I am a Computer Science Ph.D. candidate at University of Illinois Urbana-Champaign advised by Prof. Jimeng Sun. I am also working closely with Prof. Jiawei Han, Prof. Sheng Wang, Dr. Lijun Wu, Dr. Kun Qian and Tian Wang. Before that, I earned my B.Eng and MS degree in Automation at Tsinghua University.

My research focuses on foundation models (e.g., large language models (LLMs), multi-modal models), reinforcement learning (RL), and agents, with their applications in healthcare, biomedicine, and recommendation systems. I am particularly interested in how these models can be used for reasoning, retrieval, and decision-making in complex, real-world scenarios.

• LLM-centric Research: Search Agents (DeepRetrieval, s3), Knowledge Graphs (GenRES), LLM Efficiency (Cascade Speculative Drafting), Revisiting SFT Forgetting (TALR)
• LLMs/RL + Healthcare: EHR Reasoning Model with RL (EHRMind), Clinical Trial Foundation Model (Panacea, TrialPanorama)
• BioMedical Foundation Models: Molecule Foundation Model (Pisces, R²-DDI), Single-cell Foundation Model (EpiAgent)
• LLMs/RL + Recommendation: Bridge RecSys with LLMs using RL (Rec-R1)

I am currently seeking research-oriented internship opportunities for the summer of 2026. Please contact me if you have any recruitment openings.

News

12/2025 Our Survey Adaptation of Agentic AI is released! We categorized existing agent papers into four paradigms, which makes the overall research landscape much clearer.

10/2025 Our Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning is accepted by TMLR.

09/2025 Our EpiAgent: Foundation model for single-cell epigenomic data is accepted by Nature Methods (IF=32.1).

08/2025 Starting Machine Learning Research Internship at Keiji AI.

08/2025 Our s3: You Don’t Need That Much Data to Train a Search Agent via RL is accepted by EMNLP 2025.

07/2025 Our DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning is accepted by COLM 2025.

07/2025 Our GT2Vec: Large Language Models for Knowledge Graph Augmented Text Embedding is accepted by KDD 2025 Workshop SKnow-LLM.

05/2025 Starting Applied Scientist Internship at Amazon SFAI, Palo Alto, advised by Hyokun Yun and Lihong Li.

04/2025 Our Pisces: A multi-modal data augmentation approach for drug combination synergy prediction is accepted by Cell Genomics.

09/2024 Our Cascade Speculative Drafting for Even Faster LLM Inference is accepted by NeurIPS 2024.

05/2024 Starting Applied Scientist Internship at Amazon, Palo Alto, advised by Sheng Wang and Danai Koutra.

03/2024 Our GENRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models is accepted by NAACL 2024.

01/2024 Our CAMBranch: Contrastive Learning with Augmented MILPs for Branching is accepted by ICLR 2024.

Selected Papers

• Jiacheng Lin, Tian Wang, Kun Qian, Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning, TMLR, 2025. [PDF][CODE]
• Pengcheng Jiang*, Jiacheng Lin∗, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han, DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning, COLM, 2025. [PDF][CODE]
• Hanwen Xu*, Jiacheng Lin∗, Addie Woicik, Zixuan Liu, Jianzhu Ma, Sheng Zhang, Hoifung Poon, Liewei Wang, Sheng Wang, Pisces: A multi-modal data augmentation approach for drug combination synergy prediction, Cell Genomics, 2025. [PDF][CODE]
• Jiacheng Lin∗, Hanwen Xu∗, Addie Woicik, Jianzhu Ma and Sheng Wang, Pisces: A cross-modal contrastive learning approach to synergistic drug combination prediction, RECOMB, 2023. [PDF][CODE]
• Jiacheng Lin, Lijun Wu, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin and Tie-Yan Liu, R²-DDI: Relation-aware Feature Refinement for Drug-Drug Interaction Prediction, Briefings in Bioinformatics, Volume 24, Issue 1, 2023. [PDF][CODE]