CONFIRMED SPEAKERS INCLUDE
Applied Scientist/Software Engineer, Machine Learning - Technical Lead
Walmart Global Tech
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.
Product Leader, Entrepreneur, AI Leadership
Johnson & Johnson
Deep Learning for Personalized Recommendations
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.
Meta Reality Labs
Aayush is an engineering manager who leads the machine learning team within the synthetic data organization at Reality Labs, Meta. 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 I spent 6 years on synthetic data research in computer vision. While at Nvidia, his group delivered some of the prominent works in synthetic data creation. He graduated with a B.Tech in E&ECE from Indian Institute of Technology (IIT) Kharagpur, India, in 2010, and MASc in Computer Engineering from University of Waterloo, Canada, in 2013.
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
Regular Attendees Include:
ANML- Learning to Continually Learn
Secure Deep Reinforcement Learning
Interview on AI Ethics & Bias with ML Expert