SysML Conference

Schedule

Sunday, March 31

5:30pm - 8:30pm	Welcome Reception (dinner + drinks)

Monday, April 1

7:45am - 8:40am	Breakfast & Registration
8:40am - 9:00am	Opening Remarks
9:00am - 10:30am	Session I: Parallel & Distributed Learning I
	TicTac: Accelerating Distributed Deep Learning with Communication Scheduling Sayed Hadi Hashemi (University of Illinois at Urbana-Champaign), Sangeetha Abdu Jyothi (University of Illinois at Urbana-Champaign), Roy Campbell (University of Illinois at Urbana-Champaign) Priority-based Parameter Propagation for Distributed DNN Training Anand Jayarajan (University of British Columbia), Jinliang Wei (Carnegie Mellon University), Garth Gibson (Carnegie Mellon University), Alexandra Fedorova (University of British Columbia), Gennady Pekhimenko (University of Toronto) BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy Minsik Cho (IBM Research), Ulrich Finkler (IBM Research), David Kung (IBM Research) Beyond Data and Model Parallelism for Deep Neural Networks Zhihao Jia (Stanford University), Matei Zaharia (Stanford University), Alex Aiken (Stanford University)
10:30am - 11:00am	Coffee Break
11:00am - 12:30pm	Session II: Parallel & Distributed Learning II
	ParMAC: Distributed Optimisation of Nested Functions, with Application to Learning Binary Autoencoders Miguel A Carreira-Perpinan (UC Merced), Mehdi Alizadeh (UC Merced) 3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning Hyeontaek Lim (Google Brain), David G Andersen (Carnegie Mellon University), Michael Kaminsky (Intel Labs) Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD Jianyu Wang (Carnegie Mellon University), Gauri Joshi (Carnegie Mellon University) YellowFin and the Art of Momentum Tuning Jian Zhang (Stanford University), Ioannis Mitliagkas (University of Montreal)
12:30pm - 2:00pm	Lunch
2:00pm - 2:30pm	*Keynote: Compiling AI for the Edge* - Ofer Dekel (Microsoft Research)**
2:30pm - 4:00pm	Session III: Security and Privacy
	AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation Georgios Damaskinos (EPFL), El Mahdi El Mhamdi (EPFL), Rachid Guerraoui (EPFL), Arsany Guirguis (EPFL), Sébastien Rouault (EPFL) Towards Federated Learning at Scale: System Design Keith Bonawitz (Google), Hubert Eichner (Google), Wolfgang Grieskamp (Google), Dzmitry Huba (Google), Alex Ingerman (Google), Vladimir Ivanov (Google), Chloé Kiddon (Google), Jakub Konečný (Google), Stefano Mazzocchi (Google), Brendan McMahan (Google), Timon Van Overveldt (Google), David Petrou (Google), Daniel Ramage (Google), Jason Roselander (Google) Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification Qi Lei (UT Austin), Lingfei Wu (IBM T. J. Watson Research Center), Pin-Yu Chen (IBM Research), Alex Dimakis (UT Austin), Inderjit Dhillon (University of Texas at Austin), Michael J Witbrock (IBM Research) To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression Ilia Shumailov (University of Cambridge), Yiren Zhao (University of Cambridge), Robert Mullins (University of Cambridge), Ross Anderson (University of Cambridge)
4:00pm - 4:30pm	Coffee Break
4:30pm - 6:00pm	Session IV: Video Analytics
	Scaling Video Analytics on Constrained Edge Nodes Christopher Canel (Carnegie Mellon University), Thomas Kim (Carnegie Mellon University), Giulio Zhou (Carnegie Mellon University), Conglong Li (Carnegie Mellon University), Hyeontaek Lim (Carnegie Mellon University), David G Andersen (Carnegie Mellon University), Michael Kaminsky (Intel Labs), Subramanya R. Dulloor (ThoughtSpot) CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video Huizi Mao (stanford university), Taeyoung Kong (Stanford University), William J. Dally (Stanford University) AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling Ting-Wu Chin (Carnegie Mellon University), Ruizhou Ding (Carnegie Mellon University), Diana Marculescu (Carnegie Mellon University) FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning Paul Whatmough (ARM Research), Chuteng Zhou (Arm Research), Patrick Hansen (Arm Research), Shreyas Venkataramanaiah (Arizona State University), Jae-sun Seo (Arizona State University), Matthew Mattina (ARM Research)
6:00pm - 6:15pm	Demo Previews (see demo list here)
6:30pm - 9:00pm	Posters, Demos, & Reception (dinner + drinks)

