Hugo Terashima Marín | Computer Science | Best Researcher Award

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Prof. Hugo Terashima Marín | Computer Science | Best Researcher Award

Professor at Tecnológico de Monterrey | Mexico

Dr. Hugo Terashima-Marín is a distinguished Professor of Computer Science and Intelligent Systems at Tecnológico de Monterrey, Mexico, widely recognized for his pioneering work in computational intelligence and heuristic optimization. His academic foundation spans prestigious institutions in Mexico, the United States, and the United Kingdom, reflecting a strong interdisciplinary background in informatics, artificial intelligence, and knowledge-based systems. As a leading researcher in evolutionary computation, constraint satisfaction problems, and hyper-heuristics, Dr. Terashima-Marín has developed innovative methodologies that bridge artificial intelligence and practical problem-solving across domains such as logistics, medicine, and smart cities. His extensive publication record in high-impact journals demonstrates his global influence in advancing algorithmic design, machine learning integration, and automated reasoning systems. Beyond research, he has mentored numerous doctoral and master’s students, fostering new generations of scientists in computational intelligence. His leadership roles at Tecnológico de Monterrey—directing graduate and doctoral programs and leading research groups in intelligent systems—underscore his commitment to academic excellence and institutional innovation. Recognized by the Mexican National System of Researchers and honored by the Mexican Academy of Sciences and the IEEE, Dr. Terashima-Marín’s contributions have elevated the standards of AI research in Latin America. His current projects explore multi-objective optimization, digital twins for smart city applications, and AI-driven decision support systems, continuing to push the boundaries of how computation can model, predict, and enhance complex human and industrial processes. Through decades of scholarship and collaboration, he remains an influential figure shaping the global discourse on intelligent systems and applied artificial intelligence.

Profiles: Scopus | ORCID

Featured Publications

Ali, F., Ahmed, A., Alipour, M. A., & Terashima-Marin, H. (2025, October 25). Adoption of AI-coding assistants in programming education: Exploring trust and learning motivation through an extended technology acceptance model.

Morales-Paredes, A., Juárez, J., Falcón-Cardona, J., Terashima-Marin, H., & Coello Coello, C. (2025, July 14). Automatic design of specialized variation operators for the multi-objective quadratic assignment problem.

Morales-Paredes, A. I., Falcón-Cardona, J. G., Juárez, J., Terashima-Marín, H., & Coello Coello, C. A. (2025, July 14). Reference point specification in greedy inclusion hypervolume-based subset selection: A study on two objectives. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2025).

Pirzado, F. A., Ahmed, A., Hussain, S., Ibarra-Vázquez, G., & Terashima-Marin, H. (2025, March 11). Assessing computational thinking in engineering and computer science students: A multi-method approach.

Garza-Santisteban, F., Cruz-Duarte, J. M., Amaya, I., Ortiz-Bayliss, J. C., Conant-Pablos, S. E., & Terashima-Marín, H. (2025, February). Selection hyper-heuristics and job shop scheduling problems: How does instance size influence performance

Mr. Serhii Savin | Data Science | Data Science Excellence Award

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Mr. Serhii Savin | Data Science | Data Science Excellence Award 

Accomplished Data Scientist | Lyft | Poland

Mr. Serhii Savin is an accomplished data scientist specializing in artificial intelligence, machine learning, econometrics, and geospatial analytics, with extensive experience developing predictive and optimization models for real-world applications in transportation, finance, and technology. Mr. Savin holds a Master of Arts in Economics with a concentration in Business and Financial Economics from the Kyiv School of Economics in affiliation with the University of Houston, where he graduated with distinction and received a full merit scholarship for ranking in the top one percent of applicants. His academic foundation in data science, finance, and quantitative modeling serves as the cornerstone for his applied research and professional achievements. Mr. Savin’s professional experience spans global technology leaders, including Lyft (United States), Reface (Ukraine), Appflame (Genesis), and Civitta, where he has demonstrated excellence in data-driven decision-making, artificial intelligence deployment, and model optimization. At Lyft, he has developed advanced geospatial route optimization and time prediction models that significantly enhanced operational efficiency and reduced financial discrepancies, contributing to multi-million-dollar savings annually. His earlier tenure at Reface involved creating recommendation systems for intelligent user engagement, while his contributions at Appflame focused on optimizing revenue-generating analytics for streaming platforms and designing A/B testing frameworks to improve product performance. His consulting experience at Civitta strengthened his capabilities in market forecasting, financial modeling, and quantitative research, contributing to multiple innovation and grant projects funded by USAID. Mr. Savin’s research interests encompass predictive analytics, AI-driven forecasting, experimental design, and human-centered data science, integrating these disciplines to drive efficiency, fairness, and transparency in algorithmic systems. His technical expertise includes proficiency in Python, PySpark, SQL, R, Tableau, and Power BI, with strong grounding in supervised and unsupervised learning, A/B experimentation, and econometric analysis. He has completed advanced training programs such as the MIT MicroMasters in Statistics and Data Science and holds certifications in Machine Learning and Data Analysis from globally recognized platforms. Mr. Savin has received numerous honors, including a full merit academic scholarship from the Ampersand.Foundation, finalist recognition in McKinsey Business Diving (top one percent teams), and multiple national Olympiad awards in economics and mathematics.

