Hi there, I’m Zhe Zeng. I am an Assistant Professor in the Department of Computer Science at University of Virginia, directing the Trustworthy AI through Knowledge and Optimization (TAKO🐙) Lab. My research interests lie broadly in artificial intelligence (AI) and machine learning (ML) with recent focus on neurosymbolic AI and probabilistic ML. I aim to enable and support decision-making in the real world in the presence of probabilistic uncertainty and symbolic knowledge (graph structures, logical, arithmetic, and physical constraints, etc) to achieve trustworthy AI and aid scientific discoveries. My current work can be roughly catalogued as:
- Reasoning: probabilistic inference, tractable probabilistic models
- Learning: constrained deep learning, graph machine learning, weakly supervised learning
- Trustworthiness: explainability, uncertainty quantification, domain-knowledge incorporation
Prior to joining UVA, I was a Faculty Fellow in the Computer Science Department at New York University (NYU), hosted by Prof. Andrew Gordon Wilson. I obtained my Ph.D. degree in Computer Science at University of California, Los Angeles (UCLA) in 2024, where I was lucky to be advised by Prof. Guy Van den Broeck as a member of the Statistical and Relational Artificial Intelligence (StarAI) Lab, and my B.S. in Mathematics and Applied Mathematics at Zhejiang University in 2018. I received the Amazon Doctoral Student Fellowship in 2022 and the NEC Student Research Fellowship in 2021, and was selected for UVA Engineering Rising Scholars in 2025, and the Rising Stars in EECS in 2023.
👩💻 Office: Rice Hall 105
📩 Email: zhez [at] virginia [dot] edu
TAKO Lab
We are the Trustworthy AI through Knowledge and Optimization (TAKO🐙) Lab@UVA. Our research addresses core challenges in advancing methods in neurosymbolic AI and generative modeling, with a focus on developing trustworthy AI models. Learn more about the lab here.
📢 About Joining: I have open, fully-funded PhD positions in Fall 2026 or later, and am also looking for interns/visiting students. If you would like to work with our lab, feel free to reach out by email, specifying in the subject the position you are looking for: [PhD], [Internship], or [Visiting].
Selected Publications
Talks
Honors and Awards
Professional Activities and Service
Organizer
- UAI 2025 Workshop: 8th Workshop on Tractable Probabilistic Modeling: From Logic to Probabilities and Back Link
- CVPR 2025 Workshop: Navigating the Future: Ensuring Trustworthiness in Multi-Modal Open-World Intelligence Link
Guest Editor
- International Journal of Computer Vision
Reviewer
- Conference: ICLR, UAI, IJCAI, ICML, AISTATS, NeurIPS, AAAI
- Journal: International Journal of Approximate Reasoning (IJAR)
Volunteer
- WiML Workshop at NeurIPS 2023
- WiML Mentorship Program 2023
- Samueli Undergraduate Research Program 2023
- WiML Un-Workshop at ICML 2023
- CSPhD@UCLA Mentorship Program 2021
Teaching
Lecturer
- CS 6501 Special Topics in Computer Science: Trustworthy AI at UVA, Fall 2025
- CSCI-UA.0473 Fundamentals of Machine Learning_ at NYU, Spring 2025
- CSCI-UA.0473 Fundamentals of Machine Learning at NYU, Fall 2024
- CS267A Probabilistic Programming and Relational Learning at UCLA, Spring 2023 as guest lecturer
Teaching Assistant
- CS C121/221 Probabilistic Models in Computational Genomics at UCLA, Spring 2024
- CS161 Fundamentals of Artificial Intelligence at UCLA, Fall 2020/2021/2022
Miscellaneous
- StarAI Lab 2022 World Cup Bracket Challenge Champion 😎
Contact
- 85 Engineer's Way, Charlottesville, VA 22903