Invited Speakers

Andrew Howard

Staff Software Engineer at Google

Andrew Howard is a Staff Software Engineer at Google Research working on efficient computer vision models for mobile applications. He is the originator of Google’s popular MobileNet models.

Alexander Berg

Professor at University of North Carolina

Alex Berg's research concerns computational visual recognition. He has worked on general object recognition and detection in images, action recognition in video, human pose identification in images, image parsing, face recognition, image search, and machine learning for computer and human vision. He co-founded and co-organized the ImageNet Large Scale Visual Recognition Challenge, and co-organized the first Large-Scale Learning for Vision workshop. He is currently a research scientist at Facebook Inc and an associate professor (on leave) in computer science at UNC Chapel Hill. His work received the Marr Prize in 2013, the Everingham Prize for community contributions in 2016, and the Helmholtz Prize for work that stood the test of time in 2017.

Jonathan Ragan-Kelley

Assistant Professor at UC Berkeley

Jonathan is creator of the Halide language environment that is widely used for creating efficient mobile and embedded software implementations.

Prof. Song Han

Assistant Professor at MIT

Song Han received the Ph.D. degree from Stanford University advised by Prof. Bill Dallyat Stanford University. His research focuses on energy-efficient deep learning, at the intersection between machine learning and computer architecture. He proposed Deep Compression that can compress deep neural networks by an order of magnitude without losing the prediction accuracy. He designed EIE: Efficient Inference Engine, a hardware architecture that can perform inference directly on the compressed sparse model, which saves memory bandwidth and results in significant speedup and energy saving. His work has been featured by TheNextPlatform, TechEmergence, Embedded Vision and O’Reilly. He led research efforts in model compression and hardware acceleration for deep learning that won the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17. Before joining Stanford, Song graduated from Tsinghua University. I will join MIT EECS as an assistant professor starting summer 2018.

Pavlo Molchanov

Senior Research Scientist at NVIDIA Research

Pavlo Molchanov is a Senior Research Scientist at NVIDIA Research. He obtained PhD from Tampere University of Technology, Finland in the area of signal processing in 2014. His research is focused on methods for neural network acceleration, and designing novel human-computer interaction systems for in-car driver monitoring.

Panelists

TBD