Zhen Wang / 王震

Hi! I'm Zhen, currently a postdoctoral researcher at UC San Diego, working with Prof. Zhiting Hu and Prof. Eric P. Xing, focusing on advancing foundation agentic systems and scientific discovery. I obtained my PhD from The Ohio State University, advised by Prof. Huan Sun, where I developed foundational frameworks for knowledge-centric NLP systems.

I'm fortunate to have the privilege of working with exceptional researchers like Rameswar Panda, Yoon Kim, Nebojsa Jojic, Nikolay Malkin, Leonid Karlinsky, and Bo Zong across premier industrial labs (MIT-IBM Lab, Microsoft Research, NEC Labs America) and academic institutions (UCSD, CMU, MBZUAI). I've been honored with the OpenAI Agentic AI Research Grant, SoCal NLP 2023 Best Paper Award, Alexa Prize TaskBot Challenge 2022, and Rising Star in Data Science 2021.

Outside of research, you'll find me exploring hiking trails, playing pickleball, or planning my next adventure in national parks. I'm also a passionate sports fan, cheering for the Buckeyes, Dodgers, Lakers, Inter Miami, and Chiefs.

Email  /  GitHub  /  Twitter  /  Google Scholar

Research Overview (Check More Here →)

My research sits at the emerging intersection of advanced AI reasoning and accelerated discovery across science and society. This space extends traditional data‑driven discovery, calling for AI systems that actively explore hypotheses, simulate possibilities, and generate new, verifiable knowledge across disciplines.

I approach this field by identifying new foundational problems with rigorous, reproducible benchmarks and evaluation protocols (scBench, FIRE‑Bench, LLM Reasoners), and by building systems that reason, simulate, and interact directly with complex data, code, tools, literature, and human collaborators. I validate these systems end‑to‑end across biology, materials science, machine learning research, and human‑centered domains, with novel discovery results including but not limited to new cancer driver genes, new materials, and new scientific insights.

Together, these three pillars form a unified research program where AI reasoning drives discovery across Science and Society. My advances in structured reasoning (RAP, ThinkSum, PromptAgent) supply the computational foundation for autonomous scientific exploration (scPilot, FIRE-Bench, TaijiChat in T-cell programming, Nature), while these same principles enable scalable governance and social simulation (Dynamic Rewarding, Decentralized Arena, DeepPersona). This integrated approach establishes a framework for reliable, verifiable, and human-guided discovery.

News

Research Highlights (Full List in Here or Google Scholar)

Source code from Leonid Keselman, design and inspiration from Jon Barron and Dongkuan.