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Di Wu, Ph.D.
Associate Professor @ Concordia, Department of Mechanical, Industrial and Aerospace Engineering
Adjunct Professor @ McGill, Department of Electrical and Computer Engineering
About Me
About Me
Di is an Associate Professor at Concordia University and an Adjunct Professor at McGill University. He was previously a Senior Staff Research Scientist at the Samsung AI Center Montreal, where he led the LLM Optimization team and the AI for Telecommunications team. Prior to joining Samsung, he conducted postdoctoral research at Mila and at Stanford University. Di received his Ph.D. from McGill University (Montreal, Canada) in 2018 and his M.Sc. from Peking University (Beijing, China) in 2013. He also holds dual bachelor’s degrees in Microelectronics and Economics. His research focuses on data-efficient reinforcement learning for real-world decision-making under uncertainty, with the goal of enabling reliable, safe, and scalable AI systems in high-stakes environments. His work integrates reinforcement learning with predictive modelling, including time-series forecasting, and trustworthy generative AI to support decision-making in complex, dynamic systems. He validates these methods through large-scale deployments in critical infrastructure domains, including power systems, communication networks, and transportation, achieving measurable improvements in efficiency, reliability, and resource utilization. Di has published over 100 papers in leading venues, including flagship domain journals (e.g., IEEE Transactions on Smart Grid, IEEE Transactions on Intelligent Transportation Systems, IEEE Journal on Selected Areas in Communications) and top-tier AI conferences and journals (e.g., NeurIPS, ICML, ICLR, AAAI, IJCAI, IROS, ICRA; Journal of Machine Learning Research, Transactions on Machine Learning Research, IEEE Transactions on Artificial Intelligence). He has secured over CAD $3 million in competitive and partnered research funding from agencies and industry partners, including NSERC, InnovÉÉ, Mitacs, Hydro-Québec, and FLO. He also holds 20+ U.S. patents on applications in decision-making, forecasting, trustworthy LLMs, load balancing, energy management, anomaly detection, and energy efficiency, including two recognized as strategically important. His work has received three Best Paper Awards—IEEE GLOBECOM 2021 (AI for Telecommunications), IEEE ITSC 2024 (AI for Transportation), and IEEE EPEC 2025 (AI for Energy)—as well as multiple Best Paper Award nominations.
Di is an Associate Professor at Concordia University and an Adjunct Professor at McGill University. He was previously a Senior Staff Research Scientist at the Samsung AI Center Montreal, where he led the LLM Optimization team and the AI for Telecommunications team. Prior to joining Samsung, he conducted postdoctoral research at Mila and at Stanford University. Di received his Ph.D. from McGill University (Montreal, Canada) in 2018 and his M.Sc. from Peking University (Beijing, China) in 2013. He also holds dual bachelor’s degrees in Microelectronics and Economics. His research focuses on data-efficient reinforcement learning for real-world decision-making under uncertainty, with the goal of enabling reliable, safe, and scalable AI systems in high-stakes environments. His work integrates reinforcement learning with predictive modelling, including time-series forecasting, and trustworthy generative AI to support decision-making in complex, dynamic systems. He validates these methods through large-scale deployments in critical infrastructure domains, including power systems, communication networks, and transportation, achieving measurable improvements in efficiency, reliability, and resource utilization. Di has published over 100 papers in leading venues, including flagship domain journals (e.g., IEEE Transactions on Smart Grid, IEEE Transactions on Intelligent Transportation Systems, IEEE Journal on Selected Areas in Communications) and top-tier AI conferences and journals (e.g., NeurIPS, ICML, ICLR, AAAI, IJCAI, IROS, ICRA; Journal of Machine Learning Research, Transactions on Machine Learning Research, IEEE Transactions on Artificial Intelligence). He has secured over CAD $3 million in competitive and partnered research funding from agencies and industry partners, including NSERC, InnovÉÉ, Mitacs, Hydro-Québec, and FLO. He also holds 20+ U.S. patents on applications in decision-making, forecasting, trustworthy LLMs, load balancing, energy management, anomaly detection, and energy efficiency, including two recognized as strategically important. His work has received three Best Paper Awards—IEEE GLOBECOM 2021 (AI for Telecommunications), IEEE ITSC 2024 (AI for Transportation), and IEEE EPEC 2025 (AI for Energy)—as well as multiple Best Paper Award nominations.
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