About Me
I am a fourth-year PhD student in the Department of Computer and Information Science at the University of Pennsylvania, advised by Jacob Gardner and Osbert Bastani.
Previously, I completed my undergraduate degrees in Computer Science and Mathematics at Cornell University.
I develop methods at the intersection of probabilistic machine learning, Bayesian optimization, and generative modeling.
My work combines large language models with principled optimization for open-ended design problems, using generative models
as priors in BO loops to enable sample-efficient search. Applications span natural sciences (peptide/protein sequence design)
and systems challenges (database query optimization). I focus on building closed-loop discovery pipelines that integrate
modeling, acquisition, and real-world feedback, emphasizing reliability, interpretability, and scalability.
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Research
* indicates equal contribution. See Google Scholar for a complete list of publications.
Conference & Journal Publications
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Automated High-Level Code Optimization for Warehouse Performance
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
IEEE Micro, "Top Picks" issue, 2025
[paper]
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Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity
Wentao Guo, Jikai Long, Yimeng Zeng, Zirui Liu, Xinyu Yang, Yide Ran, Jacob R. Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, Zhaozhuo Xu
International Conference on Learning Representations (ICLR 2025)
[paper]
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Learned Offline Query Planning via Bayesian Optimization
Jeffrey Tao, Natalie Maus, Haydn Jones, Yimeng Zeng, Jacob R. Gardner, Ryan Marcus
ACM SIGMOD International Conference on Management of Data (SIGMOD 2025)
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Learning Performance-Improving Code Edits
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
International Conference on Learning Representations (ICLR 2024)
[paper]
[code]
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Generative Adversarial Model-Based Optimization via Source Critic Regularization
Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James Gee, Osbert Bastani
Advances in Neural Information Processing Systems (NeurIPS 2024)
[paper]
[code]
Workshop Papers
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Adversarial Query Synthesis via Bayesian Optimization
Yimeng Zeng, Jeffrey Tao, Haydn Thomas Jones, Natalie Maus, Osbert Bastani, Jacob R. Gardner, Ryan Marcus
NeurIPS ML for Systems Workshop 2025
[paper]
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Antibody Design with Constrained Bayesian Optimization
Yimeng Zeng, Hunter Elliott, Phillip Maffettone, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
ICLR Workshop on Generative & Experimental Methods in Biology (GEMBio 2024)
Oral Presentation
[paper]
Preprints
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Large Scale Multi-Task Bayesian Optimization with Large Language Models
Yimeng Zeng, Natalie Maus, Haydn Thomas Jones, Jeffrey Tao, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Ryan Marcus, Osbert Bastani, Jacob R Gardner
arXiv preprint, 2025
[paper]
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A Generative Artificial Intelligence Approach for Antibiotic Optimization
Marcelo D. T. Torres*, Yimeng Zeng*, Fangping Wan*, Natalie Maus, Jacob Gardner, Cesar de la Fuente-Nunez
bioRxiv preprint, 2024
[paper]
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Improving Structural Diversity of Black-Box LLMs via Chain-of-Specification Prompting
Halley Young, Yimeng Zeng, Jacob Gardner, Osbert Bastani
arXiv preprint, 2024
[paper]
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Inverse Protein Folding Using Deep Bayesian Optimization
Natalie Maus*, Yimeng Zeng*, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
arXiv preprint, 2023
[paper]
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Teaching
- Graduate Teaching Assistant, CIS 5200 Introduction to Machine Learning (Fall 2023)
- Undergraduate Teaching Assistant, CS 4780 Introduction to Machine Learning, Cornell University (2021-2022)
Service
Reviewer: NeurIPS 2024, ACL ARR (June 2024), NeurIPS 2025
Honors & Awards
- University of Pennsylvania Graduate Fellowship
- Cornell University Dean's List, College of Arts and Sciences (Fall 2019, Fall 2020)
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Modified from Jon Barron.
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