2019 Speakers

The 2019 Embedded Vision Summit features more than 100 expert speakers covering a range of topics on computer vision and visual AI, organized as four tracks: Fundamentals, Technical Insights, Business Insights and Enabling Technologies. Our speakers and topics are below, or you can download a PDF version of the complete program here.

Keynote Speakers:

Ramesh Raskar

Associate Professor, MIT Media Lab

Making the Invisible Visible: Within Our Bodies, the World Around Us, and Beyond

Pete Warden

Staff Research Engineer, Google

The Future of Computer Vision and Machine Learning is Tiny

Speakers:
 

Manisha Agrawal

Software Applications Engineer, Texas Instruments

Machine Learning at the Edge in Smart Factories Using TI Sitara Processors

Felix Baum

Director of Product Management, AI Software, Qualcomm

Efficient Deployment of Quantized ML Models at the Edge Using Snapdragon SoCs

Walter Bell

3D Imaging Application Engineer, Infineon

REAL3 Time of Flight: A New Differentiator for Mobile Phones

Jeff Bier

Founder, Embedded Vision Alliance / President, BDTI

Visual AI Applications and Technologies: Trends and Opportunities

Petronel Bigioi

CTO, Imaging, Xperi

An Ultra-low-power Multi-core Engine for Inference on Encrypted DNNs

Ian Billing

Quality Assurance Manager, Teknique

Accelerating Smart Camera Time to Market Using a System-on-Module Approach

Ben Bodley

CEO, Teknique

Designing Your Next Vision Product Using a Systems Approach

Audrey Jill Boguchwal

Senior Product Manager, Samasource

Practical Approaches to Training Data Strategy

Mike Borza

Principal Security Technologist, Synopsys

Fundamental Security Challenges of Embedded Vision

Ilya Brailovskiy

Principal Engineer, Ring

Designing Home Monitoring Cameras for Scale

Paul Brasnett

Business Development Director Vision and AI, PowerVR, Imagination Technologies

Object Detection for Embedded Markets

Frank Brill

Design Engineering Director, Cadence

Portable Performance via the OpenVX Computer Vision Library: Case Studies

Gary Brown

Director of AI Marketing, Intel

Building AI Cameras with Intel® Movidius™ VPUs

Rudy Burger

Managing Partner, Woodside Capital

What’s Changing in Autonomous Vehicle Investments Worldwide – and Why?

Daniel Casner

Systems Engineer, Anki (former)

Selecting and Exploiting Sensors for Sensor Fusion in Consumer Robots

Li Chen

Data Scientist and Research Scientist, Intel

AI Reliability Against Adversarial Inputs

Bill Chou

Sr. Product Marketing Manager, MathWorks

Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB

Greg Chu

Sr. Computer Vision Scientist, GumGum

Creating Efficient, Flexible, and Scalable Cloud Computer Vision Applications: An Introduction

Gokcen Cilingir

AI Software Architect, Intel

AI Reliability Against Adversarial Inputs

Carlo Dal Mutto

CTO, Aquifi

Training Data for Your CNN: What You Need and How to Get It

Orr Danon

CEO, Hailo

Introducing Hailo-8: The Most Efficient Deep Learning Processor for Edge Devices

Gergely Debreczeni

Chief Scientist, Almotive

Distance Estimation Solutions for ADAS and Automated Driving

Pulin Desai

Vision Product Marketing, Cadence

Highly Efficient, Scalable Vision and AI Processors IP for the Edge

Sergey Dorodnicov

Software Architect, Intel

Applied Depth Sensing with Intel RealSense

Sylvain Dubois

Vice President, Business Development and Marketing, Crossbar

Memory-centric Hardware Acceleration for Machine Intelligence

Michael Fingeroff

HLS Technologist, Mentor

Using High-level Synthesis to Bridge the Gap Between Deep Learning Frameworks and Custom Hardware Accelerators

Shrinivas Gadkari

Design Engineering Director, Cadence

Fundamentals of Monocular SLAM

Jessica Gehlhar

Imaging Engineer

Introduction to Optics for Embedded Vision

Yury Gorbachev

Principal Engineer, Intel

How to Get the Best Deep Learning Performance with the OpenVINO Toolkit

Michael Gormish

Research Manager, Clarifai

Machine Learning-based Image Compression: Ready for Prime Time?

