About
I'm an incoming research scientist at Tiktok. My research focuses on building trustworthy AI systems, addressing safety and security challenges across the entire AI lifecycle.
Keywords:
AI Safety
Data Security
LLM Guardrails
FL Security
Data Management
Research Overview
My research addresses AI safety and security across multiple layers of AI systems, including:
Selected Works
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FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024 (KDD 2024)Link Invited Talk @ AI TIMEKick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof VerificationNDSS-PRISM 2026Veil: Storage and Communication Efficient Volume Hiding AlgorithmsProceedings of the ACM on Management of Data, 2023 (SIGMOD 2024)Link Invited Talk @ Cryptography Group, MongoDB Inc.An Iterative Scheme for Leverage-based Approximate AggregationIEEE 35th International Conference on Data Engineering (ICDE 2019)Don't Be a Pot Stirrer! Authorized Vector Data Retrieval via Access-Aware IndexingBridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM InferencesLink Invited Talk @ Ploutos AI CommunityFedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning SystemFL@FM-NeurIPS 2023 WorkshopFox-1: Open Small Language Model For Cloud And EdgeAlopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
Vision Papers
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Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
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LLM Multi-Agent Systems: Challenges and Open Problems
PhD Thesis
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Safeguarding AI Lifecycles in the Cloud: Secure Data Management for Data at Rest, in Transit, and in Use.