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

Research Interests

• 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

• 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], 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

• 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.