Speakers

Ryan Calo, Assistant Professor, University of Washington

Ryan Calo is an assistant professor at the University of Washington School of Law and a former research director at CIS. A nationally recognized expert in law and emerging technology, Ryan’s work has appeared in the New York Times, the Wall Street Journal, NPR, Wired Magazine, and other news outlets. Ryan serves on several advisory committees, including the Electronic Frontier Foundation, the Electronic Privacy Information Center, and the Future of Privacy Forum. He co-chairs the American Bar Association Committee on Robotics and Artificial Intelligence and serves on the program committee of National Robotics Week.

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Alex Korbonits, Data Scientist Analyst, Remitly

Alex Korbonits is a Data Scientist at Remitly, Inc., where he works extensively on feature extraction and putting machine learning models into production. Outside of work, he loves Kaggle competitions, is diving deep into topological data analysis, and is exploring machine learning on GPUs. Alex is a graduate of the University of Chicago with degrees in Mathematics and Economics.

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Serena Yeung, PhD Student, Stanford

Serena is a Ph.D. student in the Stanford Vision Lab, advised by Prof. Fei-Fei Li. Her research interests are in computer vision, machine learning, and deep learning. She is particularly interested in the areas of video understanding, human action recognition, and healthcare applications. She interned at Facebook AI Research in Summer 2016.

Before starting her Ph.D., she received a B.S. in Electrical Engineering in 2010, and an M.S. in Electrical Engineering in 2013, both from Stanford. She also worked as a software engineer at Rockmelt (acquired by Yahoo) from 2009-2011.

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Tianqi Chen, Computer Science PhD Student, University of Washington

Tianqi holds a bachelor’s degree in Computer Science from Shanghai Jiao Tong University, where he was a member of ACM Class, now part of Zhiyuan College in SJTU. He did his master’s degree at Changhai Jiao Tong University in China on Apex Data and Knowledge Management before joining the University of Washington as a PhD. He has had several prestigious internships and has been a visiting scholar including: Google on the Brain Team, at Graphlab authoring the boosted tree and neural net toolkit, at Microsoft Research Asia in the Machine Learning Group, and the Digital Enterprise Institute in Galway Ireland. What really excites Tianqi is what processes and goals can be enabled when we bring advanced learning techniques and systems together. He pushes the envelope on deep learning, knowledge transfer and lifelong learning. His PhD is supported by a Google PhD Fellowship.

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Hanie Sedghi, Research Scientist at Allen Institute for Artificial Intelligence

Hanie Sedghi is a Research Scientist at Allen Institute for Artificial Intelligence (AI2). Her research interests include large-scale machine learning, high-dimensional statistics and probabilistic models. More recently, she has been working on inference and learning in latent variable models. She has received her Ph.D. from University of Southern California with a minor in Mathematics in 2015. She was also a visiting researcher at University of California, Irvine working with professor Anandkumar during her Ph.D. She received her B.Sc. and M.Sc. degree from Sharif University of Technology, Tehran, Iran.

Abstract summary

Beating Perils of Non-convexity:Guaranteed Training of Neural Networks using Tensor Methods:
Neural networks have revolutionized performance across multiple domains such as computer vision and speech recognition. However, training a neural network is a highly non-convex problem and the conventional stochastic gradient descent can get stuck in spurious local optima. We propose a computationally efficient method for training neural networks that also has guaranteed risk bounds. It is based on tensor decomposition which is guaranteed to converge to the globally optimal solution under mild conditions. We explain how this framework can be leveraged to train feedforward and recurrent neural networks.

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