Tuesday, April 2

7:45am - 9:00am	Breakfast & Registration
9:00am - 10:30am	Session I: Hardware
	Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator Tian Zhao (Stanford University), Yaqi Zhang (Stanford University), Kunle Olukotun (Stanford University) Restructuring Batch Normalization to Accelerate CNN Training Wonkyung Jung (Seoul National University), Daejin Jung (Samsung), Byeongho Kim (Seoul National University), Sunjung Lee (Seoul National University), Wonjong Rhee (Seoul National University), Jung Ho Ahn (Seoul National University) Bandana: Using Non-Volatile Memory for Storing Deep Learning Models Assaf Eisenman (Stanford University), Maxim Naumov (Facebook), Darryl Gardner (Facebook), Misha Smelyanskiy (Facebook), Sergey Pupyrev (Facebook), Kim Hazelwood (Facebook), Asaf Cidon (Stanford University), Sachin Katti (Stanford University) Mini-batch Serialization: CNN Training with Inter-layer Data Reuse Sangkug Lym (The University of Texas at Austin), Armand Behroozi (The University of Michigan), Wei Wen (Duke University), Ge Li (The University of Texas at Austin), Yongkee Kwon (University of Texas at Austin), Mattan Erez (UT Austin)
10:30am - 11:00am	Coffee Break
11:00am - 12:30pm	Session II: Hardware & Debugging
	Accurate and Efficient 2-bit Quantized Neural Networks Jungwook Choi (IBM Research), Swagath Venkataramani (IBM Research), Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson), Kailash Gopalakrishnan (IBM Watson), Zhuo Wang (IBM Research), Pierce Chuang Full Deep Neural Network Training on a Pruned Weight Budget Mieszko Lis (University of British Columbia), Maximilian Golub (UBC / Mercedes Benz Research & Dev.), Guy Lemieux (University of British Columbia) Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment Cedric Renggli (ETH Zurich), Bojan Karlaš (ETH Zürich), Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group"), Feng Liu (Huawei Technologies), Kevin Schawinski (Modulos AG), Wentao Wu (Microsoft Research), Ce Zhang (ETH) Data Validation for Machine Learning Neoklis Polyzotis (Google), Martin Zinkevich (Google), Sudip Roy (Google), Eric Breck (Google), Steven Whang (KAIST)
12:30pm - 2:00pm	Lunch
	1:15pm - 1:45pm: Reproducibility Breakout Session (Fisher Conference Center)
2:00pm - 2:30pm	*Keynote: How Do We Make AI Fair?* - Maya Gupta (Google AI)**
2:30pm - 4:00pm	Session III: Efficient Training & Inference
	Kernel machines that adapt to GPUs for effective large batch training Siyuan Ma (The Ohio State University), Mikhail Belkin (Ohio State University) Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications Dibakar Gope (Arm Inc.), Ganesh Dasika (Arm Research), Matthew Mattina (ARM Research) Optimizing DNN Computation with Relaxed Graph Substitutions Zhihao Jia (Stanford University), James Thomas (Stanford University), Todd Warszawski (Stanford University), Mingyu Gao (Stanford University), Matei Zaharia (Stanford University), Alex Aiken (Stanford University) Pytorch-BigGraph: A Large Scale Graph Embedding System Adam Lerer (Facebook AI Research), Ledell Wu (Facebook AI Research), Jiajun Shen (Facebook AI Research), Timothee Lacroix (Facebook AI Research), Luca Wehrstedt (Facebook AI Research), Abhijit Bose (Facebook AI Research), Alex Peysakhovich (Facebook AI Research)
4:00pm - 4:30pm	Coffee Break
4:30pm - 6:00pm	Session IV: Programming Models
	RLgraph: Modular Computation Graphs for Deep Reinforcement Learning Michael Schaarschmidt (University of Cambridge), Sven Mika (rlcore), Kai Fricke (Helmut Schmidt University), Eiko Yoneki (University of Cambridge) TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning Akshay Agrawal (Google Brain), Akshay Naresh Modi (Google Brain), Alexandre Passos (Google Brain), Allen Lavoie (Google Brain), Ashish Agarwal (Google Brain), Asim Shankar (Google Brain), Igor Ganichev (Google Brain), Josh Levenberg (Google Brain), Mingsheng Hong (Google Brain), Rajat Monga (Google Brain), Shanqing Cai (Google Brain) AutoGraph: Imperative-style Coding with Graph-based Performance Dan Moldovan (Google Inc.), James Decker (Purdue University), Fei Wang (Purdue University), Andrew Johnson (Google Inc.), Brian Lee (Google Inc.), Zack Nado (Google Inc.), D Sculley (Google), Tiark Rompf (Purdue University), Alexander B Wiltschko (Google Inc.) TensorFlow.js: Machine Learning for the Web and Beyond Daniel Smilkov (Google), Nikhil Thorat (Google), Yannick Assogba (Google), Charles Nicholson (Verily), Nick Kreeger (Google), Ping Yu (Google), Shanqing Cai (Google), Eric Nielsen (Google), David Soegel (Google), Stan Bileschi (Google), Michael Terry (Google), Ann Yuan (Google), Kangyi Zhang (Google), Sandeep Gupta (Google), Sarah Sirajuddin (Google), D Sculley (Google), Rajat Monga (Google), Greg Corrado (Google), Fernanda Viegas (Google), Martin M Wattenberg (Google)
6:00pm - 6:15pm	Closing Remarks & SysML 2020

