Artificial Intelligence (AI) is going to be the extension of our brains, in the same way as cars are the extension of our legs. It has already been an indispensable part of our life. Every day, AI navigates us to places, answers our queries, and recommends restaurants and movies. Overall, it amplifies what we do, augmenting our memory, giving you instant knowledge, allowing us to concentrate on doing things that are properly human.
However, designing new AI models is still reserved for experts; and the goal of my research is to democratize AI, making it accessible to everybody, such that any person regardless of their prior experiences, and any company regardless of size can deploy sophisticated AI solutions with only a few simple clicks.
I'm a senior deep learning engineer at NVIDIA. I got my Ph.D. from the CS department of Brown University, advised by Prof.Rodrigo Fonseca.
Before Brown, I was a OMSCS student at Gatech while being a full time software developer at Dow Jones. I acquired my bachelor degree from
University of Electronic Science and Technology of China (UESTC) at the beautiful Qing Shui He campus in 2011. I also work closely with
Wei Wu, and
Works at Brown:
Automated Machine Learning: building an AI that builds AI.
AlphaX: inspired by AlphaGo, we build the very first NAS search algorithm based on Monte Carlo Tree Search (MCTS). We showed Neural Networks designed by AlphaX improve the downstream applications such as detection, style transfer, image captioning, and many others.
LA-MCTS: we find that different action space used in MCTS significantly affects the search efficiency, which motivates the idea of learning action space for MCTS (LA-MCTS). This project contains 1) a distributed version that is scalable to hundreds of GPUs to push SoTA results, and 2) a one-shot version that let you get a working result within a few GPU days. You can find the entire pipeline (search and training) for doing NAS here.
With LA-MCTS, we have achieved SoTA results on many CV tasks including CIFAR-10, ImageNet and detection. Besides, LA-MCTS also achieves strong performance in general black-box optimization and reinforcement learning benchmarks, in particular for high-dimensional problems.
Machine Learning System: building efficient distributed systems for AI.
SuperNeurons: this project builds a C++ Deep Learning framework, which features a dynamic GPU memory scheduling run-time to enable the neural network training far beyond the GPU DRAM capacity, and a FFT based gradient compression protocol for the efficient distributed DNN training.
BLASX: this project builds a level-3 BLAS library for heterogeneous multiGPUs. Due to the novel tile-cache design to avoid unnecessarily data-swapping, BLASX is 30% faster than commercial cuBLAS-XT from NVIDIA.
- Brown Fellowship (2017, 2020)
- PPoPP Travel Grant (2018)
- Finalist, Facebook Fellowship (2019)
- Top Reviewer, NeurIPS (2020)
- Reviewer of International Conference on Intelligent Robots and Systems (IROS)
- Reviewer of International Conference on Computer Vision (ICCV)
- Reviewer of Conference on Computer Vision and Pattern Recognition (CVPR)
- Reviewer of AAAI conference on Artificial Intelligence (AAAI)
- Reviewer of International Conference on Machine Learning (ICML)
- Reviewer of Neural Information Processing System (NIPS)
- Reviewer of International Conference on Learning Representations (ICLR)
- Reviewer of IEEE Transactions on Evolutionary Computation (TEC)
- Reviewer of Neural Architecture Search workshop (NAS-2020, NAS-2021) at ICLR
- Reviewer of International Journal of Intelligent Systems (IJIS)
- Reviewer of International Journal of Computer Vision (IJCV)
- Reviewer of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- Reviewer of IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- Reviewer of Journal of Machine Learning Research (JMLR)
- Reviewer of Computer Vision and Image Understanding (CVIU)
- Reviewer of Neural Computing and Applications (NCAA)
- Reviewer of Neural Networks
- Reviewer of IEEE Transactions on Emerging Topics in Computing (TETC)
- Reviewer of Journal of Computer Science and Technology (JCST)
- Reviewer of IEEE Transactions on Computers (TC)
- Reviewer of IEEE Transactions on Parallel and Distributed Systems (TPDS)
- External Research Collaborator, Facebook AI Research, Menlo Park, 2019.Sept ~ Present, supervised by Yuandong Tian
- Research Intern, Facebook AI Research, Menlo Park, 2019.Jan ~ 2019.May, supervised by Yuandong Tian
- Research Intern, Microsoft Research AI, Redmond, 2018.May ~ 2018.August, supervised by Yuxiong He
- Research Intern, NEC Labs, Princeton, 2016.Aug ~ 2017.Jan, supervised by Yi Yang
- Software Developer, Dow Jones, Princeton, 2014.Aug ~ 2016.Aug