Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

Dr. Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

King Faisal University | Saudi Arabia

Dr. Shaymaa E. Sorour is an Assistant Professor of Computer Science specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with a strong focus on educational technologies and intelligent learning systems. She earned her Ph.D. in Computer Science from Kyushu University, Japan (2016), following an M.Sc. in Computer Education and a B.Sc. in Computer Teacher Preparation with honors. Dr. Sorour has extensive academic experience across teaching, research, quality assurance, and academic advising, serving in faculty roles at King Faisal University, Saudi Arabia, and Kafrelsheikh University, Egypt. Her research integrates data mining, learning analytics, student performance prediction, adaptive and intelligent educational systems, and technology-enhanced learning, with publications in leading international journals and conferences. She has actively contributed to global scholarly communities through sustained participation in IEEE, LNCS, and international education and AI venues. Her scholarly impact includes 486 citations, 49 documents, and an h-index of 11, reflecting consistent contributions to AI in education and learning analytics (486 citations by 441 documents). Among her recognitions, she received a Best Paper Award at an international conference. Dr. Sorour’s work continues to bridge advanced computational intelligence with practical, scalable educational innovation, supporting data-driven decision-making and improved learning outcomes worldwide.

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Featured Publications

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

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.

Weitao Yue | Engineering | Research Excellence Award

Mr. Weitao Yue | Engineering | Research Excellence Award

China University of Mining and Technology | China

Mr. Weitao Yue is a Ph.D. candidate at the China University of Mining and Technology, specializing in Safety Science and Engineering with a focus on coal and rock dynamic disaster prevention and control. His research is dedicated to addressing critical challenges in coal mine safety, emphasizing the investigation of underlying mechanisms, monitoring and early warning systems, and the development of innovative control technologies for coal and rock dynamic disasters. He has actively contributed to multiple national-level major research projects, including the National Natural Science Foundation of China (NSFC) projects, National Key Research and Development Program projects, and National Major Scientific Instrument Development projects, where he played pivotal roles in theoretical innovation, experimental research, and data analysis. Mr. Yue has published seven high-impact SCI papers as first or corresponding author in top-tier journals such as International Journal of Mining Science and Technology, Engineering Geology, Rock Mechanics and Rock Engineering, Measurement, Journal of Central South University, and Physics of Fluids. Notably, two of his papers have been recognized as ESI Highly Cited, ranking among the top 1% most cited globally, reflecting his significant academic influence. His research provides theoretical foundations and technical solutions to enhance coal mine safety, contributing substantially to the advancement of mining engineering and disaster prevention technologies.

Profile : Orcid

Featured Publications

Feng, X., Yue, W., Cao, Z., Zhou, S., Cao, X., & Wang, E. (2026). “Multi-scale DEM simulation and experimental validation of water-saturated coal failure under variable-angle shear loading: Revealing hydrochemical coupling damage mechanisms.” Measurement. https://doi.org/10.1016/j.measurement.2025.119812

Feng, X., Yue, W., Zhao, X., Wang, D., Liu, Q., & Ding, Z. (2025). “Evolution law and risk analysis of fault-slip burst in coal mine based on microseismic monitoring.” Environmental Earth Sciences. https://doi.org/10.1007/s12665-024-12080-5

Samaneh Saeedinia | Engineering | Best Researcher Award

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.

Alexander Churbanov | Predictive Modeling Innovations | Best Researcher Award

Dr. Alexander Churbanov | Predictive Modeling Innovations | Best Researcher Award

Keldysh Institute of Applied Mathematics, Russian Academy of Sciences | Russia

Dr. Alexander Churbanov is a senior scientist at the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, specializing in mathematical modeling, computational fluid dynamics (CFD), and coupled physical phenomena in porous media and viscous flows. He received his M.S. and Ph.D. degrees in Mathematical Modeling from Lomonosov Moscow State University in 1978 and 1988, respectively, with his doctoral thesis focused on numerical simulation of convective phenomena in technological facilities. Dr. Churbanov began his career as an engineer developing training systems for Soviet orbital flights at the Bureau of Space Research “ENERGIA” and later contributed to semiconductor material production modeling at the State Institute of Rare Metals Industry. Since 1988, he has served as a Senior Scientist at Keldysh Institute, where his research emphasizes the development of efficient algorithms and codes for CFD applications. He has authored 34 documents, including 25 SCI/Scopus-indexed journal papers, and his work has been cited 240 times, yielding an h-index of 8. His research interests encompass incompressible and slightly compressible viscous flows, coupled phenomena, open-source CFD software, and predictive modeling innovations. Recognized for contributions to mathematical modeling of coupled physical systems, Dr. Churbanov continues to advance computational methods, bridging theory and practical applications in engineering and applied sciences.