Demonstrations

ML.NET: Machine Learning Toolkit for Software Developers
Matteo Interlandi (Microsoft), Sergiy Matusevych (Microsoft), Markus Weimer (Microsoft)
Horizontally Scalable ML Pipelines with a Feature Store
Alexandru A. Ormenisan (KTH), Mahmoud Ismail (KTH), Kim Hammar (Logical Clocks AB), Robin Andersson (Logical Clocks AB), Ermias Gebremeskel (Logical Clocks AB), Theofilos Kakantousis (Logical Clocks AB), Antonios Kouzoupis (Logical Clocks AB), Fabio Buso (Logical Clocks AB), Gautier Berthou (Logical Clocks AB, RISE AB), Jim Dowling (KTH, Logical Clocks AB), Seif Haridi (KTH, RISE AB)
On-chip FPGA Debug Instrumentation for Machine Learning Applications
Daniel Holanda Noronha (University of British Columbia), Ruizhe Zhao (Imperial College London), Jeff Goeders (Brigham Young University), Wayne Luk (Imperial College London), Steven J.E. Wilton (University of British Columbia)
VisualHyperTuner: Visual Analytics for User-Driven Hyperparameter Tuning of Deep Neural Networks
Heungseok Park (Clova AI Research), Jinwoong Kim (Clova AI Research), Minkyu Kim (Clova AI Research), Ji-Hoon Kim (Clova AI Research), Jaegul Choo (Korea University), Jung-Woo Ha (Clova AI Research), Nako Sung (Clova AI Research)
Using AutoTVM to Automatically Generate Deep Learning Libraries for Mobile Devices
Eddie Yan (University of Washington), Tianqi Chen (University of Washington), Lianmin Zheng (Shanghai Jiao Tong University), Ziheng Jiang (University of Washington), Thierry Moreau (University of Washington), Carlos Guestrin (University of Washington), Luis Ceze (University of Washington), Arvind Krishnamurthy (University of Washington)
Demonstration of Krypton: Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations
Supun Nakandala (UC San Diego), Arun Kumar (UC San Diego)
Demonstration of JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
Eunji Jeong (Seoul National University), Sungwoo Cho (Seoul National University), Gyeong-In Yu (Seoul National University), Joo Seong Jeong (Seoul National University), Dong-Jin Shin (Seoul National University), Byung-Gon Chun (Seoul National University)
Demonstration of the BlazeIt Visual Query Engine Using Specialized Neural Networks
Daniel Kang (Stanford University), Peter Bailis (Stanford University), Matei Zaharia (Stanford University)
Deepling: A Visual Interpretability System for Convolutional Neural Networks
Daniel Weidele (IBM Research), Hendrik Strobelt (IBM Research), Mauro Martino (IBM Research)
Demonstrating Customizable and Reusable Pipelines to Automate and Reproduce SysML Experiments
Grigori Fursin (cTuning Foundation, Dividiti)
TBD Suite: Benchmarking and Profiling Tools for DNNs
Geoffrey X. Yu (University of Toronto), Hongyu Zhu (University of Toronto), Anand Jayarajan (University of British Columbia), Bojian Zheng (University of Toronto), Abhishek Tiwari (University of Toronto), Gennady Pekhimenko (University of Toronto)
Guild AI: Simple Reproducibility in Machine Learning
Garrett Smith (Guild AI)
An Edge-Centric Scalable Intelligent Framework To Collaboratively Execute DNN
Jiashen Cao (Georgia Institute of Technology), FeiWu (Georgia Institute of Technology), Ramyad Hadidi (Georgia Institute of Technology), Lixing Liu (Georgia Institute of Technology), Tushar Krishna (Georgia Institute of Technology), Micheal S. Ryoo (Google Brain), Hyesoon Kim (Georgia Institute of Technology)
Demonstration of MLflow: A System to Accelerate the Machine Learning Lifecycle
Corey Zumar (Databricks), Andrew Chen (Databricks), Aaron Davidson (Databricks), Ali Ghodsi (Databricks), Sue Ann Hong (Databricks), Andy Konwinski (Databricks), Siddharth Murching (Databricks), Tomas Nykodym (Databricks), Paul Ogilvie (Databricks), Mani Parkhe (Databricks), Fen Xie (Databricks), Matei Zaharia (Databricks)
nGraph/PlaidML Demo for SysML 2019
Robert Earhart (Intel)

