Yifei Wang's Homepage

Yifei Wang

PhD student

Brandeis University

About Me

Yifei Wang will receive her Ph.D. degree in Computer Science at Brandeis University. She earned her bachelor’s degree in Mathematics from Tsinghua University under the guidance of Prof. Chenglong Bao. Her research focuses on graph neural networks, multimodal learning, fairness, and generative models, with interdisciplinary work in AI4Science and healthcare.

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Interests

Multimodal Learning
Graph Neural Networks
Large Language Model
Generative AI
AI4Science
AI4Healthcare

Education

Ph.D. in Computer Science
Brandeis University
Bachelor of Mathematics
Tsinghua University

📚 My Research

I received my Ph.D. degree in Computer Science at Brandeis University. I blog about my publications and recent news. Great thanks to my undergraduate thesis advisor, Prof. Chenglong Bao from Tsinghua University, for inspiring my passion for research. Great thanks to Prof. Chuxu Zhang from the Univeristy of Connecticut for his valuable guidance and support on my PhD dissertation proposal. I am actively collaborating with those interested in cutting-edge AI topics and am always happy to assist junior students who are keen on pursuing research. Feel free to reach out via email if you’d like to collaborate or discuss research ideas. 😃

Featured Publications

Fairness

Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases

This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.

Jun 11, 2024

Multimodal Learning

Asymmetric contrastive multimodal learning for advancing chemical understanding

We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.

Nov 11, 2023

Graph Neural Network

On dyadic fairness: Exploring and mitigating bias in graph connections.

We theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks and accordingly introduced an algorithm for fair link prediction by adjusting the adjacency weight matrix to address the fairness-utility trade-off.

Jan 12, 2021

Recent Publications

Yifei Wang, Zhengyang Zhou, Liqin Wang, John Laurentiev, Peter Hou, Li Zhou, Pengyu Hong (2024). Counterpart Fairness--Addressing Systematic between-group Differences in Fairness Evaluation. NeurIPS workshop.

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Xinsong Du, Zhengyang Zhou, Yifei Wang, Ya-Wen Chuang, Richard Yang, Wenyu Zhang, Xinyi Wang, Rui Zhang, Pengyu Hong, David W. Bates, Li Zhou (2024). Generative Large Language Models in Electronic Health Records for Patient Care Since 2023: A Systematic Review. medRxiv.

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Yifei Wang, Liqin Wang, Zhengyang Zhou, John Laurentiev, Joshua R Lakin, Li Zhou, Pengyu Hong (2024). Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases. JBI.

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Yunrui Li, Zahra Zarei, Phu N Tran, Yifei Wang, Aparna Baskaran, Seth Fraden, Michael F Hagan, Pengyu Hong (2024). A machine learning approach to robustly determine director fields and analyze defects in active nematics. Soft Matter.

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Hao Xu, Yifei Wang, Yunrui Li, Pengyu Hong (2023). Asymmetric contrastive multimodal learning for advancing chemical understanding. arXiv.

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Recent & Upcoming Talks

Graph Neural Network

Oral Presentation of My Dissertation Proposal at CS PhD Retreat

Better AI in Scientific Applications: Interdisciplinary Approaches for Advancing Representational Learning

May 29, 2024

Fairness

Oral Presentation at AMIA 2023 Annual Symposium

In Session 14: Application of Informatics in Health Equity - "All the Things You Are" at AMIA.

Nov 13, 2023

Graph Neural Network

Poster Presentation at Boston Symmetry Day 2023

Motif-based Graph Representation Learning for Molecules

Apr 7, 2023

🎉 Congratulations to Andrew Nguyen for receiving the Kukin Moskowitz Prize for Excellence 2024.

Apr 19, 2024

I am delighted to mentor Andrew Nguyen on his undergraduate thesis and nominate him for the 2024 Student Involvement Prize and Award at Brandeis University. It’s wonderful to see Andrew being recognized with the Kukin Moskowitz Prize for Excellence.

Apr 19, 2024