailo, M.I.

Journal: Pattern Recognition and Image Analysis

Year: 2024

On Some Possibilities of Using AI Methods in the Search for Cause-And-Effect Relationships in Accumulated Empirical Data

Authors: Grusho, A., Grusho, N., Zabezhailo, M., Timonina, E.

Journal: Lecture Notes in Networks and Systems

Year: 2024

Probabilistic Models for Detection of Causal Relationships in Data Sequences

Authors: Grusho, A., Grusho, N., Zabezhailo, M., Timonina, E.

Journal: Lecture Notes in Networks and Systems

Year: 2024

Statistical Causality Analysis

Authors: Grusho, A.A., Grusho, N.A., Zabezhailo, M.I., Samouylov, K.E., Timonina, E.E.

Journal: Discrete and Continuous Models and Applied Computational Science

Year: 2024