Denis Gudovskiy
Chandra Khatri
Chandra Khatri is the Co-Founder at Got It AI, wherein, his team is building the world's first fully autonomous Conversational AI technology. Under his leadership, Got It AI is pushing the boundaries of the Conversational AI ecosystem and delivering the next generation of automation products. Prior to Got-It, Chandra was leading various kinds of applied research groups at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.
Prior to Uber AI, he was leading R&D for the Alexa Prize Competition (Alexa AI) at Amazon, wherein he got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.
His current areas of research include Artificial and General Intelligence, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.
David Martin
Mark Kurtz
Leader of ML and engineering teams focused on the design, development, and implementation of cutting-edge technologies and products. With over 12 years of experience in software engineering and machine learning, well-practiced in building and managing teams for both closed source and open source software solutions. Currently the Director of Machine Learning at Neural Magic, Mark is focused on lowering the cost, improving the performance, and increasing the adoption of deep learning technologies through SOTA research and engineering. An active GitHub contributor, blogger, and researcher with published papers in top ML conferences.
Neural Magic
Bernease Herman
Bernease Herman is a Sr. Data Scientist at WhyLabs, the AI Observability company, and a research scientist at the University of Washington eScience Institute. At WhyLabs, she is building model and data monitoring solutions using approximate statistics techniques. Earlier in her career, Bernease built ML-driven solutions for inventory planning at Amazon and conducted quantitative research at Morgan Stanley. Her academic research focuses on machine learning evaluation and interpretability with specialty on synthetic data and societal implications. Bernease serves as faculty for the University of Washington Master’s Program in Data Science program and as chair of the Rigorous Evaluation for AI Systems (REAIS) workshop series. She has published work in top machine learning conferences and workshops such as NeurIPS, ICLR, and FAccT. She is a PhD student at the University of Washington and holds a Bachelor’s degree in mathematics and statistics from the University of Michigan.