Profiles : Scopus | Orcid

Featured Publications

Churbanov, A. G., Churbanova, N. G., & Trapeznikova, M. A. (2023). “Coupled Prediction of Flows in Domains Containing a Porous Medium and a Free Stream.” Mathematical Models and Computer Simulations.

Churbanova, N. G., Trapeznikova, M. A., Churbanov, A. G., & Emets, V. V. (2023). “Prediction of Flows in an Automotive Catalytic Converter.” In Systems of Signals Generating and Processing in the Field of On-Board Communications, SOSG 2023 – Conference Proceedings.

Podryga, V. O., Churbanov, A. G., Tarasov, N. I., Polyakov, S. V., Trapeznikova, M. A., & Churbanova, N. G. (2022). “Multiscale Approach for Modeling Multiphase Fluid Flows in Installations for Reprocessing of Natural Gas.” Lobachevskii Journal of Mathematics.

Churbanov, A., Churbanova, N., Polyakov, S., & Trapeznikova, M. (2021). “Coupled Calculations of Flows in Domains Including a Porous Medium and a Homogeneous Fluid.” In Proceedings – 2021 Ivannikov Memorial Workshop, IVMEM 2021.

Polyakov, S. V., Trapeznikova, M. A., Churbanov, A. G., & Churbanova, N. G. (2021). “Prediction of Incompressible Flows in a Porous Medium-Free Stream System.” Keldysh Institute Preprints.

Gleb Sychugov |  Healthcare Data Analysis | Best Researcher Award

Assoc. Prof. Dr. Gleb Sychugov |  Healthcare Data Analysis | Best Researcher Award

Southern Urals Federal Research and Clinical Center for Medical Biophysics | Russia

Assoc. Prof. Dr. Gleb Sychugov is a distinguished pathologist and medical researcher with a Ph.D. in Medicine and the highest qualification category in pathology. He serves as Deputy Chief Physician at the Chelyabinsk Regional Pathological Anatomy Bureau, Associate Professor at South Ural State Medical University, and Senior Research Fellow at the Southern Urals Federal Research and Clinical Center for Medical Biophysics (SUFRCC MB). With over 190 scientific publications, including 25 in Scopus-indexed journals and 4 patents, Dr. Sychugov has made pioneering contributions to radiation pathology, pulmonary pathology, reproductive system pathology, oncology, and dermatopathology. His research innovations include patented diagnostic algorithms for cervical intraepithelial neoplasia and mucinous ovarian carcinoma, as well as computational models for predicting dermal changes in anti-aging therapy. As Chief Pathologist of the Chelyabinsk Region and the Ural Federal District, he has led multiple regional and federal initiatives in pathology standardization. He actively collaborates in international epidemiological programs and serves on the Presidium of the Russian Society of Pathologists. With Scopus h-index 25, Web of Science h-index 4, and more than 55 citations, Dr. Sychugov continues to advance diagnostic pathology through evidence-based research, mentorship, and innovation, embodying excellence in translational medical science.

Profiles : Orcid | Scopus

Featured Publications

Gogoleva, D. V., & Sychugov, G. V. (2023). Immunohistochemistry of bone marrow extracellular matrix in Ph-negative myeloproliferative diseases. Ural Medical Journal.

Shamanova, A. Yu., Rostovtsev, D. M., Privalov, A. V., Yarina, L. V., Aristarkhova, K. S., Sychugov, G. V., Vasil’kova, I. V., Artem’eva, A. S., Saevets, V. V., & Alymov, E. A. (2023). Undifferentiated pancreatic carcinoma with osteoclast-like giant cells. Russian Journal of Archive of Pathology.

Sychugov, G. V., Kazachkov, E. L., Osovets, S. V., Grigoryeva, E. S., Sychugov, A. G., & Azizova, T. V. (2022). Leukemia inhibitory factor and cellular renewal in various types of pulmonary fibrosis in plutonium production workers. Biology Bulletin.

Zhuntova, G. V., Azizova, T. V., Bannikova, M. V., & Sychugov, G. V. (2022). Characteristics of malignant neoplasms of the hepatobiliary system in the cohort of occupationally-exposed workers. Ural Medical Journal.