Sebastian Guenther

Host Systems Competence Center Lead, Allied Vision Technologies

Accessing Advanced Image Processing Feature Sets with Alvium Cameras Using a V4L2/GenICam Hybrid Driver

Moses Guttmann

CTO and Founder, allegro.ai

Optimizing SSD Object Detection for Low-power Devices

Tom Hackenberg

Principal Analyst, IHS Markit

Embedded Vision Applications Lead Way for Processors in AI: A Market Analysis of Vision Processors

Mike Henry

CEO/Founder, Mythic

Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling

Sandeep Hiremath

Product Manager, MathWorks

Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB

Burkhard Huhnke

Vice President of Automotive Strategy, Synopsys

Making Cars That See - Failure is Not an Option

Gian Marco Iodice

Staff Compute Performance Software Engineer, ARM

Performance Analysis for Optimizing Embedded Deep Learning Inference Software

Mehrsan Javan

Chief Technology Officer, Sportlogiq

Teaching Machines to See, Understand, Describe, and Predict Sports Games in Real Time

Changsoo Jeong

Head of Algorithm, Ring

Designing Home Monitoring Cameras for Scale

David Julian

CTO and Founder, Netradyne

Addressing Corner Cases in Embedded Computer Vision Applications

Ioannis Kakadiaris

Distinguished University Professor of Computer Science, University of Houston

AI-Powered Identity: Evaluating Face Recognition Capabilities

Vinod Kathail

Xilinx Fellow and Chief Architect, Xilinx

The Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability

László Kishonti

CEO, AImotive

Shifts in the Automated Driving Industry

Adam Kraft

Deep Learning Engineer, Orbital Insight

Challenges and Approaches for Extracting Meaning from Satellite Imagery

Ilya Krylov

Software Engineering Manager, Intel

Fast and Accurate RMNet: A New Neural Network for Embedded Vision

Vinod Kulathumani

Lead Computer Vision Scientist, TVision Insights

Accurately Measuring Viewer Attention for Maximum Marketing Impact Using Computer Vision

Robert Lay

Product Management, Computer Vision and Camera, Qualcomm

Snapdragon Hybrid Computer Vision/Deep Learning Architecture for Imaging Applications

Yuan Li

Director, Applied AI Lab, Horizon Robotics

High-performance DNNs at the Edge: Co-optimization of Model Architectures, Compiler and Accelerator

William Santana Li

Co-founder, Chairman and CEO, Knightscope

Visual AI Enables Autonomous Security: Interview with William Santana Li

Dwight Linden

COO and Co-founder, Entropix

Vision Tank Finalist

Anton Lokhmotov

CEO, Dividiti

Introducing MLPerf for Community-driven Benchmarking of Embedded Vision Systems

Yung-Hsiang Lu

Professor, Purdue University

Low-Power Computer Vision: Status, Challenges, and Opportunities

Phil Magney

Founder, VSI Labs

Improving the Safety and Performance of Automated Vehicles Through Precision Localization

Satya Mallick

Interim CEO, OpenCV.org

OpenCV: Current Status and Future Plans

Nikita Manovich

Senior Software Engineer, Intel

Data Annotation At Scale: Pitfalls and Solutions

Peter McGuinness

VP AI and Services, Mindtech Global

Can Simulation Solve the Training Data Problem?

Tim Merel

Managing Director, Digi-Capital

The Reality of Spatial Computing: What's Working in 2019 (and where it goes from here)

Peter Milford

President, Parallel Rules

Eye Tracking For The Future: The Eyes Have It

Austin Miller

Robotics Engineer, Robotic Materials

Vision Tank Finalist

Takeo Miyazawa

Founder and CEO, Magik Eye

Game Changing Depth Sensing Technique Enables Simpler, More Flexible 3D Solutions

Thies Möller

Technical Architect, Basler

Using Blockchain to Create Trusted Embedded Vision Systems

Clay D. Montgomery

Freelance Embedded Multimedia Developer

Building Complete Embedded Vision Systems on Linux – From Camera to Display

Bert Moons

Hardware Design Architect, Synopsys

5+ Techniques for Efficient Implementation of Neural Networks

Praveen Nayak

Tech Lead, Pathpartner

Using Deep Learning for Video Event Detection on a Compute Budget

Hussein Osman

Consumer Segment Manager, Lattice

Accelerate Adoption of AI at the Edge with Easy to Use, Low-power Programmable Solutions

