王林楠 (Linnan Wang)

Office 351, CIT
Department of Computer Science
Brown University
Providence, RI 02906

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My Mission:

Artificial Intelligence is going to be the extension of our brains, in the same way as cars are the extension of our legs. Every day, it finds the best path for us to drive, it recommends excellent restaurants, products, 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.

Linnan's mission is democratizing AI, making it accessible to everybody. Any people with any knowledge, any companies of any sizes, any institutions at any places will fully benefit from fantastic AI solutions that are comparable to Google, Facebook, DeepMind with a few simple clicks.

Specifically, I'm keen on automating the design of neural architectures for a wide spectrum of applications using modern GPU accelerated Supercomputers. Please feel free to shoot an email to wangnan318@gmail.com if this also piques your interests.

I'm a Ph.D. student at 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. In addition to my advisor, I also work closely with Yiyang Zhao, Yuu Jinnai, Yi Yang, Wei Wu, George Bosilca, Jack Dongarra, and Maurice Herlihy.

in Submission:

Wang, Linnan, Yiyang Zhao, Yuu Jinnai
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Paper 

Wang, Linnan, Wei Wu, Yiyang Zhao, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca
SuperNeurons: Gradient Compression in the Distributed Training of Deep Neural Networks

Publications:

2018

Luo, Xi , Wei Wu, George Bosilca, Thananon Patinyasakdikul, Linnan Wang, Jack Dongarra
ADAPT: An Event-based Adaptive Collective Communication Framework
In Proceedings of 27th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC2018)
Paper 

Li, Ang, Weifeng Liu, Linnan Wang, Kevin Barker, Shuaiwen Leon Song
Warp-Consolidation: A Novel Execution Model for Modern GPUs
In Proceedings of the 2018 International Conference on Supercomputing (ICS2018)
Paper 

Carsten Binnig, et al.
Towards Interactive Curation & Automatic Tuning of ML Pipelines
SysML Conference (SysML2018)
Paper 

Ye, Jinmian, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
In Proceedings of 31th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2018)
Paper ·  Poster 

Wang, Linnan, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska
SuperNeurons:Dynamic GPU Memory Management for Training Deep Nonlinear Neural Networks
In Proceedings of the 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP2018)
Paper ·  Talk ·  Presentation  ·  Code

2017

Wang, Linnan, Yi Yang, Renqiang Min, and Srimat Chakradhar
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
Neural Networks (2017)
Paper ·  Patent

Zhao, Yiyang, Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Jinmian Ye, Wenqi Tang, and Zenglin Xu.
Efficient Communications in Training Large Scale Neural Networks
In Proceedings of the 25th ACM international conference on Multimedia (MM2017)
Paper

Li, Guangxi, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, and Michael Lyu
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
In 2017 International Joint Conference on Neural Networks (IJCNN2017)
Paper

2016

Wang, Linnan, Wei Wu, Zenglin Xu, Jianxiong Xiao, and Yi Yang
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous MultiGPU Computing
In Proceedings of the 2016 International Conference on Supercomputing (ICS2016)
Paper ·  SC Poster ·  Code ·  Presentation

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