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Research Overview

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, etc.

My research agenda comprises three tightly connected pillars:

  • Reasoning. Reliable structured reasoning is the prerequisite for enabling autonomous discovery at scale. My contributions in this area include structured reasoning frameworks that facilitate probabilistic reasoning (ThinkSum, ACL'23), planning with textual world models (RAP, EMNLP'23; LLM Reasoners, COLM'24), simulation‑based reasoning and strategic planning with language models (PromptAgent, ICLR'24), and dynamic, test‑time optimization techniques (Nabla Reasoner), etc.
  • Science. Science drives my research because its open‑ended complexity offers the most rigorous testbed for reasoning systems and the clearest path toward socially meaningful progress. I operationalize this vision by training scientific foundation models (MutationProjector), building autonomous scientific copilots (scPilot, NeurIPS'25), creating agents capable of end‑to‑end research automation and insight rediscovery (FIRE‑Bench), and pioneering systematic AI augmentation of scientific inquiry (TaijiChat in Multi-omics atlas for T-cell programming, Nature), etc.
  • Society. True discovery is inherently human‑centered, demanding scalable governance, human‑aligned AI behavior, and deep societal simulation to ensure that AI‑generated insights authentically serve societal progress. My research addresses these goals by developing scalable self‑alignment and safe behavior (Dynamic Rewarding, EMNLP'24), decentralized and democratic evaluation frameworks (Decentralized Arena), and sophisticated human‑behavior simulations for personalized, socially informed discovery (DeepPersona), etc.

Research Keywords & Technical Expertise

My technical expertise spans the following interconnected areas:

• Reasoning & Planning Methods reasoning with language models, planning as inference, Monte Carlo Tree Search (MCTS), textual world models, probabilistic reasoning, test-time compute optimization, structured reasoning frameworks, neuro-symbolic methods
• Scientific Discovery & Automation automated scientific discovery, AI agents for science, foundation models for biology, single-cell transcriptomics analysis, computational biology, automated hypothesis generation, omics-native reasoning, scientific workflow automation, materials discovery
• Adaptation & Efficient Learning parameter-efficient fine-tuning (PEFT), prompt optimization, multi-task learning, transfer learning, few-shot adaptation, tool-augmented reasoning, mixture-of-experts (MoE), test-time adaptation
• AI Governance & Alignment scalable AI alignment, scalable oversight, decentralized evaluation, self-alignment (without human labels), scalable oversight, AI safety benchmarking
• Human-Centered AI & Social Modeling human behavior simulation, synthetic personas, social simulation with LLMs, LLM personalization, human-AI interaction, computational social science, agent-based modeling
• Knowledge-Centric NLP & Graph Learning graph neural networks (GNNs), knowledge graphs, retrieval-augmented generation (RAG), knowledge-centric NLP, structured representations, knowledge acquisition, domain adaptation, biomedical NLP, graph embedding methods