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Tien Dat Nguyen I am a Master of Mathematics student in Computer Science at the University of Waterloo and an affiliate of the Vector Institute, being supervised by Professor Victor Zhong. I am driven by a mission to apply foundational Machine Learning research to critical challenges in AI Safety and the acceleration of Scientific Discovery, with a focus on domains like Drug Discovery and Material Discovery. I earned my Bachelor's in Computer Science from the Korea Advanced Institute of Science and Technology (KAIST), where my expertise was grounded in 2.5 years of research at KAIST’s Vision & Learning Lab. There, I researched under the supervision of Professor Seunghoon Hong and Professor Hongseok Yang, and was mentored by Dr. Jinwoo Kim. My research focused on the topics of Geometric Deep Learning and Graph Machine Learning. I have also applied my skills in industry at BionSight, a Drug Discovery startup, where I developed an LC-MS quantification algorithm for measuring molecular abundance, supporting their drug discovery pipeline. Moving forward, I am focusing my research on two core areas. I am committed to accelerating Scientific Discovery in areas like Drug Discovery and Material Discovery, alongside a dedication to advancing AI Safety by developing safe-by-design AI systems. Technically, I am exploring these problems through Probabilistic Modeling and Inference, and advanced Generative models, particularly GFlowNets and LLMs. CV / Google Scholar / GitHub / Twitter / LinkedIn |
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News
Sep 2024: I start my graduate study at Waterloo University.
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Publications* denotes equal contribution. |
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Learning Symmetrization for
Equivariance with Orbit Distance Minimization
Tien Dat Nguyen*, Jinwoo Kim*, Hongseok Yang, Seunghoon Hong NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2023 paper / code / poster |
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Learning Probabilistic
Symmetrization for Architecture Agnostic
Equivariance
Jinwoo Kim,Tien Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong NeurIPS, 2023 (Spotlight Presentation, Top 3% of Submissions) paper / code / poster / slides (extended) |
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Pure Transformers are
Powerful
Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong NeurIPS, 2022 Slated for official integration in PyTorch Geometric (PyG) library, as mentioned in technical paper paper / code / Invited talk / poster / slides (extended) |
Experience
Vector Institute
Reading to Learn Lab
BionSight
Vision & Learning Lab |
Honors and Awards
Excellence Award, Undegraduate Research Program (KAIST), Spring 2022 |
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Last updated: November 2025 |
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