Gogoleva, D., & Sychugov, G. (2021). Matrix metalloproteinases and their inhibitors in JAK2-mutated myeloproliferative neoplasms. Virchows Archiv. WOSUID: WOS:000692384500404

Hamed Etezadi | Quantitative Research | Best Researcher Award

Mr. Hamed Etezadi | Quantitative Research | Best Researcher Award

McGill University | Canada

Dr. Hamed Etezadi is a research-focused scholar in Bioresource Engineering with a Ph.D. from McGill University, Canada, where his thesis focused on developing decision support infrastructure for sustainable crop production under the supervision of Prof. Viacheslav Adamchuk. He holds an M.S. in Biosystems and Agricultural Engineering from the University of Tehran, Iran, and a B.E. in Agricultural Engineering from Urmia University, Iran. With over 15 years of academic and professional experience, Dr. Etezadi has contributed significantly to precision agriculture, data-driven modeling, remote sensing, machine learning, and autonomous agricultural systems. He has published 6 peer-reviewed journal articles, including Q1 and Q2 journals, and presented at leading conferences such as ASABE and IEEE/CS Robotics and Mechatronics, His research explores soil variability, aerial pollination systems, and predictive modeling for sustainable agriculture. He has received multiple honors, including the FRQNT Scholarship, Grad Excellence Award, and global recognition on Kaggle Notebooks. Dr. Etezadi combines academic teaching, research leadership, and industry experience, including directing growth and innovation in agri-tech platforms, fostering data-driven decision-making, and advancing sustainable farming technologies globally.

Profile : Orcid 

Featured Publications

Etezadi, H., Adamchuk, V., Bouroubi, Y., Leduc, M., Gasser, M.-O., & Titley-Peloquin, D. (2025). “Quantifying intra-field soil variability using categorical data: A case study of predicting soil organic matter using soil survey maps.”

Etezadi, H., & Eshkabilov, S. (2024). “A comprehensive overview of control algorithms, sensors, actuators, and communication tools of autonomous all-terrain vehicles in agriculture.”

Mazinani, M., Zarafshan, P., Dehghani, M., Vahdati, K., & Etezadi, H. (2023). “Design and analysis of an aerial pollination system for walnut trees.”

Zarafshan, P., Etezadi, H., Javadi, S., Roozbahani, A., Hashemy, S. M., & Zarafshan, P. (2023). “Comparison of machine learning models for predicting groundwater level, case study: Najafabad region.”

Salari, K., Zarafshan, P., Khashehchi, M., Chegini, G., Etezadi, H., Karami, H., Szulżyk-Cieplak, J., & Łagód, G. (2022). “Knowledge and technology used in capacitive deionization of water.”

Alexia Iasonos | Algorithm Development | Best Researcher Award

Dr. Alexia Iasonos | Algorithm Development | Best Researcher Award

Memorial Sloan-Kettering Cancer Center | United States

Dr. Alexia E. Iasonos is a recognized expert in biostatistics, currently a Member at Memorial Sloan Kettering Cancer Center where she serves as Director of Clinical Research Development. She earned her B.S. in Mathematics & Statistics from the University of Cyprus (1992-96), followed by an M.S. in Biometry & Statistics and a Ph.D. in Biometry & Statistics from SUNY Albany (awarded in 1998 and 2002 respectively). Her professional trajectory includes roles in academic statistics, pharmaceutical industry, and extensive hospital-affiliated clinical trial biostatistics. Her methodological research focuses on design and analysis of clinical trials, especially Bayesian adaptive Phase I and II designs, dose-finding methods, dose-expansion cohort development, predictive modeling and nomograms, biomarker evaluation and endpoints, particularly in gynecologic oncology. Her work has been instrumental in improving early drug development processes. According to AD Scientific Index, her h-index is 52, with over 10,779 total citations. She is an elected Fellow of the Society of Clinical Trials, deputy editor of Journal of Clinical Oncology, member/chair in several protocol and monitoring committees. Among her awards are those tied to leadership in early-phase trial methodology and contributions to cancer biostatistics. In conclusion, Dr. Iasonos continues to make high impact contributions in biostatistics and oncology by combining rigorous methodology with translational collaboration.

Profile : Scopus

Featured Publications

Iasonos, A. (2024). Evaluation of early phase dose finding algorithms in heterogeneous populations. Journal of Statistical Planning and Inference.

Iasonos, A. (2023). Controlled amplification in oncology dose-finding trials. Contemporary Clinical Trials.

Iasonos, A. (2022). Stopping rules for phase I clinical trials with dose expansion cohorts. Statistical Methods in Medical Research.

Iasonos, A. (2021). Controlled backfill in oncology dose-finding trials. Contemporary Clinical Trials.

