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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 Stanford University. Di received his Ph.D. degree from McGill University, Montreal, Canada, in 2018, and his M.Sc. degree from Peking University, Beijing, China, in 2013. He also holds dual bachelor’s degrees in Microelectronics and Economics. During his time at Samsung AI Center Montreal, Di received multiple recognitions and awards, including the All-Star Award and Rock-Star Award, and was named a Top Performer for three consecutive years. His research interests span reinforcement learning, time series forecasting, and trustworthy generative AI, with a strong focus on developing fundamental AI methods and translating them into reliable, real-world engineering systems such as power grids, communication networks, and transportation systems. Di has published more than 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, and holds 20+ U.S. patents on AI applications in decision-making, forecasting, trustworthy LLMs, load balancing, energy management, anomaly detection, and energy efficiency, including two patents recognized as Corporate Strategically Important. His work has been recognized with three Best Paper Awards—IEEE GLOBECOM 2021 (AI for Telecommunications), IEEE ITSC 2024 (AI for Transportation), and IEEE EPEC 2025 (AI for Energy)—and four 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 Stanford University. Di received his Ph.D. degree from McGill University, Montreal, Canada, in 2018, and his M.Sc. degree from Peking University, Beijing, China, in 2013. He also holds dual bachelor’s degrees in Microelectronics and Economics. During his time at Samsung AI Center Montreal, Di received multiple recognitions and awards, including the All-Star Award and Rock-Star Award, and was named a Top Performer for three consecutive years. His research interests span reinforcement learning, time series forecasting, and trustworthy generative AI, with a strong focus on developing fundamental AI methods and translating them into reliable, real-world engineering systems such as power grids, communication networks, and transportation systems. Di has published more than 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, and holds 20+ U.S. patents on AI applications in decision-making, forecasting, trustworthy LLMs, load balancing, energy management, anomaly detection, and energy efficiency, including two patents recognized as Corporate Strategically Important. His work has been recognized with three Best Paper Awards—IEEE GLOBECOM 2021 (AI for Telecommunications), IEEE ITSC 2024 (AI for Transportation), and IEEE EPEC 2025 (AI for Energy)—and four Best Paper Award nominations.
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