AI Summit West

CEO

You.com

Member of Technical Staff

OpenAI

Applied Scientist II

Amazon

Graph based neural networks have garnered a lot of attention over the past few years especially in Search and Recommendation technology. Large global web companies like Amazon, Facebook, LinkedIn and Google use graph based models both in production and offline to develop robust representations of their existing knowledge graph systems . However, large scale graph modeling brings in a host of new challenges both on the machine learning architecture front and the distributed computing front. Large real world graphs are noisy, have a power-law distribution, contain high-behavior hub nodes which cause load imbalance during training/inference on the one hand and non-behavioral nodes with cold-start issues on the other. In this talk, I’ll be going over the implementation of graphML models for industry scale data.

Product Leader, Entrepreneur, AI Leadership

Johnson & Johnson

Former Team Lead

OpenAI

Kenneth O. Stanley is currently deciding his next adventure after most recently leading a research team at OpenAI on the challenge of open-endedness. He was previously Charles Millican Professor of Computer Science at the University of Central Florida and was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he was head of Core AI research. He received a B.S.E. from the University of Pennsylvania and received a Ph.D. from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, novelty search, POET, and ELM algorithms, as well as the CPPN representation, among many others. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, quality diversity, and open-endedness. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, Galactic Arms Race, POET, and MCC. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 - 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, "Why Greatness Cannot Be Planned: The Myth of the Objective" (published by Springer), and has spoken widely on its subject.

Senior Machine Learning Engineer

LinkedIn

Data Manager, International | Deep Learning Engineer, NLP

Yummly | AI vs COVID-19 initiative

Uliana loves ML and Data. She received her B.A. in Comp. Science from the Technion. After graduation she worked as SW Engineer at IBM. At grad school her main focus was big data visualization. In collaboration with LANL she analyzed the formation of large scale structures in the Universe. At the onset of the pandemic Uliana joined AIvsCovid19 initiative as ML Engineer. She is fascinated by the power of the Transformers, and their impact on the NLP field.

Computer Science PhD

UC Berkeley

Dan Hendrycks is a PhD candidate at UC Berkeley, advised by Jacob Steinhardt and Dawn Song. His research aims to disentangle and concretize the components necessary for safe AI. His research is supported by the NSF GRFP and the Open Philanthropy AI Fellowship. Dan contributed the GELU activation function, the default activation in nearly all state-of-the-art ML models including BERT, Vision Transformers, and GPT-3

PhD, Director of Machine Learning

Toyota Research Institute

Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research - Inria Paris in 2012, has over 50 publications and patents in ML & Computer Vision (cf. Google Scholar), and his research is used in a variety of domains, including automated driving

Principal Research Scientist

Amazon Alexa

Lead Research Scientist

Snap

Data Science Manager - ML

DoorDash

Sudeep is a Data Science Manager at DoorDash, working within the Machine Learning team. He was previously a Machine Learning Area Lead at Netflix, where his main focus was on developing the next generation of machine learning algorithms to drive the personalization, discovery and search experience in the product. Apart from algorithmic work, he also takes a keen interest in data visualizations. Sudeep has had more than fifteen years of experience in machine learning applied to both large scale scientific problems, as well as in the industry. He holds a PhD in Astrophysics from Princeton University.

Research Scientist

Grammarly

Vipul Raheja is a Research Scientist at Grammarly. He works on developing robust and scalable Natural Language Processing and Deep Learning approaches for building the next generation of intelligent writing assistance systems, focused on improving the quality of written communication. His research interests lie at the intersection of text editing and controllable text generation. He holds a Masters in Computer Science from Columbia University, where he was affiliated with the Center for Computational and Learning Systems. He received a dual-degree in Computer Science and Engineering from IIIT Hyderabad.

Senior Research Manager

Autodesk

Karl is a Senior Research Manager at Autodesk Research, within the Autodesk AI Lab, focused on design software for manufacturing. He leads research projects in collaboration with industry and academia to solve difficult problems related to design, machine learning, and advanced manufacturing. He is passionate about research and building great teams and great products. He has worked extensively at the intersection of software and hardware, with 10+ years of experience in R&D for product.

Engineering Manager

Meta Reality Labs

Aayush Prakash is an Engineering Manager at Meta where he leads the machine learning team within the synthetic data organization in the Reality Labs. His group works on problems at the juncture of machine learning, computer vision and computer graphics. They tackle challenges in domain adaptation, neural rendering and other sim2real problems for mixed reality. Before joining Meta, he was the head of machine learning at synthetic data startup, AI Reverie. Prior to this, he worked at Nvidia where he spent 6 years on synthetic data research in computer vision.

Researcher

Berkeley Artificial Intelligence Research

Alishba Imran is a 19-year-old machine learning developer working on accelerating hardware/automation and energy storage.
Currently, Alishba is managing ML perception and prediction projects at Cruise and has done research at NVIDIA on RL-based simulation. Alongside this, she is publishing a book with O'Reilly on her work. Previously, Alishba led ML research at SJSU/the BLINC lab to reduce the cost of prosthetics from $10,000 to $700 and led neuro-symbolic AI for Sophia the Robot, the world's most human-like robot.

Senior Research  Engineer DeepMind

Industrial Task Suite: Deep Reinforcement Learning for Industrial Cooling System Control

This talk details the work in developing simulations for AI based industrial control . This is part of  his group's broader efforts to demonstrate real world energy savings in industrial cooling systems using Reinforcement Learning, published at NeurIPS RL in real world workshop. We cover the design choices for developing the `Industrial Task Suite` to enable research in this domain.

- Design choices for developing a suite of Industrial simulators for AI control.
Experimentation of  Hierarchical

- Reinforcement Learning on efficient chiller switching to minimize energy wastage

- Challenges in real-world Reinforcement Learning

At Google DeepMind, Praneet is a Senior Research Engineer, having previously worked on AI for Industrial Controls in its Applied group. Praneet serves as part of the Technical Program Committee for leading AI venues  such as NeurIPS, ICML and ICLR among others. He is a member of the Confederation of Indian Industry's Artificial Intelligence Task Force. Prior to DeepMind, he was a  Machine Learning Engineer at Google Cloud. He holds an MS in Electrical and Computer Engineering from Carnegie Mellon University and is an alumni of the Stanford Graduate School of Business Ignite program.

Associate Professor, Computer Science / Canada CIFAR AI Chair

University of British Columbia

Applied Scientist II

Google Brain

Dark Kingma is a Research Scientist at Google Brain. His research is on principled and scalable methods for machine learning, with a focus on generative models. His contributions include the Variational Autoencoder (VAE), the Adam optimizer, Glow, and Variational Diffusion Models, but please see Scholar for a more complete list. He obtained a PhD (cum laude) from University of Amsterdam in 2017, and was part of the founding team of OpenAI in 2015. Before that, he co-founded Advanza which got acquired in 2016.

Professor

UC Berkeley

Dawn Song is a professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and the blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, the blockchain, and smart contracts to the intersection of machine learning and security. She is also a serial entrepreneur. Previously, she was a faculty member at Carnegie Mellon University. She is the recipient of various awards, including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and best paper awards from top conferences in computer security and deep learning. She is ranked the most cited scholar in computer security (AMiner Award). Dawn holds a PhD from UC Berkeley.