I'm a third-year Ph.D. candidate in Computer Science at Yale University, where I am very fortunately advised by Prof. Mark Gerstein. Previously, I got my master's from Yale CS as well, advised by Prof. Dragomir Radev. I am a graduate affiliate at Grace Hopper College since 2021. My research lies in the intersection of large language models and applications in bioinformatics, with a view towards building safe AI scientists, e.g.,

  • Reasoning and Coding: AI scientists capable of verifiable reasoning can autonomously design, plan, and perform experiments by code execution [ACL 24 Findings, Bioinfo. 24, NAACL 24, ACL 23, EMNLP 23, SEKE 19].
  • LLM Agents and Tool Use: AI scientists could integrate AI models and specialized tools with experimental platforms [ICLR 24, ICLR 24 LLM Agents WS].
  • Drug Design: AI scientists can impact areas ranging from molecule modeling, protein folding, and virtual cell simulation to developing new therapies [Nat. Biotech. 24, Brief. in Bioinfo. 24, Bioinfo. 24].
I am looking for grads / undergrads to collaborate and actively engage in mentorship. Feel free to email me if you are starting in the field / confused about publication expectations, PhD Admissions, etc. I especially encourage students from underrepresented groups to reach out.

My research is supported by Schmidt Futures.

Selected Publications

Discover the google scholar | semantic scholar

  • Fast, Sensitive Detection of Protein Homologs Using Deep Dense Retrieval
    Liang Hong*, Zhihang Hu*, Siqi Sun*, Xiangru Tang*, Jiuming Wang, Qingxiong Tan, Liangzhen Zheng, Sheng Wang, Sheng Xu, Irwin King, Mark Gerstein, Yu Li.
    Nature Biotechnology (IF=33.1)
    [PDF] [Abstract] [Bib]
    DPR
  • MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications
    Xiangru Tang*, Chunyuan Deng*, Hanmin Wang*, Haoran Wang*, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein.
    EMNLP 2024 (Demo)
    [PDF] [Abstract] [Bib]
    MIMIR
  • Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
    Xiangru Tang, Qiao Jin, Kunlun Zhu, Tongxin Yuan, Yichi Zhang, Wangchunshu Zhou, Meng Qu, Yilun Zhao, Jian Tang, Zhuosheng Zhang, Arman Cohan, Zhiyong Lu, Mark Gerstein.
    ICLR 2024 Workshop on LLM Agents
    [PDF] [Abstract] [Bib]
  • Step-Back Profiling: Distilling User History for Personalized Scientific Writing
    Xiangru Tang, Xingyao Zhang, Yanjun Shao, Jie Wu, Yilun Zhao, Arman Cohan, Ming Gong, Dongmei Zhang, Mark Gerstein.
    IJCAI 2024 Workshop on AI4Research (Best Paper Award)
    [PDF] [Abstract] [Bib]
    Step-Back Profiling
  • BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding
    Xiangru Tang*, Xinwu Ye*, Fang Wu*, Yanjun Shao, Yin Fang, Siming Chen, Dong Xu, Mark Gerstein.
    biorXiv, 2024
    "A quantum leap in inverse protein folding from 67% to 88%!"
    [PDF] [Abstract] [Bib]
    BC-Design
  • ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
    Xiangru Tang*, Yuliang Liu*, Zefan Cai*, Junjie Lu, Yichi Zhang, Yanjun Shao, Zexuan Deng, Helan Hu, Kaikai An, Ruijun Huang, Shuzheng Si, Sheng Chen, Haozhe Zhao, Liang Chen, Yan Wang, Tianyu Liu, Zhiwei Jiang, Baobao Chang, Yujia Qin, Wangchunshu Zhou, Yilun Zhao, Arman Cohan, Mark Gerstein.
    arXiv, 2023
    "Can LLMs do machine learning tasks?"
    [PDF] [Abstract] [Bib]
    ML-Bench
  • A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation
    Xiangru Tang*, Howard Dai*, Elizabeth Knight*, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein.
    Briefings in Bioinformatics 2024 (IF=13.99)
    "An introductory overview with a clear breakdown of datasets, benchmarks, & models."
    [PDF] [Abstract] [Bib]
    GenAI4Drug
  • MolLM: A Unified Language Model for Integrating Biomedical Text with 2D and 3D Molecular Representations
    Xiangru Tang, Andrew Tran, Jeffrey Tan, Mark Gerstein.
    ISMB 2024 (published in Bioinformatics, IF=6.93)
    [PDF] [Abstract] [Bib]
    MolLM
  • BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models
    Xiangru Tang, Bill Qian, Rick Gao, Jiakang Chen, Xinyun Chen, Mark Gerstein.
    ISMB 2024 (published in Bioinformatics, IF=6.93)
    "BioCoder input covers repository-level potential package dependencies, class declarations, & global variables."
    [PDF] [Abstract] [Bib]
    BioCoder
  • MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
    Xiangru Tang*, Anni Zou*, Zhuosheng Zhang, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark Gerstein.
    ACL 2024 Findings
    "The first multi-agent framework within the medical context!"
    [PDF] [Abstract] [Bib]
    MedAgents
  • Struc-Bench: Are Large Language Models Good at Generating Complex Structured Tabular Data?
    Xiangru Tang, Yiming Zong, Jason Phang, Yilun Zhao, Wangchunshu Zhou, Arman Cohan, Mark Gerstein.
    NAACL 2024 (Oral)
    [PDF] [Abstract] [Bib]
    Struc-Bench
  • Meta-CoT: Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models
    Anni Zou, Zhuosheng Zhang, Hai Zhao, Xiangru Tang.
    arXiv, 2023
    "Bridge the gap between performance and generalization when using the CoT prompting!"
    [PDF] [Abstract] [Bib]
    Meta-CoT
  • Aligning Factual Consistency for Clinical Studies Summarization through Reinforcement Learning
    Xiangru Tang, Arman Cohan, Mark Gerstein.
    Clinical Natural Language Processing Workshop at ACL 2023
    [PDF] [Abstract] [Bib]
  • GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning
    Xiangru Tang, Andrew Tran, Jeffrey Tan, Mark Gerstein.
    Clinical Natural Language Processing Workshop at ACL 2023
    [PDF] [Abstract] [Bib]
    MEDIQA
  • CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
    Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev.
    NAACL 2022 (Oral)
    [PDF] [Abstract] [Bib]
  • Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
    Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev.
    NAACL 2022
    [PDF] [Abstract] [Bib]