Chris Osterwood

Founder & CEO, Capable Robot Components

Selecting the Right Imager for Your Embedded Vision Application

Minje Park

Deep Learning R&D Engineer, Intel

Object Trackers: Approaches and Applications

Facundo Parodi

Research and Machine Learning Engineer, Tryolabs

An Introduction to Machine Learning and How to Teach Machines to See

Mohammad Rastegari

CTO, Xnor.ai

Methods for Creating Efficient Convolutional Neural Networks

Vijay Janapa Reddi

Associate Professor, Harvard University

Introducing MLPerf for Community-driven Benchmarking of Embedded Vision Systems

Andrew Richards

CEO & Co-Founder, Codeplay

Can We Have Both Safety and Performance in AI for Autonomous Vehicles?

Ian Riches

Executive Director - Global Automotive Practice, Strategy Analytics

Automotive Vision Systems – Seeing the Way Forward

Barbara Rosario

CTO and Co-founder, Vyrill

Vision Tank Finalist

Ravi Sahu

Founder & CEO, Strayos

Mining Site Data Extraction Using 3D Machine Learning; Vision Tank Finalist

Nishita Sant

Manager Computer Vision, GumGum

Creating Efficient, Flexible, and Scalable Cloud Computer Vision Applications: An Introduction

Yoshio Sato

Sr. Product Marketing Manager, Industrial Business Unit, Renesas

Dynamically Reconfigurable Processor Technology for Vision Processing

Stephen Se

Research Manager, FLIR Systems

Deep Learning for Manufacturing Inspection Applications

Ronak Shah

Director, AI Marketing Strategy, Intel PSG

AI+: Combining AI and Other Critical Functions Using Intel FPGAs

Oleg Sinyavskiy

Director of Research & Development, Brain Corp

Sensory Fusion for Scalable Indoor Navigation

Raj Talluri

Senior Vice President and General Manager, Mobile Business Unit, Micron

Processor Options for Edge Inference: Options and Trade-offs

Bruce Tannenbaum

Manager, Technical Marketing, AI Applications, MathWorks

Three Key Factors for Successful AI Projects

Sébastien Taylor

Vision Technology Architect, Au-Zone

Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit

Steve Teig

Chief Technology Officer, Xperi

Beyond CNNs for Video: the Chicken vs. the Datacenter

Neil Trevett

President, Khronos Group

APIs for Accelerating Vision and Inferencing: an Industry Overview of Options and Trade-offs

Yohann Tschudi

Technology & Market Analyst, Yole Développement

AI Is Moving to the Edge – What’s the Impact on the Semiconductor Industry?

Peter Vajda

Research Manager, Facebook

Hardware-aware Deep Neural Network Design

Sugosh Venkataraman

Vice President Technology, Whirlpool

Enabling the Next Kitchen Experience Through Embedded Vision

Luca Verre

Co-founder and CEO, Prophesee

Neuromorphic Event-Based Vision: From Disruption to Adoption at Scale

Ben Weiss

Computer Vision Expert, Customer Solution, CEVA

Deploying Visual SLAM in Low-power Devices

John Williams

CTO and Co-Founder, Kudan

Commercial Grade SLAM Frameworks for Indoor and Outdoor Applications

Tom Wilson

VP Automotive, Graphcore

DNN Challenges and Approaches for L4/L5 Autonomous Vehicles

Bichen Wu

Graduate Student Researcher, EECS, University of California, Berkeley

Enabling Automated Design of Computationally Efficient Deep Neural Networks

Chen-Ping Yu

Co-founder and CEO, Phiar

Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms

Bing Yu

Senior Technical Manager/Architect

MediaTek’s Approach for Edge Intelligence

Peter Zatloukal

Vice President of Engineering, Xnor.ai

A Self-service Platform to Deploy State-of-the-art Deep Learning Models in Under 30 Minutes

Bo Zhu

CTO, BlinkAI

Vision Tank Finalist

Vision Tank Judges:

John Feland

Founder and CEO, Argus Insights

Lina Karam

Professor and Computer Engineering Director, Arizona State University

Vin Ratford

Executive Director, Embedded Vision Alliance
See you at the Summit! May 20-23 in Santa Clara, California!
Register today and reserve your hotel room!