Incoming Research Scientist at Meta and a PhD student under the supervision of Prof. Mario Fritz at CISPA, Germany.

My research focuses on efficient and privacy-preserving machine learning, with the goal of making AI models more accessible to the general public. Specifically, my work involves privacy-preserving federated learning, domain adaptation, and adapting generative models, such as Generative Adversarial Networks (GANs) and large language models (LLMs), to new tasks. My work has received over 500 citations according to Google Scholar.

Before joining CISPA, I was a research intern at the Max-Planck Institute for Informatics, Germany, and received my Master’s degree under the supervision of Prof. Wen-Hsiao Peng and Prof. Wei-Chen Chiu in National Yang Ming Chiao Tung University, Taiwan.

News

• [Feb. 2024] Our work DP-2Stage is accepted by TMLR.
• [Sep. 2024] Our work LM-GC is accepted by NeurIPS 2024.
• [Jun. 2024] Our work FedLAP-DP is accepted by PETS 2024.
• [Aug. 2024] I am presenting our work LM-GC at ELSA General Assembly Meeting 2024.
• [Jan. 2024] I am giving a talk on privacy-presering FL via loss approximation for medical applications at National Yang Ming University.


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[Jan. 2024] I am giving a talk on privacy-presering ML for medical applications at Taipei Veterans General Hospital.
[Jan. 2023] I am giving a talk on efficient federated learning at National Yang Ming University.
[Oct. 2022] Our work ProgFed is accepted by ICML 2022.
[Dec. 2021] Our work CosSGD is accepted by NeurIPS Workshop on New Frontiers in Federated Learning, 2022
[Jun. 2021] Invited to give a talk on HijackGAN at Responsible Computer Vision, CVPRw 2021.
[Mar. 2021] Our work HijackGAN is accepted by CVPR 2021.
[Mar. 2021] Our work MIMO-VRN is accepted by CVPR 2021.
[Mar. 2021] Our work InfoScrub is accepted by FA.DE.TR.CV, CVPRw 2021.

Publications [Google Scholar] [DBLP]