School of Engineering
Showing 1-55 of 55 Results
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Vijay Prakash Dwivedi
Postdoctoral Scholar, Computer Science
BioVijay Prakash Dwivedi is a Postdoctoral Scholar in Computer Science working on graph representation learning. He holds a PhD from Nanyang Technological University (NTU), Singapore. His work has made contributions to advancing benchmarks for Graph Neural Networks (GNNs), graph positional and structural encodings, and Graph Transformers as universal deep neural networks for graph-based learning. He has also contributed to the integration of parametric knowledge in large language models (LLMs) for diverse applications, particularly in healthcare. Several of the methods he developed during his PhD are now widely adopted in state-of-the-art Graph Transformers and other leading graph learning models. For his research, he received one of the Outstanding PhD Thesis Awards from the NTU College of Computing and Data Science. Vijay has over 7 years experience in both academia and industry with institutions including NTU, Snap Inc., Sony, and ASUS.
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Francis Engelmann
Postdoctoral Scholar, Computer Science
BioFrancis Engelmann is a PostDoc at Stanford university with Prof. Leonidas Guibas and Prof. Jeannette Bogh. Before that, he was a postdoctoral researcher at ETH Zurich collaborating with Prof. Marc Pollefeys and a visiting researcher at Google Zurich working with Federico Tombari. His current research focuses on computer vision and deep learning, particularly in the realm of 3D scene understanding. Prior to joining ETH Zurich, he obtained his Ph.D. from RWTH Aachen University under the guidance of Prof. Bastian Leibe, and interned at Google X in Munich, Google Research in Zurich, and Apple in California. Francis is a Fellow of the ETH AI Center, a member of the ELLIS Society, and a recipient of ETHZ Career Seed Award and SNSF Postdoc.Mobility fellowship.
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Pei Huang (黄 沛)
Postdoctoral Scholar, Computer Science
Current Research and Scholarly InterestsAutomated Reasoning, Trustworthy AI, Neural Symbolic Methods, Constraint Solving
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Haolun Wu
Graduate Visiting Researcher Student, Computer Science
BioHaolun Wu is a graduate student researcher at STAIR hosted by Prof. Sanmi Koyejo at Stanford University. He is a Ph.D. candidate in Computer Science at Mila - Quebec AI Institute and McGill University. His research focuses on AI alignment and human-centric AI, encompassing personalization, evaluation, and responsibility. He is specifically interested in learning from human feedback for personalization as well as in exploring the social benefits of these technologies. Additionally, he loves interdisciplinary research and is particularly interested in applying AI/ML techniques to education and psychology. During his Ph.D., Haolun interned and collaborated closely with Google Research and Microsoft Research.
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Min Wu
Postdoctoral Scholar, Computer Science
Current Research and Scholarly InterestsResponsible AI, AI safety, trustworthy AI, robustness, explainability and interpretability.
Formal methods, automated verification, verification of deep neural networks, formal explainable AI. -
Pei Xu
Postdoctoral Scholar, Computer Science
Current Research and Scholarly Interestscharacter animation, physics-based character control, crowd simulation
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Zhuo Zheng
Postdoctoral Scholar, Computer Science
BioMy research interests are Earth Vision and AI4Earth, especially multi-modal and multi-temporal remote sensing image analysis and their real-world applications.
First-author representative works:
- Our Change family: ChangeStar (single-temporal learning, ICCV 2021), ChangeMask (many-to-many architecture, ISPRS P&RS 2022), ChangeOS (one-to-many architecture, RSE 2021), Changen (generative change modeling, ICCV 2023)
- Geospatial object segmentation: FarSeg (CVPR 2020) and FarSeg++ (TPAMI 2023), LoveDA dataset (NeurIPS Datasets and Benchmark 2021)
- Missing-modality all weather mapping: Deep Multisensory Learning (first work on this topic, ISPRS P&RS 2021)
- Hyperspectral image classification: FPGA (first fully end-to-end patch-free method for HSI, TGRS 2020)
