February 15-16, 2023
Deep Learning Summit
Discover the latest advancements and real-world applications
Bridging the gap between the latest technological research advancements and real-world applications in business and society
CONFIRMED SPEAKERS INCLUDE
Traditional search engines were conceived and designed when the internet and AI were in a different era.
Instead, you.com incorporates recent AI breakthroughs in generating text, images, and code in a conversational way directly into search.
In this keynote, we will explore the recent advancement in generative AI and its potential to transform the search engine space. We will discuss the challenges and opportunities this technology presents and its potential impact on how we access and consume information online.
Member of Technical Staff
Applied Scientist II
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
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
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
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
Lead Research Scientist
Data Science Manager - ML
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.
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
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.
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.
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
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.
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.
WHAT TO EXPECT
Receive access to multiple stages to optimize cross-industry learnings & collaboration
Solve shared problems with like-minded attendees during round table discussions, Q&As with speakers or schedule 1:1 meetings
Connect with attendees during and after the summit and build new collaborations through our interactive networking sessions
On-Demand Access: if you're unable to attend in-person, you can register for on-demand access to watch the recorded presentations and panel discussions in your own time
Hear from Expert Speakers to discover the latest advancements and trends of AI in the industry and the hot topic of Generative AI
Topics we cover
Black Box Problem
Training from Scratch
Learning Rate Decay
Deep Learning Algorithms
Deep Neural Networks
Our events bring together the latest technology advancements as well as practical examples to apply AI to solve challenges in business and society. Our unique mix of academia and industry enables you to meet with AI pioneers at the forefront of research, as well as exploring real-world case studies to discover the business value of AI.
Discover advances in deep learning algorithms and methods from the world's leading innovators. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP. Explore how deep learning will impact healthcare, manufacturing, search & transportation.
Discover Emerging Trends
The summit will showcase the opportunities of advancing trends in deep learning and their impact and successful applications in business. Where do the challenges still lie in research and application? Learn the latest technological advancements & industry trends from a global line-up of experts.
Expand Your Network
A unique opportunity to interact with industry leaders, influential technologists, data scientists & founders leading the AI revolution. Learn from & connect with 200+ industry innovators and regulators sharing best practices and advice to improve regulatory compliance, data strategy, and risk.
Who Should Attend
- Data Scientists
- Data Engineers
- Machine Learning Scientists
- Director of Engineering
Join the discussion
- 40+ speakers
- Leading technologists & innovators
- Group brainstorming sessions
- Interactive workshops
- 12+ hours of networking
- Access to filmed presentations & slides
- Discover technology shaping the future
Regular Attendees Include:
ANML- Learning to Continually Learn
Secure Deep Reinforcement Learning
Interview on AI Ethics & Bias with ML Expert
WHAT PEOPLE SAY ABOUT RE•WORK
Event Organizer /
Our events are all carefully created from scratch. The whole process from research to post-production is crafted by our team, so we are always available to assist with any queries! We look forward to meeting you at the event!