I am a joint postdoctoral fellow advised by Dr. Zhangyang Wang in the Institute for Foundations of Machine Learning (IFML) and Wireless Networking and Communications Group (WNCG), and also affiliated with the UT AI Health Lab as well as the Good System Challenge. I was recognized as one of the MLSys Rising Stars in 2024 and received a Best Paper Nomination at VLDB 2024. My work was covered by The White House, and MSU Office of Research and Innovation. Part of my work is funded by OpenAI Researcher Access Program.
Check my curricula vitae and feel free to drop me an email if you are interested in collaboration.
News
- 11/2024 Our research on engaging chatbot based on A-CONECT is supported by the NAIRR Pilot Program!
- 08/2024 LLM-PBE benchmark [VLDB24] is selected as the best paper finalist, which is covered by UT ECE News and is used for our NeurIPS 2024 LLM Privacy Challenge!
- 07/2024 Co-organize the GenAI4Health workshop at NeurIPS 2024.
- 07/2024 Co-organize The LLM and Agent Safety Competition at NeurIPS 2024!
- 07/2024 Our GenAI for Dementia Health project (A-CONECT) is supported by the OpenAI’s Researcher Access Program!
- 07/2024 DP-OPT [ICLR24] (private prompt tuning) is selected as Spotlight.
- 05/2024 I am selected as MLSys Rising Stars for my work in health and trustworthy ML, covered by UT ECE News.
- 03/2024 Invited talk on GenAI Privacy [SaTML24] at UT Good System Symposium.
- 03/2023 Our ILLIDAN Lab team wins the 3rd place in the U.S. PETs prize challenge, covered by The White House, and MSU Office of Research and Innovation.
- Healthcare
- Responsible AI
- Privacy
- Federated Learning
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PhD in CSE, 2023
Michigan State University (Advisor: Jiayu Zhou)
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MSc in Computer Science, 2018
University of Science and Technology of China
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BSc in Physics, minor in CS., 2015
University of Science and Technology of China
Research
My research vision is to harmonize, understand, and deploy Responsible AI: Optimizing AI systems that balance real-world constraints in computational efficiency, data privacy, and ethical norms through comprehensive threat analysis and the development of integrative trustworthy, resource-aware collaborative learning frameworks. Guided by this principle, I aim to lead a research group combining rigorous theoretical foundations with a commitment to developing algorithm tools that have a meaningful real-world impact, particularly in healthcare applications.
T1: Harmonizing Multifaceted Values in AI Trust.
Trust in AI is complex, reflecting the intricate web of social norms and values. Pursuing only one aspect of trustworthiness while neglecting others may lead to unintended consequences. For instance, overzealous privacy protection can come at the price of transparency, robustness, or fairness. To address these challenges, I have developed innovative collaborative learning approaches that balance key aspects of trustworthy AI, including privacy-preserving learning [FL4DM23&PETs23 ] with fairness guarantees [KDD21, TMLR23], enhanced robustness [AAAI23, ICLR23a], and provable computation and data efficiency [ICLR22, FAccT22, NeurIPS22a, ICLR24]. These methods are designed to create AI systems that uphold individual privacy while remaining efficient, fair, and accountable.
T2: Understanding Multi-faceted Emerging Risks in GenAI Trust.
As AI evolves from traditional machine learning to generative AI (GenAI), new privacy and trust challenges arise, yet remain opaque due to the complexity of AI models. My research aims to anticipate and address these challenges by developing theoretical frameworks that generalize privacy risk analysis across AI architectures [NeurIPS23], introducing novel threat models for generation-driven transfer learning [ICML23] and pre-trained foundation models [SaTML24], and leveraging insights from integrative benchmarks [VLDB24 , ICML24]. This deeper understanding of GenAI risks further informs the creation of collaborative or multi-agent learning paradigms that prioritize privacy [ICLR24] and safety [arXiv24].
T3: Deploying AI Aligned with Human Norms in Dementia Healthcare.
To ground my research in real-world impacts, I am actively exploring applications in healthcare, a domain where trust, privacy, and fairness are paramount. My projects include clinical-protocol-compliant conversational AI for dementia prevention [ICLRW24] and fair, in-home AI-driven early dementia detection [KDD21, AD20]. These initiatives serve as testbeds for responsible AI principles, particularly in ensuring ethical considerations like patient autonomy, data confidentiality, and equitable access to technology, while demonstrating AI’s potential to improve lives.
Publications
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Resume
Experience
- Postdoctoral Fellow with Dr. Zhangyang Wang, VITA group@UT Austin, IFML and WNCG, 2023-Now.
- Research Intern with Dr. Lingjuan Lyu, Sony AI, 2022
Awards
- Best Paper Finalist, VLDB 2024
- ML and Systems Rising Stars, ML Commons, 2024
- Top Reviewer, NeurIPS, 2023
- Research Enhancement Award, MSU, 2023
- The 3rd place in the U.S. PETs prize challenge, 2023
- Dissertation Completion Fellowship, MSU, 2023
- Carl V. Page Memorial Graduate Fellowship, MSU, 2021
Fundings
I am grateful that our research is supported by the multiple programs.