Iasonos, A. (2021). Phase I clinical trials in adoptive T-cell therapies. Journal of the Royal Statistical Society: Series C (Applied Statistics).

Elena Stamate | Machine Learning and AI Applications | Best Researcher Award

Dr. Elena Stamate | Machine Learning and AI Applications | Best Researcher Award

Faculty of Medicine and Pharmacy, University Dunarea de Jos of Galati | Romania

Dr. Stamate Elena is an accomplished Romanian cardiologist, educator, and researcher whose career bridges the domains of clinical cardiology, academic teaching, and medical innovation. Serving as both an Assistant Lecturer and a practicing Cardiology Resident Doctor, she is recognized for her contributions to advancing diagnostic and therapeutic strategies in cardiovascular medicine. Her work, particularly in cardiogenic shock prediction and the integration of artificial intelligence in cardiology, has earned her a respected position in Romania’s medical and academic community. She is an active member of the Romanian College of Physicians and the Romanian Society of Cardiology, actively engaging in scientific discourse at both national and international levels.

Professional Profile

ORCID

Education

Dr. Stamate earned her Bachelor’s degree in Medicine from the Faculty of Medicine and Pharmacy at “Dunărea de Jos” University of Galați. Her academic training provided a solid foundation in general medicine and clinical sciences, which she has since built upon through advanced research in cardiology, physiology, and multidisciplinary approaches to complex cardiac conditions. As a PhD student, she continues to deepen her expertise, focusing on predictive modeling, cardiovascular pathophysiology, and AI-assisted diagnostics.

Experience

Professionally, Dr. Stamate has held dual roles that complement each other—Assistant Lecturer at the Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galați, and Cardiology Resident Doctor at University Emergency Hospital Bucharest. In her academic role, she is dedicated to teaching cardiology and physiology to medical students, coordinating scientific research activities, and mentoring future physicians. In the clinical setting, she provides specialized care for patients with acute and chronic cardiovascular diseases, often managing complex and high-risk cases. Her experience spans both hospital-based interventions and academic research, enabling her to integrate evidence-based medicine into real-world practice.

Research Interest

Dr. Stamate’s research interests lie at the intersection of cardiology, artificial intelligence, and predictive modeling. She has extensively studied cardiogenic shock in ST-elevation myocardial infarction (STEMI) patients, focusing on early detection and risk stratification using AI-based predictive models. Her work also explores big data applications in cardiology, personalized treatment approaches, and the role of multidisciplinary collaboration in managing complex cardiac cases. Additionally, she has contributed to research on infective endocarditis, myocardial infarction, intracardiac hemodynamics, and AI applications in forensic medicine, reflecting her versatility and interdisciplinary mindset.

Awards and Recognition

Dr. Stamate has been honored for her academic and research excellence at multiple scientific conferences. She has received awards for best presentations, including first prize at the Galmed Congress for her case-based study on myocardial infarction, as well as recognition for her contributions to doctoral research conferences. Her publications in high-impact, Q1-ranked journals such as Journal of Clinical Medicine, Diagnostics, and Antibiotics further underscore her influence in the field. She has been invited as a lecturer at numerous cardiology conferences and interdisciplinary medical events, sharing her expertise on topics ranging from anticoagulation in pregnancy to updates in cardiogenic shock management.

Publications

Stamate, E., Culea-Florescu, A.-L., Miron, M., Piraianu, A.-I., Dumitrascu, A.G., Fulga, I., Fulga, A., Patrascanu, O.S., Iancu, D., Ciobotaru, O.C., et al. (2025). “Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases” in Journal of Clinical Medicine .

Stamate, E., Culea-Florescu, A.-L., Miron, M., Piraianu, A.-I., Dumitrascu, A.G., Fulga, I., Fulga, A., Patrascanu, O.S., Iancu, D., Ciobotaru, O.C., et al. (2025). “AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights” in Journal of Clinical Medicine.

Iancu, D., Fulga, A., Vesa, D., Fulga, I., Tutunaru, D., Zenovia, A., Piraianu, A.-I., Stamate, E., Sterian, C., Dimofte, F., et al. (2025). “Immunosuppression and Outcomes in Patients with Cutaneous Squamous Cell Carcinoma of the Head and Neck” in Clinics and Practice.

Ciobotaru, O.C., Duca, O.-M., Ciobotaru, O.R., Stamate, E., Piraianu, A.-I., Dumitrascu, A.G., Constantin, G.B., Matei, M.N., Voinescu, D.C., Luchian, S.-A. (2024). “Hydatid Cysts of the Psoas Muscle: Insights from the Past Five Years” in Life (Cited by 1).

