2020 Sessions

We’re putting together our 2020 schedule of 100+ inspirational thought leaders in computer vision and visual AI right now.

More speakers will be announced soon—please stay tuned!

(Interested in speaking in 2020? Click here to propose a presentation!)

Main Summit (Tue & Thu, Sep 15 - 24):

A Computer Vision-Based Personal Trainer That Runs On Your Phone
By Roland Memisevic CEO, Chief Scientist & Founder, Twenty BN

Technical Insights I
A New Golden Age for Computer Architecture: Processor Innovation to Enable Ubiquitous AI
By David Patterson Professor, UC Berkeley

Keynote
A Scalable, High Performance, Power Efficient DNN Processor for AI Inference at the Edge
By Pulin Desai Vision and AI Product Marketing, Cadence

Enabling Technologies I
Accuracy: Beware of Red Herrings and Black Swans
By Steve Teig CEO, Perceive

Technical Insights I
An Introduction to Machine Learning and How to Teach Machines to See
By Leonardo Piñeyro Machine Learning Engineer and Researcher, Tryolabs

Fundamentals
Can You Patent Your AI-Based Invention?
By Thomas Lebens Partner, Fitch Even

Business Insights
Can you see what I See? The Power of Deep Learning
By Scott Thibault President and Founder, StreamLogic

Fundamentals
Challenges and Approaches for Cascaded DNNs: A Case Study of Face Detection for Face Verification
By Ana Salazar Senior Research Manager, Imagination Technologies

Technical Insights I
CMOS Image Sensors: A Guide to Building the Eyes of a Vision System
By Jon Stern Director of Optical Systems, GoPro

Fundamentals
Dealing with the Real World: Optimizing Vision Models to Handle Messy Input Streams
By Arshak Navruzyan CEO, platform.ai

Technical Insights I
Deep Learning for Manufacturing Inspection: Case Studies
By Stephen Se Senior Research Manager, FLIR Systems, Inc.

Technical Insights I
Democratizing Computer Vision and Machine Learning with Open, Royalty-Free Standards: OpenVX
By Kiriti Nagesh Gowda MTS Engineer & Chair of OpenVX, AMD

Technical Insights I
Deploying AI Software to Embedded Devices Using Open Standards
By Andrew Richards Founder and CEO, Codeplay

Fundamentals
Deploying Deep Learning Applications on FPGAs with MATLAB
By Ram Cherukuri Product Marketing Manager, MathWorks

Enabling Technologies I
Designing Bespoke CNNs for Target Hardware
By Woonhyun Nam Director, Algorithms, StradVision

Technical Insights I
Driver Monitoring Systems: Present and Future
By Petronel Bigioi CTO, Imaging, Xperi

Enabling Technologies I
Embedded Vision: Where’s The Money?
By Rudy Burger Managing Partner, Woodside Capital

Business Insights
Ergo: Perceive’s Chip – Data Center-Class Inference in Edge Devices at Ultra-Low Power
By Steve Teig CEO, Perceive

Enabling Technologies I
Federated Edge Computing System Architectures
By Vaidyanathan Krishnamoorthy Edge Inference Solutions Architect, Intel

Enabling Technologies I
Feeding the World Through Embedded Vision
By Travis J. Davis Delivery Manager - Automation Delivery, John Deere

Technical Insights I
Getting Efficient DNN Inference Performance: Is It Really About the TOPS?
By Gary Brown Director of AI Marketing, Intel

Enabling Technologies I
How 5G is Pushing Processing to the Edge
By Dan Picker Chief Technology Officer, Inseego Corp

Business Insights
How to Create Your Own AI-Enabled Camera Solution in Days
By Carsten Traupe Director, Product Management, IDS Imaging

Enabling Technologies I
Image-based Deep Learning for Manufacturing Fault Condition Detection
By Jake Lee Principal Engineer, Head of Machine Learning Group, Samsung

Technical Insights I
Imaging Systems for Applied Reinforcement Learning Control
By Damas W. Limoge R&D Engineer, Nanotronics

Technical Insights I
Implementing an SSD-Based Object Detection Algorithm on a $3 Microcontroller
By Greg Lytle VP Engineering, Au-Zone

Technical Insights I
Improving Power Efficiency for Edge Inferencing with Memory Management Optimizations
By Nathan Levy Project Leader, Samsung

Technical Insights I
Introduction to the TVM Open Source Deep Learning Compiler Stack
By Luis Ceze Professor, University of Washington | Co-founder and CEO, OctoML, Inc.

Technical Insights I
Joint Regularization of Activations and Weights for Efficient Neural Network Pruning
By Zuoguan Wang Senior Algorithm Manager, Black Sesame Technologies

Technical Insights I
Khronos Standard APIs for Accelerating Vision and Inferencing
By Neil Trevett President, Khronos, and VP Developer Ecosystems, NVIDIA

Technical Insights I
Machine Learning for the Real World: What is Acceptable Accuracy, and How Can You Achieve It?
By Tim Hartley Senior Product Manager, Arm

Business Insights
Machine-Learning-Based Perception on a Tiny, Low-Power FPGA
By Hoon Choi Fellow, Lattice

Enabling Technologies I
Market Trends in Automotive Perception: From Insect-Like to Human-Like Intelligence
By Pierre Cambou Principal Analyst, Yole Développement

Business Insights
MLPerf: An Industry Standard Performance Benchmark Suite for Machine Learning
By Carole-Jean Wu Research Scientist/Associate Professor, Facebook/ASU

Technical Insights I
Modern SoCs for Consumer Robotics and AIoT
By Zhe Zhang Founder and CEO, Trifo

Technical Insights I
NGCodec: A Start-Up’s Journey from Launch to Acquisition
By Oliver Gunasekara Independent Consultant

Business Insights
Parallelizing Machine Learning Applications in the Cloud with Kubernetes: A Case Study
By Rajy Meeyakhan Rawther PMTS Software Architect, Machine Learning Software Engineering, AMD

Technical Insights I
Practical DNN Quantization Techniques and Tools
By Raghuraman Krishnamoorthi Software Engineer, Facebook

Technical Insights I
Practical Guide to Implementing Deep Neural Network Inferencing at the Edge
By Toly Kotlarsky Distinguished Member Technical Staff, R&D, Zebra Technologies

Fundamentals
Recent Advances in Post-training Quantization
By Alexander Kozlov Deep Learning R&D Engineer, Intel

Technical Insights I
Reinforcement Learning: a Practical Introduction
By Joe Booth VP of Engineering, Orions Systems, Inc

Fundamentals
Safer and More Efficient Intersections with Computer Vision
By Jeff Price VP & General Manager, Cubic | GRIDSMART

Technical Insights I
Smarter Manufacturing with Intel’s Deep Learning-Based Machine Vision
By Tara K. Thimmanaik Solutions Architect, Intel

Enabling Technologies I
Structures as Sensors: Smaller-Data Learning in the Physical World
By Pei Zhang Associate Research Professor, Carnegie Mellon University

Technical Insights I
Tackling Extreme Visual Conditions for Autonomous UAVs In the Wild
By Hayk Martiros Head of Autonomy, Skydio

Technical Insights I
Using an ISP for Real-time Data Augmentation
By Timofey Uvarov Camera System Lead, Pony.AI

Technical Insights I
Using Learning at the Edge to Deliver Business Value
By Vera Serdiukova Product Manager, Advanced AI, LG Electronics

Business Insights
Video Activity Recognition with Limited Data for Smart Home Applications
By Hongcheng Wang Director, Technical Research, Comcast

Technical Insights I

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