Computer Science
Showing 1-50 of 54 Results
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Youssef Allouah
Postdoctoral Scholar, Computer Science
BioYoussef Allouah is a postdoctoral scholar at Stanford University. He is working with Prof. Sanmi Koyejo at the Computer Science department. His research interests are broadly in the principles of trustworthy machine learning, specifically the theory and practice of privacy, robustness, and unlearning.
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Chongkai Gao
Graduate Visiting Researcher Student, Computer Science
BioChongkai is a PhD student from the National University of Singapore, and a visiting student researcher at Stanford University in Prof. Fei-Fei Li's group. His research is about building hierarchical foundation models and structured evaluation of general-purpose robot manipulation. Homepage: https://chongkaigao.com/.
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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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Enyi Jiang
Graduate Visiting Researcher Student, Computer Science
BioI am a third-year Computer Science Ph.D. student at the University of Illinois at Urbana-Champaign, advised by Prof. Sanmi Koyejo and Prof. Nancy Amato. I am broadly interested in trustworthy machine learning, especially in AI safety and alignment.
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Jing Liang
Postdoctoral Scholar, Computer Science
BioJing Liang is a postdoctoral scholar in the Department of Computer Science at Stanford University, where he is affiliated with the Stanford Robotics Center and the Stanford Center on Longevity. He received his Ph.D. in Computer Science from the University of Maryland, College Park.
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Anders Gjølbye Madsen
Graduate Visiting Researcher Student, Computer Science
BioAnders Gjølbye Madsen is a PhD fellow at the Technical University of Denmark. His research focuses on trustworthy machine learning for healthcare, with an emphasis on explainability, interpretability, and reliable evaluation of models in high-stakes settings. He works broadly with modern deep learning methods, including self-supervised learning, and is interested in questions of robustness and alignment. He is the author of PatternLocal, a NeurIPS 2025 paper on reducing false-positive attributions in explanations of non-linear models by refining local explanation approaches. He earned a BSc in Artificial Intelligence and Data from DTU and completed an MSc in Engineering in Applied Mathematics at DTU, including a study exchange in Computational Science and Engineering at ETH Zürich. Anders will spend 2026 as a visiting researcher at Stanford University’s Trustworthy AI Research (STAIR) Lab, working with Professor Sanmi Koyejo.
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Alexander Spangher
Postdoctoral Scholar, Computer Science
BioAlexander Spangher is a post-doctoral researcher advised by Daniel Ho, Sanmi Koyejo and Diyi Yang. His research focuses on modeling human decision-making in creative domains, especially in contexts where data is limited and rewards and goals are less clear. He is building out a new domain of learning, called emulation learning, with the goal of training the next generation of reasoning-oriented language models to be more proficient in these domains. His research has been used at technology organizations like OpenAI, Google and EleutherAI. He is especially passionate about helping journalists and has framed tasks and trained reasoning LLMs to help journalists find stories and sources, structure narratives and track information updates. These tools have been incorporated into newsrooms at the New York Times, Bloomberg and Stanford Big Local News, impacting thousands of journalists; and his work is also informing the next generation of journalistic education at USC Annenberg. His work has received numerous awards including two outstanding paper awards at EMNLP 2024, one spotlight award at ICML 2024, one outstanding paper award at NAACL 2022 and a best paper award at CJ2023; and he has been supported by a 4-year Bloomberg PhD Fellowship. His work is broad: in addition to his work in NLP and computational journalism, he has studied misinformation at Microsoft Research and collaborated with the MIT Plasma Science and Fusion Center to model plasma fusion processes.
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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.