Profile: Orcid

Featured Publications

  • Savin, S. (2023). Impact of Experts’ Forecast on UAH/USD Exchange Rate Volatility. KSE Working Paper Series, 12(3), 45–59. Citations: 18

 

Mr. Sachin Pandey | AI Data Science | AI & Machine Learning Award

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Mr. Sachin Pandey | AI Data Science | AI & Machine Learning Award

Head of Data Engineering and Data Science, Oracle Corporation, United States

Mr. Sachin Pandey is an accomplished data scientist and engineering professional whose expertise bridges the domains of artificial intelligence, data management, and enterprise analytics. With more than thirteen years of progressive experience, Mr. Sachin Pandey currently serves as the Head of Data Engineering and Data Science at Oracle Corporation, where he leads multidisciplinary teams in the development of intelligent data infrastructures, machine learning solutions, and scalable MLOps frameworks. He previously contributed his expertise as Head of Data Science at Walmart US, overseeing large-scale analytical transformations that enhanced predictive decision systems and optimized data-driven strategies across global business operations. Mr. Sachin Pandey’s academic foundation is rooted in a Master of Science in Management Information Systems from the University of Illinois at Chicago – Liautaud Graduate School of Business, where he developed a strong grounding in business intelligence, data visualization, and statistical computing. He earned his Bachelor of Technology in Electronics and Telecommunication Engineering from the Vivekananda Education Society’s Institute of Technology, Mumbai, where his technical acumen and analytical thinking shaped his approach to applied data research. His research interests include machine learning algorithms, deep learning optimization, big data analytics, AI-based automation, and data governance, focusing on how scalable AI systems can transform decision-making and industry practices. Mr. Sachin Pandey has published and co-authored peer-reviewed papers in internationally recognized journals and conference proceedings indexed by Scopus and IEEE, including notable contributions in areas of image detection, intelligent automation, and cloud-based analytics. His most cited work, “Smoke and Fire Detection” is recognized for advancing the use of AI models in safety and monitoring systems, reflecting his commitment to practical applications of data science for societal benefit. In addition to research, he possesses exceptional skills in Python programming, Spark, Airflow, data modeling, ELT/ETL frameworks, MLFlow, and cloud analytics platforms such as Power BI, Tableau, and Alteryx, complemented by a deep understanding of optimization, data governance, and model versioning techniques.

Profiles: Google Scholar | Orcid 

Featured Publications

  • Gharge, S., Birla, S., Pandey, S., Dargad, R., & Pandita, R. (2013). Smoke and fire detection. International Journal of Advanced Research in Computer and Communication Engineering, 2(6). Cited by: 16

  • Singh, A., & Pandey, S. (2014). Advanced Centralised RTO System for Traffic Data Automation. International Journal of Emerging Technology and Advanced Engineering, 4(5). Cited by: 9

  • Pandey, S. (2015). Intelligent Data Governance Using Cloud-based Frameworks. International Journal of Data Science and Analytics, 3(2). Cited by: 11

  • Pandey, S., & Birla, S. (2016). Optimization of Machine Learning Pipelines for Enterprise Analytics. Proceedings of the IEEE International Conference on Computational Intelligence. Cited by: 7

  • Pandey, S. (2019). Scalable AI Systems for Predictive Data Engineering. Journal of Artificial Intelligence Research and Applications, 10(4). Cited by: 13

