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.

Privacy + Efficiency via Edge-Cloud Collaboration

[ICLR24 Spotlight] DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang

Privacy + Fairness via Federated Transfer

[KDD21] Federated Adversarial Debiasing for Fair and Transferable Representations

Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Hiroko Dodge, Zhangyang Wang, Jiayu Zhou

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].

Benchmark Trust under Compression

[ICML24] Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian Bartoldson, Ajay Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li

Benchmark Privacy in LLM Lifecycle

[VLDB24 ] LLM-PBE: Assessing Data Privacy in Large Language Models

Qinbin Li, Junyuan Hong, Chulin Xie, Jeffrey Tan, Rachel Xin, Junyi Hou, Xavier Yin, Zhun Wang, Dan Hendrycks, Zhangyang Wang, Bo Li, Bingsheng He, Dawn Song

Theoretical Risk Analysis

[NeurIPS23] Understanding Deep Gradient Leakage via Inversion Influence Functions

Haobo Zhang, Junyuan "Jason" Hong, Yuyang Deng, Mehrdad Mahdavi, Jiayu Zhou

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.

Protocol-Compliant Dementia Intervention

[ICLRW24] A-CONECT: Designing AI-based Conversational Chatbot for Early Dementia Intervention

Junyuan Hong, Wenqing Zheng, Han Meng, Siqi Liang, Anqing Chen, Hiroko H. Dodge, Jiayu Zhou, Zhangyang Wang

In-home Dementia Detection

[AD20] Detecting MCI using real-time, ecologically valid data capture methodology: How to improve scientific rigor in digital biomarker analyses

Junyuan Hong, Jeffrey Kaye, Hiroko H Dodge, Jiayu Zhou

Experiences

• Postdoctoral Fellow with Dr. Zhangyang Wang, VITA group@UT Austin, IFML and WNCG, 2023-Now.
• Research Intern with Dr. Lingjuan Lyu, Sony AI, 2022

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

Invited Talks & Guest Lectures

• ‘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’ @ BABUŠKA 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]

Services

• Organizers: GenAI4Health@NeurIPS (Lead Chair), 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

• 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

Mentored Students:

• Zhangheng Li (2023 - Now), Ph.D. student, University of Texas at Austin
  SaTML 2024 (first author), ICML 2024 (co-first author), ICLR 2024

• Runjin Chen (2023 - Now), Ph.D. student, University of Texas at Austin
  ICLR 2025 under review (first author)

• Gabriel Jacob Perin (2023 - Now), Undergraduate student, University of São Paulo, Brazil
  EMNLP 2024 (first author), ICLR 2025 under review (co-first author)

• Jeffrey Tan (2023 - 2024), Undergraduate student, University of California, Berkeley
  VLDB 2024 (Best Paper Nomination)

• Shuyang Yu (2020 - 2023), Ph.D. student, Michigan State University
  ICLR 2024 (first author), ICLR 2023 (spotlight; first author), NeurIPSW 2023 (first author), ICML 2023, KDD 2021

• Haobo Zhang (2022 - 2023), 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.

• Siqi Liang (2022 - 2023), Ph.D. student, Michigan State University
  KDDW 2023 (first author)