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Posters (all accepted papers) + Break
Jianyu Wang · Denis Gudovskiy · Ziheng Jiang · Michael Kaufmann · Andreea Anghel · James Bradbury · Nikolas Ioannou · Nitin Agrawal · Emma Tosch · Gyeongin Yu · Keno Fischer · Jarrett Revels · Giuseppe Siracusano · Yaoqing Yang · Jeff Johnson · Yang You · Hector Yuen · Chris Ying · Honglei Liu · Nikoli Dryden · Xiangxi Mo · Yangzihao Wang · Amit Juneja · Micah Smith · Qian Yu · pramod gupta · Deepak Narayanan · Keshav Santhanam · Tim Capes · Abdul Dakkak · Norman Mu · Ke Deng · Liam Li · Joao Carreira · Luis Remis · Deepti Raghavan · Una-May O'Reilly · Amanpreet Singh · Mahmoud (Mido) Assran · Eugene Wu · Eytan Bakshy · Jinliang Wei · Michael Innes · Viral Shah · Haibin Lin · Conrad Sanderson · Ryan Curtin · Marcus Edel

Fri Dec 07 11:55 AM -- 12:40 PM (PST) @
in MLSys: Workshop on Systems for ML and Open Source Software »

Author Information

Jianyu Wang (Carnegie Mellon University)
Denis Gudovskiy (Panasonic Beta Research Lab)
Ziheng Jiang (University of Washington)
Michael Kaufmann (IBM Research, Karlsruhe Institute of Technology)
Andreea Anghel (IBM Research)
James Bradbury (Google Brain)
Nikolas Ioannou (IBM Research)
Nitin Agrawal (Samsung Research)
Emma Tosch (University of Massachusetts Amherst)
Gyeongin Yu (Seoul National University)
Keno Fischer (Julia Computing Inc)
Jarrett Revels (MIT)
Giuseppe Siracusano (NEC Laboratories Europe)
Yaoqing Yang (Carnegie Mellon University)
Jeff Johnson (Facebook AI Research)
Yang You (UC Berkeley)
Hector Yuen (Facebook)
Chris Ying (Google Brain)
Honglei Liu (Facebook Conversational AI)
Nikoli Dryden (University of Illinois at Urbana-Champaign)
Xiangxi Mo (UC Berkeley)
Yangzihao Wang (Tencent Inc.)

Yangzihao is a software engineer at Tencent (Beijing) working on AI platform and AutoML. Before that he worked as a software engineer at Google Brain on Tensorflow. He graduated from UC Davis in December 2016. During his PhD years, he was fortunate to work with Prof. John Owens on various research topics: 1) structure of parallelism and locality in irregular algorithms such as graph algorithms on the GPU; 2) parallel programming model for graph analytics; and 3) large-scale graph processing and data analysis system. He also did internships at AMD Research, DARPA, and Google. Before UC Davis, Yangzihao received his B.E. degree in Computer Science and M.E. degree in Software Engineering both from Beijing University of Aeronautics and Astronautics. During his Master years, Yangzihao worked on several projects on water simulation, collision detection, and distributed rendering system.

Amit Juneja (IBM)
Micah Smith (MIT)
Qian Yu (University of Southern California)
pramod gupta (Google DeepMind)
Deepak Narayanan (Stanford University)
Keshav Santhanam (Stanford University)
Tim Capes (SAIC Toronto)
Abdul Dakkak (UIUC)
Norman Mu (UC Berkeley)
Ke Deng (Microsoft)
Liam Li (Carnegie Mellon University)
Joao Carreira (UC Berkeley)

UC Berkeley RISELab PhD Student

Luis Remis (Intel Labs)
Deepti Raghavan (Stanford University)
Una-May O'Reilly (Massachusetts Institute of Technology)
Amanpreet Singh (Facebook AI Research)
Mahmoud (Mido) Assran (Facebook AI Research / McGill University)

## Byte-sized bio PhD Student, supervised by Prof. Michael Rabbat, working on developing multi-agent optimization algorithms for large-scale and distributed machine learning. > We're all here on this earth to help others, what on earth the others are here for, I have no idea. > -- W.H. Auden ## More about research Many practical machine learning systems are distributed across multiple machines... either because the data is naturally distributed, or because of the scale of the task. I'm really interested in developing distributed optimization algorithms for training large-scale machine learning systems; whether that be a high performance computing cluster, a controlled production environment, or a multi-agent system. So far, my research has utilized tools from the control systems literature and adapted them to the machine learning setting with theoretical convergence guarantees and strong empirical evidence. Here are some of our recent works :) [1]. Stochastic Gradient Push for Distributed Deep Learning [2]. Asynchronous Subgradient Push [3]. An Empirical Comparison of Multi-Agent Optimization Algorithms

Eugene Wu (Columbia University)
Eytan Bakshy (Facebook)
Jinliang Wei (Carnegie Mellon University)
Michael Innes (Julia Computing)
Viral Shah (Julia Computing, Inc.)
Haibin Lin (Amazon.com Inc)
Conrad Sanderson (University of Queensland)
Ryan Curtin (RelationalAI)
Marcus Edel (Free University of Berlin)

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