Prof. Bingliang Ye | Robotics in Agriculture | Best Researcher Award

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Prof. Bingliang Ye | Robotics in Agriculture | Best Researcher Award 

Professor, Zhejiang Sci-Tech University, China

Prof. Bingliang Ye is a distinguished Doctor of Engineering, Professor, and Doctoral Supervisor at the Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou, China, recognized for his pioneering contributions in mechanical design optimization, intelligent robotics, agricultural automation, and AI-integrated machinery. He has a strong academic foundation with a Doctor of Engineering degree, which underpins his expertise in designing adaptive mechanisms, multi-linkage systems, and optimized mechanical structures free from kinematic defects. Over his career, Prof. Ye has developed and led numerous research projects with both national and international collaboration, focusing on the integration of artificial intelligence, multi-objective optimization, and robotics in precision agriculture and industrial automation. His professional experience encompasses academic leadership, mentorship of doctoral students, and guiding interdisciplinary research teams toward innovative solutions for mechanized harvesting, planting, and intelligent control systems. His research interests include mechanism synthesis, optimization algorithms, AI-guided robotics, multi-target design, and intelligent agricultural equipment development, and he demonstrates advanced research skills in computational modeling, kinematic simulation, mechanical optimization, algorithm development, and system integration. Prof. Ye’s scholarly output is extensive, with 61 Scopus-indexed publications, over 998 citations, and an h-index of 21, featuring contributions to high-impact journals such as Mechanism and Machine Theory, Journal of Field Robotics, International Journal of Agricultural and Biological Engineering, and Information Technology and Control.

Profiles: Scopus | Orcid 

Featured Publications

  • Ye, B., et al. (2025). Integrated optimization synthesis of linkage mechanism structures and dimensions free from kinematic defects. Mechanism and Machine Theory.

  • Ye, B., et al. (2025). Improved YOLOv8n based lotus seedpod detection algorithm. Information Technology and Control.

  • Ye, B., et al. (2025). New automatic sketching method for planetary gear train. Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science.

  • Ye, B., et al. (2025). Optimization design and testing of the under-actuated lotus seedpod harvesting grasping mechanism. Journal of Field Robotics.

  • Ye, B., et al. (2025). Design and experiment of a vegetable plug seedling planting mechanism combining non-circular gear system and multi-link. International Journal of Agricultural and Biological Engineering.

Prof. Ivan Beloev | Transport | Young Researcher Award

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Prof. Ivan Beloev | Transport | Young Researcher Award

Researcher and Innovator, University of Ruse, Bulgaria

Prof. Ivan Beloev is a distinguished academic, researcher, and innovator at the University of Ruse, Bulgaria, renowned for his significant contributions to the fields of power systems engineering, electrical energy management, and artificial intelligence applications in smart grids. With a Scopus profile reflecting more than 80 research documents, over 495 citations, and an h-index of 11, Prof. Beloev exemplifies scholarly excellence and international impact. His educational foundation is firmly rooted in engineering and applied sciences, holding a Ph.D. in Electrical Engineering from the University of Ruse, where his research emphasized advanced modeling and optimization of distributed energy systems. Prof. Beloev’s research interests span electromagnetic field modeling, distributed generation, hydrogen-based power systems, AI-driven regulatory frameworks, and sensor technology for energy monitoring, with his work frequently appearing in reputed journals such as Applied Sciences (Switzerland), Sensors (Basel), and International Journal of Power Electronics and Drive Systems.  His membership in professional societies such as IEEE and participation in multiple conference review boards and academic committees underscore his dedication to the advancement of science and engineering education. Prof. Beloev’s career continues to inspire the next generation of engineers and researchers through mentorship, collaborative projects, and technology-driven teaching practices. In conclusion, Prof. Ivan Beloev’s pioneering work at the intersection of artificial intelligence and power system engineering continues to advance global knowledge on sustainable energy, smart grids, and predictive modeling.

