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个人简介
My research is in data privacy and trustworthy machine learning. I am interested in designing methods to quantitatively measure the privacy risks of data processing algorithms, and build scalable schemes for generalizable machine learning models that are also privacy-preserving, robust, interpretable, and fair. Our research is on analyzing the trade-offs between different pillars of trust in machine learning for practical scenarios, and on resolving such conflicts with rigorous mathematical guarantees. We are currently working on many interesting problems in this domain, including trustworthy federated learning, differential privacy for machine learning, fairness versus privacy in machine learning, privacy-aware model explanations, privacy-preserving data synthesis, and quantifying privacy risks of data analytics. Our research is supported by research awards and grants from Intel, Google, Facebook, VMWare, NEC, Huawei, AI Singapore, NUS, Singapore MoE, and NRF.
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论文共 103 篇作者统计合作学者相似作者
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Conference of the European Chapter of the Association for Computational Linguisticspp.278-293, (2024)
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ICLR 2024 (2023)
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ICLR 2023 (2023)
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CoRR (2023)
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ICLR 2023 (2023): 1789-1800
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