Copciag, R., Bratu, V., Rimbas, R., Stamate, E., Lixandru, T., Corlan, A., Vinereanu, D. (2025). “Comparative Reproducibility of Myocardial Work and Left Ventricular Ejection Fraction in Patients After an Acute Coronary Syndrome” in Preprint .

Ciobotaru, O.C., Stamate, E., Stoleriu, G., Duca, O.M., Piraianu, A.-I., Ciobotaru, O.R. (2024). “The Early Stage Adenocarcinoma of the Gallbladder Incidentally Diagnosed” in Romanian Journal of Oral Rehabilitation.

Conclusion

Through her integrated roles as a physician, educator, and researcher, Dr. Stamate Elena exemplifies the modern clinician-scientist who bridges patient care with academic excellence and technological innovation. Her contributions to predictive cardiology, particularly the application of AI and big data in early diagnosis and personalized therapy, position her as a forward-thinking leader in her field. With her commitment to advancing cardiovascular medicine, fostering medical education, and embracing multidisciplinary collaboration, she continues to shape the future of cardiology in Romania and beyond.

Mohammad Zahid | Cybersecurity Data Analysis | Young Scientist Award

Mr. Mohammad Zahid | Cybersecurity Data Analysis | Young Scientist Award

Jamia Millia Islamia | India

Author Profile

Google Scholar

Mr. Mohammad Zahid

Junior Research Fellow | Ph.D. Scholar in Computer Science | Cybersecurity & AI Researcher

Summary

Mohammad Zahid is a motivated and research-focused computer science professional currently pursuing a Ph.D. in Computer Science at Jamia Millia Islamia, New Delhi. He is working as a Junior Research Fellow (JRF) with a specialization in IoT security, machine learning, deep learning, and federated learning. With strong analytical and technical skills, he is actively engaged in AI-driven cybersecurity research, aiming to develop intelligent systems for threat detection. Zahid is passionate about contributing to innovative and interdisciplinary projects in both academia and the tech industry.

Education

Mr. Zahid is currently enrolled in a Ph.D. program at Jamia Millia Islamia (Central University), New Delhi, with a research focus in computer science and AI. He holds a Master of Computer Applications (MCA) from Maulana Azad National Urdu University (MANUU), Hyderabad, where he graduated with an excellent academic record (87.6%). He also completed a Bachelor of Science (Hons) in Chemistry, Mathematics, and Physics from Aligarh Muslim University (AMU), Aligarh.

Professional Experience

As a Junior Research Fellow, Mr. Zahid is engaged in cutting-edge research related to cybersecurity and artificial intelligence. During his postgraduate studies, he developed a web-based fraud detection system that utilized machine learning algorithms to identify and visualize suspicious banking transactions. His hands-on experience includes technologies such as Java, MySQL, JavaScript, and HTML/CSS, with a strong foundation in data preprocessing and anomaly detection.

Research Interests

Mr. Zahid’s research interests span across IoT security, federated learning, machine learning, and deep learning. His current Ph.D. research is centered on AI-based cybersecurity frameworks, especially for detecting and mitigating threats in decentralized and IoT-based environments. He is particularly interested in applying federated learning models to preserve data privacy while improving detection efficiency in real-world systems.

Honors and Awards

Mr. Zahid was awarded the UGC Junior Research Fellowship (JRF) in Computer Science by the National Testing Agency (NTA) in March 2022, recognizing him among the top national-level researchers eligible for academic and research roles across Indian universities. He also received the Merit-cum-Means Scholarship for Professional Courses from the Ministry of Minority Affairs, Government of India, during his MCA studies (2018–2021), in acknowledgment of his academic performance and financial need.

Publications

 Empowering IoT Networks

Author: M Zahid, TS Bharati

Journal: Artificial Intelligence for Blockchain and Cybersecurity Powered IoT
Year: 2025

Enhancing Cybersecurity in IoT Systems: A Hybrid Deep Learning Approach for Real-Time Attack Detection

Author: M Zahid, TS Bharati
Journal: Discover Internet of Things, Volume 5(1), Page 73
Year: 2025

 Comprehensive Review of IoT Attack Detection Using Machine Learning and Deep Learning Techniques

Author: M Zahid, TS Bharati
Journal: 2024 Second International Conference on Advanced Computing & Communication
Year: 2024

Empowering IoT Networks: A Study on Machine Learning and Deep Learning for DDoS Attack

Author: M Zahid, TS Bharati
Journal: Artificial Intelligence for Blockchain and Cybersecurity Powered IoT
Year: 2025