Deep Learning Summit
Speakers

Richard Socher is the founder and CEO of You.com, the search engine that puts you in control — Your sources. Your time. Your privacy. Richard previously served as the Chief Scientist and EVP at Salesforce. Before that, Richard was the CEO/CTO of AI startup MetaMind, acquired by Salesforce in 2016. Richard received his Ph.D. in computer science at Stanford, where he was recognized for his groundbreaking research in deep learning and NLP. He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo! Key Scientific Challenges Award, a Microsoft Research Ph.D. Fellowship in 2012 and a 2013 "Magic Grant" from the Brown Institute for Media Innovation, and the 2014 GigaOM Structure Award. He also served as an adjunct professor in the computer science department at Stanford. Outside of work, Richard enjoys paramotor adventures, traveling, and photography.

Associate Professor, Computer Science / Canada CIFAR AI Chair

University of British Columbia

• I summarize our video pretraining (VPT) work, described in an OpenAI blog post: https://openai.com/blog/vpt

• We extend the GPT paradigm of performing unsupervised training in large models on internet-scale data to learning from online video

• Like GPT, VPT trains on internet data and can be fine-tuned with reinforcement learning: it performs at human-level on previously unsolvable tasks, here using a computer to do tasks that take humans over 20 minutes and over 24,000 actions 

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

How LinkedIn uses Deep Learning to Help you Find People

If you’ve used LinkedIn, you’re probably familiar with one of its oldest capabilities: searching for other people. Sometimes you find people you know, sometimes it’s someone new. Regardless of who you’re looking for, LinkedIn wants to connect you to people you find the most interesting. How does LinkedIn do that?
In this presentation, we will take a tour of the ranking models used in LinkedIn’s People Search, and we’ll explore the deep learning architectures in production today. In addition, we’ll take a look at some of the design decisions of the past, what we’ve learned along the way, and what we have in store for the future. 

Mat is passionate about helping others find what they need.
As an ML engineer at LinkedIn, he develops search ranking algorithms, helping members find other people that are interesting to them. Before that, he built the NLP system at brain.ai, an early-stage startup that helps users shop by just saying what they need. And before that, he worked as a Data Scientist at Quora, analyzing experiments that helped users find answers to their questions.
When he is not finding local minima in high-dimensional spaces, Mat enjoys finding local minima in his ski boots, or local maxima in his hiking boots.  
When he is not finding local minima in high-dimensional spaces, Mat enjoys finding local minima in his ski boots, or local maxima in his hiking boots. 

Senior Research Scientist (Machine Learning)

Toyota Research Institute

Principal Research Scientist

Amazon Alexa

Lead Research Scientist

Snap

Data Science Manager - ML

DoorDash

Enabling a Delightful Consumer Experience at DoorDash with Deep Learning 

At DoorDash our mission is to empower local economies. As our offerings expand beyond made-to-order food delivery to new product verticals like groceries, convenience, and retail, so do the responsibilities of the machine learning algorithms that power seamless consumer experiences at every touchpoint in the product.  Personalization, search, and recommendations for low-in-stock item substitutions all play major roles  in the consumer facing experience, enabling fast basket building capabilities, delightful discoveries, and fulfilled orders. A foundation on which these algorithms rely is an accurate product catalog and taxonomy, where we also apply machine learning methods.  In this talk, we will take you through some of the high level concepts of how we are leveraging Deep Learning within each of these main areas of ML application at DoorDash. 

- Personalization, Search and Substitution Recommendations are key pillars of a delightful consumer experience on DoorDash 
- These algorithms rely on an accurate and dynamic product catalog and taxonomy
- Machine Learning, esp Deep Learning has been a cornerstone for unlocking each of these areas

Sudeep is an Applied Machine Learning Manager at DoorDash, where he leads Personalization, Search, Substitution and Catalog ML within the New Verticals. 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. 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

ML Applications for Accelerating Discovery of New Renewable Energy Storage Materials and Batteries    

Deep learning methods such as graph neural networks and active learning approaches are being widely adopted for the discovery of novel renewable energy materials and batteries. These methods are able to predict materials properties, accelerate simulations and predict synthesis routes of new materials. In her presentation, Alishba will be highlighting key areas of development such as graph neural networks for property prediction in catalysts, crystals, the synthesis of novel materials and the importance of autonomous self-driving labs.   

- GNNs can be utilized for a complete representation of materials on the atomic level while incorporating physical laws and larger scale phenomena.
- Applications of GNNs for materials property prediction and graph-based networks for predicting chemical reaction pathways in synthesis of materials.
- Importance of integrating autonomous self-driving laboratories that combine AI with automated robotic platforms for accelerating the discovery and validation of novel materials.

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.Alishba leverages machine learning and hardware with the goal of developing general machine intelligence and DL applications to discover new materials/batteries for climate. Currently she’s doing research at Berkeley AI Research Lab. Previously, she was managing a product division at Cruise and working on their AI team to develop novel sequence models for perception and prediction to power their self-driving cars. She was also the co-founder of a venture-backed startup Voltx, a software platform that utilized machine learning and physics simulations to accelerate battery testing and materials discovery for batteries. We piloted our work with the largest battery manufacturers/OEMs and reduced their lab to commercialization process from a few months down to just a few days.

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.

Xinyun Chen is a senior research scientist in the Brain team of Google Research. She obtained her Ph.D. in Computer Science from University of California, Berkeley. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis, neural-symbolic reasoning and trustworthy machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and her work AlphaCode was featured as the front cover in Science Magazine.

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Staff Deep Learning Engineer

Stryker

Applied Scientist II

Amazon

Vice President of AI & Data Science

Albertsons Companies

Applied Scientist/Software Engineer, Machine Learning - Technical Lead

Walmart Global Tech

Ipsita Mohanty is a Software Engineer, Machine Learning - Technical Lead, working on several key product and research initiatives at Walmart Global Tech. She has an MS degree in Computer Science from Carnegie Mellon University, Pittsburgh. Prior to her Masters' program, Ipsita worked as an Associate for six years, developing trading and machine learning algorithms at Goldman Sachs in their Global Market Division at Bengaluru & London locations. She has published work on Natural Language Understanding, and her research work spans across disciplines of computer science, deep learning, and human psychology.