Hi! I'm Zhen, currently a Gordon and Betty Moore Foundation Postdoctoral Fellow hosted at UC San Diego. My postdoc mentors are Profs. Eric Xing (CMU & MBZUAI) and Zhiting Hu (HDSI & CSE, UCSD).
I am interested in building the foundations of general-purpose knowledge and reasoning systems that learn to acquire knowledge and reason to discover at scientist-level efficiency and extrapolation, with reliability, and that collaborate with humans across interdisciplinary and scientific domains.
To this end, my research seeks to answer fundamental questions: How do models represent knowledge for discovery? How do they reason and plan to extrapolate? How do they reliably collaborate and co-discover with humans? My philosophy centers on bringing new perspectives to shift existing paradigms or unifying seemingly different ones—exemplified by LLM Reasoners (reasoning as planning), PromptAgent (prompting as search), ToolkenGPT (tools as tokens), and the LAW 2025 workshop at NeurIPS (bridging language, agent, and world models).
I earned my PhD from the Computer Science department at The Ohio State University, advised by Prof. Huan Sun, where I developed foundational frameworks for knowledge-centric AI systems. My work has been supported by and recognized with the Gordon and Betty Moore Foundation Fellowship, OpenAI Research Grant, Rising Star in Data Science (UChicago), Best Paper Award at SoCal NLP, Amazon Alexa Prize, and more. I collaborate with MIT-IBM Lab, Microsoft Research, NEC Labs, and academic institutions including CMU, MBZUAI, and multiple UC campuses and national labs through the UC-LEAP project.
Research Overview View Details →
My research agenda, Scientist AI, sits at the intersection of advanced AI reasoning and accelerated discovery across science and society. This space extends traditional data-driven discovery, calling for human-centered AI systems that can acquire structured knowledge from data, actively explore hypotheses, simulate possibilities, and generate new, verifiable knowledge across disciplines.
Selected Honors
- Gordon and Betty Moore Foundation Fellowship 2025
- OpenAI Research Grant Award Research into Agentic AI Systems; One of 11 teams selected worldwide 2024
- Rising Star in Data Science, University of Chicago 2022
- Best Paper Award, SoCal NLP Symposium 2023
- Amazon Alexa Prize Winner, TaskBot Challenge (3rd Place) 2022
- Graduate Research Award (Mike Liu Scholarship), OSU 2022
News
Research Highlights Full List on Google Scholar →
Algorithmic Innovation
- Test-time optimization: PromptAgent (ICLR 2024), DRPO (EMNLP 2024), Nabla Reasoner (ICLR 2026)
- Reasoning and planning: RAP (EMNLP 2023), LLM Reasoners (COLM 2024), ThinkSum (ACL 2023)
- Efficient ML: Multitask Prompt Tuning (ICLR 2023), Self-MoE (ICLR 2025)
- Tool learning: ToolkenGPT (NeurIPS 2023 Oral)
- Knowledge extraction with weak supervisions: SurfCon (KDD 2019), X-MedRELA (ACL 2020) , ConPI (WSDM 2021)
Impact in Science
- AI for biomedical science: Nature 2025, MutationProjector (the first cancer genomics foundation model for tumor mutation profiles), scPilot (NeurIPS 2025), BioNEV (Bioinformatics 2019)
- AI for material science: TritonDFT (Multi-agents for DFT Automation), UC-LEAP (Discovering Low-Energy, AI-Informed Phase Transitions Topological Materials)
- AI for social science: Computational social simulation(DeepPersona, NeurIPS 2025), TacoBot (Alexa Prize 2021)
Open-Source Infrastructure
- Reasoning library: LLM Reasoners (2.3k+ GitHub stars), PromptAgent (300+ GitHub stars)
- Model evaluation: Decentralized Arena, FIRE-Bench (Reliably Benchmarking Scientific (Re-)Discovery)
- Pre-training data: TxT360 (58K downloads last month, globally deduplicated corpus across 99 CommonCrawl snapshots)
Visit my Publications page for the complete list of papers and research contributions.