Sai Zhang
About Me
I am a research scientist at Meta Reality Lab. Previously I was a postdoctoral fellow at Harvard University, and I received my PhD degree of Computer Science at the same university. Before coming to Harvard, I received my bachelor's degree and master's degree in Electrical Engineering and Statistics from University of Toronto. My research interest lies in algorithm/hardware codesign for efficient deep neural network (DNN) implementation. I am also interested in Multi-Agent Reinforcement Learning (MARL) and its application.
My chinese name is 张赛骞
zhangs@g.harvard.edu
Selected Publications
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.
(Paper)
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H.T. Kung
28th IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022
(Paper)
Term Quantization: Furthering Quantization at Run Time
Sai Qian Zhang*, H.T. Kung*, Bradley McDanel* (equal contribution)
ACM/IEEE International Conference for High Performance Computing, Networking,
Storage and Analysis (SC), 2020
(Paper)
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
H.T. Kung*, Bradley McDanel*, Sai Qian Zhang* (equal contribution with alphabetical author list)
26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019
News
[3/2023] My high school mentee, Gavin an, has successfully finished his AI project on efficient reinforcement learning. A paper is submitted to JEI.
[9/2022] Start working at Meta!
[7/2022] Our paper “Hyperspherical Federated Learning" is accepted by ECCV 2022!
[7/2022] I gave an invited talk at AI times on multi-agent reinformcent learning and its applications.
[6/2022] I gave an invited talk at IEEE Dallas Circuits and Systems Conference (DCAS), 2022.
[4/2022] I started my AI memtorship at Veritas AI!
[2/2022] I started my postdoc study at Harvard!
[12/2021] I successfully defended my PhD!
[11/2021] Our paper “Learning Advanced Client Selection Strategy for Federated Learning" is accepted by AAAI 2022!
[10/2021] Our paper “FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding" is accepted by IEEE HPCA 2022!
[06/2021] Finished my internship at Microsoft, such a great place for research!
[03/2021] I started my virtual internship at Microsoft Research, Redmond.
[03/2021] I gave a guest lecture on DNN accelerator design on Havard Course ES201, hosted by Prof. Demba Ba.
[01/2021] One paper got accepted by IEEE International Symposium on Circuits & Systems (ISCAS), 2021.
[12/2020] I presented (virtually) our work "Succinct and Robust Multi-Agent Communication With Temporal Message Control" in NeurIPS'20.
[11/2020] I presented (virtually) our work "Term quantization: furthering quantization at run time" in SC'20.
[11/2020] Our paper “Training for Multi-resolution Inference Using Reusable Quantization Terms" is accepted by ACM ASPLOS 2021!
[09/2020] Our paper "Succinct and Robust Multi-Agent Communication With Temporal Message Control" is accepted by NeurIPS 2020!
[08/2020] I presented our work "Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN" in ICPP 2020.
[06/2020] Our paper "Term Revealing: Furthering Quantization at Run Time on Quantized DNNs" is accepted by ACM/IEEE SC 2020!
[05/2020] One paper "Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN" is accepted by ACM ICPP 2020!
[02/2020] One paper is accepted by IEEE Symposium on Security and Privacy (S&P) Deep Learning and Security workshop, 2020.
[12/2019] I attended NeurIPS and presented our work in Vancouver, Canada.
[11/2019] Our paper "RTN: Reparameterized Ternary Network" is accepted by AAAI, 2020!