2nd International Conference on Cognitive Computing, Intelligence, and Data Science Applications

Michael Beck (Dr. rer. nat.) is an Assistant Professor (soon to be Associate Professor) at the University of Winnipeg. Originally a graduate in mathematics, he started his career in digital agriculture as a postdoctoral fellow in 2019 with the inter-disciplinary and multi-institutional TerraByte research group, of which he is a co-lead. He started at TerraByte by building the robotic imaging system EAGL-I and creating the group’s online open-access data portal that gives access to millions of labelled plant images. In 2022 he joined the Applied Computer Science department’s faculty with research focus on data and digital tools in agriculture. In 2026 he became co-lead of the Manitoba Consortium for Digital Agriculture with the goal of bringing researchers in this field together and to connect them with industry partners, government, and growers’ associations. His further research interests lie in stochastic network calculus and the intersection of computer science and real-world problems, such as wireless sensor networks and automating the classification and counting of microplastics.

Carson K. Leung obtained his BSc(Hons), MSc and PhD all from the University of British Columbia (UBC), Vancouver, Canada. He is currently a Professor of Computer Science, as well as the Chair of the undergraduate Data Science program, at the University of Manitoba, Canada. He has contributed more than 450 refereed publications on the topics of big data, bioinformatics, computational intelligence, cognitive computing, data analytics, data mining, data science, fuzzy systems, health informatics, information technology, machine learning, social network analysis, and visual analytics. He received Best Paper awards in IEEE SmartData 2021, IEEE IRI 2022, IEEE CBDCom 2023, and IEEE ScalCom 2025. He has also served on the Organizing Committee of many conference (e.g., Program Chair of the recent Canadian AI 2026). He is a Senior Member of the ACM and the IEEE; he is also an IEEE Computer Society Distinguished Contributor.

Dr. Ehsan Atoofian received his B.Sc. and M.Sc. degrees in Computer Engineering from University of Tehran, Iran, in 2000 and 2003, respectively, and his Ph.D. degree in Computer Engineering from University of Victoria, Canada, in 2008.
He is currently an Associate Professor in the Department of Electrical and Computer Engineering at Lakehead University, Thunder Bay, Canada. His research focuses on quantum computing, efficient deep-learning systems, and general-purpose graphics processing units (GPGPUs). His previous work has encompassed parallel programming models and the architecture of single-core and multi-core processors.
Dr. Atoofian's research has been supported by major Canadian funding agencies, including the Natural Sciences and Engineering Research Council of Canada (NSERC) and Mitacs. He has contributed extensively to advancing high-performance and intelligent computing technologies through both academic research and industry collaborations.

Dr. T. Akilan (Ph.D., P.Eng., SMIEEE) is an Associate Professor in the Department of Software Engineering at Lakehead University. His research interests lie in computer vision, deep learning, image processing, and machine learning. His research group develops algorithms addressing challenges in pattern recognition, image segmentation, predictive modeling, and data analytics, with applications in intelligent transportation systems, healthcare, and precision agriculture.
In addition to research and development, Dr. Akilan is actively engaged in volunteer and leadership service. He serves as Director of Engineering Co-op and is a member of several internal committees at Lakehead University. He is also an active reviewer for several international journals and conferences, including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Image Processing, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and has reviewed grant applications for several national/international programs.

Dr. Naqi Sayed joined Lakehead University in August 2002 with an appointment to the Accounting Discipline in the Faculty of Business Administration. He holds a Masters of Business Administration from Pakistan and a Masters in Banking Management from the United Kingdom. He has gained extensive working experience in non-banking financial institutions and commercial banks in the area of small business and corporate finance. Prior to joining Lakehead University, Naqi taught Accounting and Management Accounting in Australia while working for his PhD in Accounting and Finance. Naqi received his Certified Management Accountant designation in Ontario, Canada in 2009. His teaching interests include management accounting, performance management and strategy. His management accounting publications and research interests include performance management, Balanced Scorecard and multi-criteria decision models. He has also published in the area of equity finance (especially venture capital), resource-based theory and accounting education.

Dr. Alkhateeb earned his Bachelor's degree in Computer Science from the University of Jordan, Amman, Jordan, in 2004, and obtained his MSc and Ph.D. degrees in Computer Science from the University of Windsor, Canada, in 2011 and 2018, respectively. Before joining Lakehead University, he served as an Assistant Professor at Princess Sumaya University for Technology in Amman, Jordan, from 2021 to 2023. Prior to that, he held positions as an Assistant Professor and Mitacs Accelerate Postdoctoral Fellow at the University of Windsor, Canada.

He is currently serving as a faculty member at Lakehead University, where his research focuses on developing AI-driven models to predict health outcomes related to various cancers and mental health states. His work involves integrating heterogeneous health data using embedding techniques and applying machine and deep learning approaches for analysis and prediction.