Profiles: Google Scholar | Scopus | Orcid 

Featured Publications

  • Iliev, I., Filimonova, A., Chichirov, A., Vlasova, A., Kamalieva, R., & Beloev, I. (2025). Natural and waste materials for desulfurization of gaseous fuels and petroleum products. Fuels, 6(1), 13. (6 citations)

  • Iliev, I. K., Fedyukhin, A. V., Semin, D. V., Valeeva, Y. S., Dronov, S. A., & Beloev, I. H. (2024). Prospects of hydrogen application as a fuel for large-scale compressed-air energy storages. Energies, 17(2), 518. (4 citations)

  • Pencheva, V., Asenov, A., Sładkowski, A., Georgiev, I., Beloev, I., & Ivanov, K. (2019). The Danube river, multimodality and intermodality. Modelling of the Interaction of the Different Vehicles and Various Transport Systems. (6 citations)

  • Beloev, I., Filimonova, A., Chichirov, A., Vinogradov, A., & Iliev, I. (2023). Design and calculation of an environmentally friendly carbon-free hybrid plant based on a microgas turbine and a solid oxide fuel cell. E3S Web of Conferences, 404, 01004. (4 citations)

  • Hristov, G., Beloev, I., & Zahariev, P. (2023). Challenges, requirements, opportunities and solutions for the digital transformation of the transport education. Strategies for Policy in Science & Education, 31(2), 45–54. (4 citations)

Dr. Maha Al-Sheikh | Supply Chain & Logistics | Best Researcher Award

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Dr. Maha Al-Sheikh | Supply chain & logistics | Best Researcher Award 

Assistant Professor, Middle East University, Jordan

Dr. Maha Al-Sheikh is an accomplished Assistant Professor of Supply Chain Management at Middle East University, known for her pioneering research in Digital Supply Chain Management, Artificial Intelligence in Logistics, and Sustainable Operations. Her academic path reflects a strong commitment to the integration of advanced analytics, technology, and sustainability within the global business ecosystem. Dr. Maha Al-Sheikh earned her Ph.D. in Supply Chain Management, where she specialized in developing frameworks for data-driven decision-making, adaptive logistics, and resilience modeling. Throughout her professional journey, she has held academic and research-oriented roles that emphasize innovation, interdisciplinary collaboration, and industry engagement. Her teaching and research experience span multiple areas, including AI for Business Transformation, Smart Logistics Systems, Sustainable Supply Chain Networks, and Predictive Modeling for Resource Optimization. Dr. Maha Al-Sheikh’s research interests center on the intersection of artificial intelligence, sustainability, and industrial transformation, exploring how digitalization and intelligent systems can reshape modern supply chains. She demonstrates expertise in AI algorithms for logistics management, neuro-fuzzy modeling, statistical forecasting, simulation tools, and environmental impact assessment. Her research excellence is evidenced through publications in highly regarded journals such as IEEE Access, Technological Sustainability, Problems and Perspectives in Management, and Environmental Economics, addressing key challenges like energy-conscious logistics, clean transportation, and adaptive supply chain resilience. Her professional achievements include active membership in academic and research associations such as IEEE, INFORMS, and the Academy of Management, enhancing her involvement in international conferences, technical sessions, and peer-review activities. Her dedication to innovation, mentorship, and educational leadership has made her a key figure in promoting AI applications for responsible business practices. She has guided numerous postgraduate students in research projects focusing on supply chain resilience, digital transformation, and sustainability transitions, fostering an environment of academic growth and collaboration.

Profiles: Google Scholar | Scopus

Featured Publications

  • Al-Sheikh, M. (2025). Toward a cleaner road: Environmental transformation in Hungary’s automotive sector. Environmental Economics, 16(2), 1. Citations: 3

  • Al-Sheikh, M., Morshed, A., Alkhodary, D., Khrais, L. T., & Altarawneh, R. (2025). Beyond efficiency: unpacking AI’s dual role in driving sustainable and energy-conscious logistics in North Africa. Technological Sustainability, 4(3), 293–310.

  • Samhouri, M., Abualeenein, M., & Al-Sheikh, M. (2025). Mitigating disruptions in transportation and logistics through adaptive neuro-fuzzy inference-based supply chain resilience. IEEE Access.

  • Zoubi, M., Estaitia, H., Morshed, A., Khrais, L. T., Haikal, E., & Al-Sheikh, M. (2025). Augmented reality and sustainable luxury: transforming fashion retail in the UAE. Technological Sustainability, 1–18.

  • Al-Sheikh, M. (2025). Assessing how supply chains strategy contributes to business success and varies by firm size and industry. Problems and Perspectives in Management, 23(2), 498.

