Ph.D. Student
Board MemberI am a first-year Ph.D. student at McGill University and Mila – Quebec AI Institute, co-advised by Prof. Doina Precup and Prof. Xiao-Wen Chang. My research focuses on continual reinforcement learning, aiming to develop AI systems that can learn and adapt in non-stationary environments. I am also a Board Member of ContinualAI, a nonprofit organization advancing research in continual learning.
Previously, I completed my M.Sc. in Electrical and Computer Engineering at McGill and Mila, under the supervision of Prof. Narges Armanfard and Prof. Samira Ebrahimi Kahou, where I worked on anomaly detection. I also hold a B.Sc. in Electrical and Computer Engineering, with a Minor in Mathematics, from the University of Alberta.
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McGill University && Mila-Quebec AI InstituteSchool of Computer Science
Ph.D. Student
Supervisors: Doina Precup, Xiao-Wen ChangSep. 2025 - present -
McGill University && Mila-Quebec AI InstituteM.Sc. in Electrical and Computer Engineering
Supervisors: Narges Armanfard, Samira Ebrahimi KahouSep. 2023 - Aug. 2025 -
University of AlbertaB.Sc. in Electrical and Computer Engineering
Minor in MathematicsSep. 2018 - Jun. 2023
Experience
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Mila – Quebec AI Institute / Linarite AIScientist in ResidenceSep 2025 – Dec 2025
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ContinualAIBoard MemberMay 2024 - Present
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Huawei Noah's Ark Lab, Montreal, CanadaResearcher InternJul. 2024 -- Sep. 2024
Honors & Awards
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Flight PS752 Commemorative Scholarship2025–2026
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FRQNT Doctoral Research Scholarship2025–2029
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FRQNT Master’s Research Scholarship2024–2025
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McGill University Graduate Excellence Fellowship2023–2025
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University of Alberta Academic Scholarship (×3)2019–2023
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University of Alberta Dean’s Research Award (×2)2021, 2022
News
Selected Publications (view all )
Zero-shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
Proceedings of the British Machine Vision Conference (BMVC) 2025 Oral
Zero-shot anomaly detection (ZSAD) aims to identify and localize unseen defects without requiring any labeled anomalies, but existing methods struggle to generalize under domain shifts. We propose PILOT, a framework combining a dual-branch prompt learning mechanism with label-free test-time adaptation, enabling dynamic adaptation to new distributions using only unlabeled data. PILOT achieves state-of-the-art performance on 13 industrial and medical benchmarks for both anomaly detection and localization under domain shift.
Zero-shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
Proceedings of the British Machine Vision Conference (BMVC) 2025 Oral
Zero-shot anomaly detection (ZSAD) aims to identify and localize unseen defects without requiring any labeled anomalies, but existing methods struggle to generalize under domain shifts. We propose PILOT, a framework combining a dual-branch prompt learning mechanism with label-free test-time adaptation, enabling dynamic adaptation to new distributions using only unlabeled data. PILOT achieves state-of-the-art performance on 13 industrial and medical benchmarks for both anomaly detection and localization under domain shift.
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
Zihan Wang, Ruimin Chen, Mengxuan Liu, Guanfang Dong, Anup Basu
International Conference on Smart Multimedia 2022
We propose SPGNet, a method for 3D human pose estimation that integrates multi-dimensional re-projection into supervised learning. Our approach enforces kinematic constraints and jointly optimizes both 2D and 3D pose consistency, leading to improved accuracy. Experiments on the Human3.6M dataset show that SPGNet outperforms many state-of-the-art methods.
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
Zihan Wang, Ruimin Chen, Mengxuan Liu, Guanfang Dong, Anup Basu
International Conference on Smart Multimedia 2022
We propose SPGNet, a method for 3D human pose estimation that integrates multi-dimensional re-projection into supervised learning. Our approach enforces kinematic constraints and jointly optimizes both 2D and 3D pose consistency, leading to improved accuracy. Experiments on the Human3.6M dataset show that SPGNet outperforms many state-of-the-art methods.