Zifeng Wang

Zifeng Wang 王子丰 | How to pronounce?
Ph.D. Student
Department of Computer Science
University of Illinois Urbana-Champaign
[Curriculum Vitae] [Google Scholar] [Github]

Contact

Email: zifengw2 [at] illinois (dot) edu

201 North Goodwin Avenue MC 258 Urbana, IL 61801-2302

I am currently pursuing a PhD in Computer Science at Sunlab@UIUC, under the guidance of Prof. Jimeng Sun.

My research primarily revolves around the development of foundation models, including large language models (LLMs), tabular models, vision-language models, and biomedical multimodal models, for drug discovery and development.

My recent focus has shifted towards creating LLM-driven systems to enhance human-AI collaboration in various clinical trial tasks, including trial search, trial design, and data analysis.

We are actively developing a startup venture, Keiji.AI, with the vision to revolutionize clinical trial development with cutting-edge AI techniques.

News

[June 2024] Two our research papers on clinical trial foundation model and LLM pipeline for clinical evidence synthesis have been released!
[Mar 2024] Our research on AI for clinical trials is reported by Nature: How AI is being used to accelerate clinical trials.
[Feb 2024] Our research in patient-trial matching is reported by AUANews: Connecting Patients to Clinical Trials With Artificial Intelligence.

Education

Ph.D. Student, Computer Science, University of Illinois Urbana-Champaign, 2021 - Present

Supervised by Prof. Jimeng Sun.

M.S., Data Science, Tsinghua University, 2018 - 2021

Supervised by Prof. Shao-Lun Huang & Prof. Khalid M. Mosalam.
Thesis: Information Bottleneck for Representation Learning: New Vision.
Award: Outstanding MS graduate of Tsinghua University; National scholarship of graduate student

B.S., Civil Engineering, Tongji University, 2014 - 2018

Experience

Research Intern, AWS, Amazon, May-Aug. 2023.

Topic: AI for Drug Discovery.
Mentor: Zichen Wang.

Research Intern, Medidata, Dassault Systèmes, Feb-May. 2023.

Topic: Synthetic Data Generation for Clinical Trials.
Mentor: Mandis Beigi.

Research Intern, Amplitude, May-Aug. 2022.

Topic: Multi-treatment Causal Inference.
Mentor: Cao Xiao.

Research Intern, Jarvis Lab, Tencent , Dec. 2019-Jun. 2021.

Topics: Graph DL for Clinical Diagnosis; Lifelong Learning Diagnostic AI; Information-theoretic DL.
Mentor: Yefeng Zheng.

Activity & Award

News coverage by Nature: How AI is being used to accelerate clinical trials, 2024
News coverage by AUANews: Connecting Patients to Clinical Trials With Artificial Intelligence, 2024
Yee Memorial Fellowship, 2023
NeurIPS Scholar Award, 2022
Yunni & Maxine Pao Memorial Fellowship, 2022
Best student paper award of PAKDD'21, 2021
Best student research runner-up of Guangdong-HK-Macau Bay Area PhD Student Symposium, 2021
Best student research runner-up of TBSI Workshop On Learning Theory, 2021
Best Student research runner-up of 2019 TBSI Workshop On Data Science, 2019
Meritorious winner (1st class prize, top 7%) in USA Mathematical Contest in Modeling, 2017

Service

PC Member/Reviewer for NeurIPS'[22,23,24], EMNLP'[22,23], IJCAI'[22,23,24], AAAI'[22,24,25], NAACL'24, COLM'24, ICLR'24, ACL'23, KDD'23, NLPCC'24, ICIP'21, ICASSP'21.
Reviewer for TPAMI, JAIR, JAMIA, Bioinformatics, ACM Computing Surveys.

Teaching Experience

Spring 2022, TA for CS598 Deep Learning for Healthcare in UIUC, given by Prof. Jimeng Sun.
Summer 2020, TA for Optimization Models and Applications in TBSI, given by Prof. Laurent El Ghaoui.
Spring 2020, TA for Bayesian Learning and Data Analysis in TBSI, given by Prof. Ercan E. Kuruoglu.
Fall 2019, TA for Learning from Data in TBSI, given by Prof. Yang Li.

Invited Talk

Aug, 2025, Large Language Models for Clinical Trial Search, Recruitment, and Design, to appear in Joint Statistical Meetings
Mar, 2024, Bridging Biomedical Foundation Models, invited by Xuegong Lab@Tsinghua University.
Oct, 2023, Bridging Biomedical Foundation Models, invited by AI4Science@ByteDance.
Oct, 2023, Automate clinical trial design with large language models, invited by Medidata.
Aug, 2023, Medical Vision-Language Modeling, invited by CS department at University of Toronto.
July, 2023, Medical Vision-Language Modeling, invited by Stanford MedAI Group.
July, 2023, Unifying Large Language Models and Knowledge Graphs, invited by Amazon Titan Lab.
Apr, 2023, Medical Vision-Language Modeling from Unpaired Images and Texts, invited by National Center for Biotechnology Information (NCBI-NLM-NIH).
Feb, 2023, Advances in Transfer Learning for Tabular Prediction (CN), invited by AI Time.
Nov, 2022, How to do transfer learning and zero-shot learning on the tabular domain? (CN), invited by AI Time
April, 2022, Understanding Deep Learning via Information in Weights, invited by AI Time.
March, 2022, PAC-Bayes Information Bottleneck, invited by ReadPaper.