- Developing Engaging AI Chatbots to Enhance Senior Well-being, NAIRR Pilot Program (NAIRR240321), 2024-2025.
- Designing AI-based Conversational Chatbot for Early Dementia Intervention (A-CONECT), OpenAI Researcher Access Program, 2024-2025.
- GoodSystem Challenge, 2024-2025.
- IFML Postdoctoral Fellowship for my research in trustworthy LLMs, 2023-2024.
Media Coverage
- Texas ECE Student and Postdoc Named MLCommons Rising Stars, UT Austin ECE News, 2024
- At Summit for Democracy, the United States and the United Kingdom Announce Winners of Challenge to Drive Innovation in Privacy-enhancing Technologies That Reinforce Democratic Values, The White House, 2023
- Privacy-enhancing Research Earns International Attention, MSU Engineering News, 2023
- Privacy-Enhancing Research Earns International Attention, MSU Office Of Research And Innovation, 2023
Talks
- ‘GenAI-Based Chatbot for Early Dementia Intervention’ @ Rising Star Symposium Series, IEEE TCCN Special Interest Group for AI and Machine Learning in Security, September, 2024: [link]
- ‘Building Conversational AI for Affordable and Accessible Early Dementia Intervention’ @ AI Health Course, The School of Information, UT Austin, April, 2024: [paper]
- ‘Shake to Leak: Amplifying the Generative Privacy Risk through Fine-Tuning’ @ Good Systems Symposium: Shaping the Future of Ethical AI, UT Austin, March, 2024: [paper]
- ‘Foundation Models Meet Data Privacy: Risks and Countermeasures’ @ Trustworthy Machine Learning Course, Virginia Tech, Nov, 2023
- ‘Economizing Mild-Cognitive-Impairment Research: Developing a Digital Twin Chatbot from Patient Conversations’ @ BABUΕ KA FORUM, Nov, 2023: [link]
- ‘Backdoor Meets Data-Free Learning’ @ Hong Kong Baptist University, Sep, 2023: [slides]
- ‘MECTA: Memory-Economic Continual Test-Time Model Adaptation’ @ Computer Vision Talks, March, 2023: [slides] [video]
- ‘Split-Mix Federated Learning for Model Customization’ @ TrustML Young Scientist Seminars, July, 2022: [link] [video]
- ‘Federated Adversarial Debiasing for Fair and Transferable Representations’, @ CSE Graduate Seminar, Michigan State University, October, 2021: [slides]
- ‘Dynamic Policies on Differential Private Learning’ @ VITA Seminars, UT Austin, Sep, 2020: [slides]
Teaching
- Mentor@VRT-CHAT: Designing Reminiscence-Therapy Chatbots with Culturally-Sensitive Visual Stimulation for Mental Health, RAI4Ukraine Program, Center for Responsible AI at NYU, 2024
- Mentor@A-CONECT: Designing AI-based Conversational Chatbot for Early Dementia Intervention, Directed Reading Program (DiRP), UT Austin, 2024
- Mentor@Directed Reading Program (DiRP) on Trustworthy LLM, UT Austin, 2023
- Teaching Assistant@CSE 847: Machine Learning, MSU, 2021
- Teaching Assistant@CSE 404: Introduction to Machine Learning, MSU, 2020
Mentoring
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2023 - Now: Zhangheng Li, Ph.D. student, University of Texas at Austin
SaTML 2024 (first author), ICML 2024 (co-first author), ICLR 2024 -
2023 - Now: Runjin Chen, Ph.D. student, University of Texas at Austin
ICLR 2025 under review (first author) -
2023 - Now: Gabriel Jacob Perin, Undergraduate student, University of SΓ£o Paulo, Brazil
EMNLP 2024 (first author), ICLR 2025 under review (co-first author) -
2023 - 2024: Jeffrey Tan, Undergraduate student, University of California, Berkeley
VLDB 2024 (Best Paper Nomination) -
2020 - 2023: Shuyang Yu, Ph.D. student, Michigan State University
ICLR 2024 (first author), ICLR 2023 (spotlight; first author), NeurIPSW 2023 (first author), ICML 2023, KDD 2021 -
2022 - 2023: Haobo Zhang, Ph.D. student, Michigan State University
NeurIPS 2023 (first author), KDDW 2023 (first author)
Team member, 3rd place winner at US-UK PETs (Privacy-enhancing technologies) Prize Challenge, 2023. -
2022 - 2023: Siqi Liang, Ph.D. student, Michigan State University
KDDW 2023 (first author)
Services
- Organizers: GenAI4Health@NeurIPS, The Competition for LLM and Agent Safety 2024, The NeurIPS 2024 LLM Privacy Challenge, FL4Data-Mining Workshop@KDD 2023 (Lead Chair), FedKDD Workshop 2024 (Co-Lead Chair)
- External Reviewer: NeurIPS 22-23 (Top Reviewer, 2023), ICML 22-24, ICLR 23-24, KDD 22-24, ECML-PKDD 23, AISTATS 23, WSDM 22, AISTATS 22, AAAI 21-23, IJCAI 19, NeuroComputing, TKDD, TKDE, JAIR, TDSC, ACM Health
- Volunteer: KDD 18, 21