Lianke Qin

Microsoft AI
firstname DOT lastname AT company DOT com

About Me

Currently, I work on the internal LLM pretraining system at Microsoft AI. Previously, I worked on multimodal and video generation model pretraining at Bytedance Seed-Infra-Training team. I am interested in building scalable, efficient, correct and fault-tolerant heterogeneous computing systems with user-friendly interfaces to facilitate the pursuit of machine intelligence.

I graduated from Shanghai Jiao Tong University with a B.Eng. in Computer Software Engineering. I was interested in the following topics when I was a Ph.D. student at UC Santa Barbara: 1) privacy-preserving algorithm design and system implementation with trusted hardware like SGX, differential privacy or modern cryptographic primitives, especially zero knowledge proofs. 2) build scalable heterogeneous systems to accelerate the generation of zero knowledge proof for extremely large circuits.

Papers

• A General Algorithm For Solving Rank-one Matrix Sensing (AISTATS 2024)
  Lianke Qin, Zhao Song, Ruizhe Zhang (Alphabetical order)
• A Sublinear Adversarial Training Algorithm (ICLR 2024)
  Yeqi Gao, Lianke Qin, Zhao Song and Yitan Wang (Alphabetical order)
• Adore: Differentially Oblivious Relational Database Operators (VLDB 2023)
  Lianke Qin, Rajesh Jayaram, Elaine Shi, Zhao Song, Danyang Zhuo and Shumo Chu
• An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization (AISTATS 2023)
  Lianke Qin, Zhao Song, Lichen Zhang and Danyang Zhuo (Alphabetical order)
• ZEN: An Optimizing Compiler for Verifiable, Zero-Knowledge Neural Network Inferences            
  Boyuan Feng, Lianke Qin, Zhenfei Zhang, Yufei Ding, Shumo Chu

Miscellaneous

Links

• Google Scholar
  
• GitHub
  
• LinkedIn