Zifeng Wang
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Zifeng Wang 王子丰 | How to pronounce? Contact
Email: zifengw2 [at] illinois (dot) edu201 North Goodwin Avenue MC 258 Urbana, IL 61801-2302 |
Research Interests
I'm a member of Sunlab on AI for healthcare, led by Prof. Jimeng Sun. My research interests include machine learning and its application in healthcare and drug development, especially in the following areas:Prediction. Patient/trial outcome prediction with tabular/sequence/vision-language models.
Generation. Synthetic tabular/sequential EHR/EDC data generation and digital twin simulation.
Optimization. Improve clinical trial design via retrieval & generation, based on large language models (LLMs).
Explanation. Provide trustworthy explanations to support decision-making in clinical trial design.
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 (2/75) of Tsinghua University; National scholarship of graduate student (3/168).
B.S., Civil Engineering, Tongji University, 2014 - 2018
Supervised by Prof. Suzhen Li.
Award: Outstanding graduate student (4/40); Outstanding graduate thesis (3/168) of Tongji University; Merit student scholarship.
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
Yee Memorial Fellowship, 2023
NeurIPS Scholar Award, 2022
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], EMNLP'[22,23], IJCAI'[22,23], AAAI'[22,24], ICLR'24, ACL'23, KDD'23, ICIP'21, ICASSP'21.
Reviewer for TPAMI, Bioinformatics.
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, 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.