Steering Committee

Jennifer Chayes
Bill Dally
Jeff Dean
Michael I. Jordan
Yann LeCun
Fei-Fei Li
Alex Smola
Dawn Song
Eric Xing

Organizing Committee

General Chair
Ameet Talwalkar

Program Chairs
Virginia Smith
Matei Zaharia

Local Chair
Erika Strandberg

Publications Chair
Dimitris Papailiopoulos

Artifact Evaluation Chairs
Grigori Fursin
Gennady Pekhimenko

Sponsorship Chair
Sarah Bird

Whitepaper Chair
Chris Re, Alex Ratner

Publicity Chairs
Alex Dimakis
Vivienne Sze

Demo Chairs
Theodoros Rekatsinas
Ce Zhang

Website Chair
Siddhartha Sen

Program Committee

Dan Alistarh, Gustavo Alonso, Anima Anandkumar, David Andersen, Peter Bailis, Sarah Bird, Joseph Bradley, John Canny, Nicholas Carlini, Bryan Catanzaro, Eric Chung, William Dally, Christopher De Sa, Inderjit Dhillon, Alex Dimakis, Pradeep Dubey, Kayvon Fatahalian, Lise Getoor, Phillip Gibbons, Garth Gibson, Joseph Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Cho-Jui Hsieh, Furong Huang, Martin Jaggi, Prateek Jain, Kevin Jamieson, Yangqing Jia, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Dimitris Papailiopoulos, Gennady Pekhimenko, Alex Ratner, Theodoros Rekatsinas, Afshin Rostamizadeh, Hanie Sedghi, Siddhartha Sen, Evan Sparks, Ion Stoica, Vivienne Sze, Ameet Talwalkar, Madeleine Udell, Jaoquin Vanschoren, Shivaram Venkataraman, Markus Weimer, Andrew Wilson, Ce Zhang

Artifact Evaluation Committee

Matteo Interlandi, Anand Jayarajan, Sergiy Matusevych, Keshav Santhanam, Hongyu Zhu

Schedule

Sunday, March 31

Monday, April 1

Tuesday, April 2

Demonstrations

Location

Hotels

Steering Committee

Organizing Committee

Program Committee

Artifact Evaluation Committee

Sponsors

Contact

Conference Code of Conduct