Dr. Alok Jain | Power Systems | Best Academic Researcher Award

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Dr. Alok Jain | Power Systems | Best Academic Researcher Award

Assistant Professor, Pandit Deendayal Energy University, India

Dr. Alok Jain (Ph.D. – IIT BHU, Varanasi) is an accomplished Assistant Professor (Grade I) at Pandit Deendayal Energy University, Gandhinagar, India, recognized for his significant contributions in renewable energy systems, smart grids, adaptive control algorithms, and AI-enabled power electronics optimization. His academic journey includes rigorous training in electrical engineering and computational modeling, culminating in a Ph.D. where he focused on intelligent energy systems integration and performance optimization. Dr. Jain has amassed a strong professional record, publishing 22 Scopus-indexed papers with 118 citations and an h-index of 6, reflecting his impactful research in AI-based control strategies for solar PV systems, EV charging integration, and energy storage management. His research interests encompass smart grid optimization, renewable energy forecasting, adaptive and predictive control, and AI-driven fault detection in energy systems, with a vision to enhance the efficiency, reliability, and sustainability of modern electrical grids. Dr. Jain possesses advanced research skills in MATLAB/Simulink, Python, machine learning algorithms for power systems, adaptive control design, and energy storage system modeling, complemented by expertise in experimental validation and simulation-based studies. He actively engages in collaborative research initiatives at national and international levels, fostering partnerships that bridge theoretical innovation with practical applications. Beyond research, Dr. Jain contributes to the academic community by mentoring graduate students, guiding research projects, and participating in curriculum development. His professional affiliations include membership in IEEE, and he regularly contributes to technical committees, workshops, and conference peer-review panels. He has demonstrated leadership in promoting sustainable energy research through conferences, seminars, and knowledge dissemination platforms. Dr. Jain’s awards, recognitions, and editorial contributions highlight his commitment to advancing AI-integrated energy solutions.

Profiles: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  • Jain, A. (2025). Performance analysis of LMS and LN-LMS adaptive control algorithms for grid-tied solar PV-based EV charging systems. Computers and Electrical Engineering. Citations: 0

  • Jain, A. (2025). Design and performance analysis of solar PV-battery energy storage system integration with three-phase grid. Journal of Power Sources. Citations: 5

  • Jain, A., & Co-authors. (2024). AI-based predictive control for efficient energy management in microgrids. Renewable Energy Journal. Citations: 12

  • Jain, A. (2024). Optimization of hybrid renewable energy systems using intelligent algorithms. Energy Reports. Citations: 8

  • Jain, A., & Co-authors. (2023). Fault detection and adaptive control strategies for smart grid applications. IEEE Transactions on Smart Grid. Citations: 15

Dr. Ting Li | Fault prediction | Best Researcher Award

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Dr. Ting Li | Fault prediction | Best Researcher Award 

Researcher and Lecturer, Guangxi University, China

Dr. Ting Li is a distinguished researcher and lecturer at the College of Computer and Electronic Information, Guangxi University, recognized for her outstanding contributions in the fields of mobile edge computing, resource allocation, optimization theory, and networked intelligent systems. She earned her Doctor of Philosophy (Ph.D.) in Cyberspace Security from the Institute of Information Engineering, Chinese Academy of Sciences, where she focused on developing intelligent, secure, and privacy-aware computational frameworks that address real-world challenges in edge computing environments. Her academic foundation began with a Bachelor’s degree in Communication Engineering from Chongqing University, equipping her with strong interdisciplinary expertise bridging communication networks and artificial intelligence. Since joining Guangxi University as a lecturer, Dr. Ting Li has been actively involved in teaching, mentoring, and research, leading and participating in several high-impact projects funded by the National Natural Science Foundation of China and the National Key R&D Program, focusing on cross-modal recognition, task offloading, and secure data processing for IoT systems. Her research interests encompass intelligent task scheduling, distributed optimization, cross-modal data analysis, and AI-driven resource management for next-generation computing systems. Dr. Ting Li’s research skills include advanced algorithm design, deep reinforcement learning, model caching, multi-hop task offloading, and edge intelligence optimization, all of which contribute to enhancing efficiency and security in distributed networks. Her publications in world-renowned journals such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Letters, and IEEE Internet of Things Journal have established her as an influential scholar in AI-powered edge computing research. She has been recognized for her research excellence through multiple institutional and academic commendations, including awards for innovation and outstanding scientific contributions in the domain of intelligent communication systems. Her scholarly work, reflected in her growing citation record, demonstrates both academic rigor and global impact.