Professional Services

Area Chair: ACL ARR (ACL, EMNLP, NAACL, etc).
Workshop Organizer: ICLR 2024 Workshop on LLM Agents, SIGDIAL/INLG 2023 Workshop on Taming LLMs.
Tutorial Organizer: ISMB 2024 Tutorial on A Practical Introduction to LLMs in Biomedical Research.
Session Chair: ACL 2024 BoF on AI for Science, NAACL 2024 BoF on LLMs for Science.
Conference Program Committee / Reviewer: NeurIPS, ICML, ACL, EMNLP, CIKM, NAACL, INLG, IEEE BigData, COLM.
Journal Reviewer: npj Digital Medicine, TPAMI, Neurocomputing, Briefings in Bioinformatics, PLOS Computational Biology, BMC Bioinformatics, PLOS ONE, Health Data Science.
Workshop Reviewer: KDD 2023 Workshop on Data Mining in Bioinformatics, ACL 2023 Workshop on Building Educational Apps, ACL 2023 Workshop on Clinical NLP, ICML 2023 Workshop on Neural Conv AI, ICML 2023 Workshop on Interpretable ML in Healthcare, NAACL-HLT 2021 Workshop on Language and Vision Research.

Teaching

Teaching Fellow - CPSC 452/CPSC 552/AMTH 552/CB&B 663 Deep Learning Theory and Applications, Yale University, 2023 Spring.
Teaching Fellow - CPSC 437/CPSC 537 Introduction to Database Systems, Yale University, 2023 Fall.
Teaching Fellow - CPSC 452/CPSC 552/AMTH 552/CB&B 663 Deep Learning Theory and Applications, Yale University, 2024 Spring.
Teaching Fellow - CPSC 437/CPSC 537 Database Systems, Yale University, 2024 Fall.

Misc.

My 12 coursework at Yale: CPSC 523 Principles of Operating Systems, 537 Intro to Database, 539 Software Engineering, 552 Deep Learning Theory, 553 Unsupervised Learning, 569 Randomized Algorithms, 577 NLP, 583 Deep Learning on Graph, 668 Blockchain Research, 677 Adv NLP, 680 Trustworthy Deep Learning, 752 Biomedical Data Sci.