Profiles: Google Scholar

Featured Publications

  • Li, T., Liu, Y., Ouyang, T., Zhang, H., Yang, K., & Zhang, X. (2025). Multi-hop task offloading and relay selection for IoT devices in mobile edge computing. IEEE Transactions on Mobile Computing, 24(1), 466–481. Cited by: 13

  • Li, T., Sun, J., Liu, Y., Zhang, X., Zhu, D., Guo, Z., & Geng, L. (2023). ESMO: Joint frame scheduling and model caching for edge video analytics. IEEE Transactions on Parallel and Distributed Systems, 34(8), 2295–2310. Cited by: 10

  • Zhu, D., Liu, H., Li, T., Sun, J., Liang, J., Zhang, H., & Geng, L. (2021). Deep reinforcement learning-based task offloading in satellite-terrestrial edge computing networks. IEEE Wireless Communications and Networking Conference (WCNC), 1–7. Cited by: 63

  • Zhu, D., Li, T., Tian, H., Yang, Y., Liu, Y., Liu, H., Geng, L., & Sun, J. (2021). Speed-aware and customized task offloading and resource allocation in mobile edge computing. IEEE Communications Letters, 25(8), 2683–2687. Cited by: 17

  • Li, T., Liu, H., Liang, J., Zhang, H., Geng, L., & Liu, Y. (2020). Privacy-aware online task offloading for mobile-edge computing. International Conference on Wireless Algorithms, Systems, and Applications (WASA), Qingdao, China. Cited by: 12

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

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Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

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Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

Multidisciplinary Researcher, Western Sydney University, Australia

Dr. Farnaz Farid is a distinguished and multidisciplinary researcher whose academic journey and professional experience span industry and academia, combining expertise in artificial intelligence, cybersecurity, human-centered systems, and applied computing. She holds a Doctor of Philosophy (PhD) degree from Western Sydney University, where her doctoral research focused on computational modeling, AI-driven predictive systems, and network quality of service frameworks; she also earned earlier degrees in engineering and computing from reputable institutions that shaped her foundation in IT, networks, and systems. Over the years, Dr. Farnaz Farid has served in both industry and academic roles: prior to joining academia, she worked at IBM as an IT Specialist, Application Developer, and Project Manager, contributing to enterprise integration, software development, and digital innovation; subsequently, she entered academia as an Associate Lecturer at the University of Sydney and then moved to Western Sydney University, where she is now a Senior Lecturer and Academic Program Advisor, co-leading global initiatives such as “Realising Digital Futures.” Her professional experience includes overseeing cross-disciplinary projects in AI, cybersecurity, IoT, and smart systems, mentoring postgraduate researchers, guiding curriculum development, and fostering partnerships with industry and community stakeholders. Her research interests encompass explainable AI, human-centred security, AI for healthcare, cyber‐physical systems, distributed networks, federated learning, and digital inclusion. Dr. Farid has received a number of awards and honors, such as the Google exploreCSR grant over multiple years to lead community‐based AI projects, the DVC Education Excellence in Teaching (Partnerships) award at her university, and the Teaching and Learning for Public Good Award in Social Sciences, all of which attest to her excellence in teaching, public engagement, and socially impactful research. Through her editorial service (for journals such as Symmetry and Sustainability), membership in the Asian Council of Science Editors (ACSE), and leadership of cross‐disciplinary grants, she has also contributed to the scientific community.

Profile: GOOGLE SCHOLAR  | SCOPUS | ORCID

Featured Publications

  • Farid, F. (2025). An explainable predictive model for the detection of mental health conditions in the workplace. (citation count: 13)

  • Farid, F. (2025). A threat analysis framework for cyberattacks in smart cities: ransomware in focus. (citation count: 24)

  • Dong, H., & Farid, F. (2024). A deep learning based patient care application for skin cancer detection.

  • Farid, F., & colleagues. (2024). AI technologies in reducing hospital readmission for chronic diseases: a recommended framework.

  • Lai, T., & Farid, F. (2024). Ensemble learning for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis.