Modern Artificial Intelligence
ECE special seminar series
addressing the most important new research in the world of artificial intelligence (AI)
The Seminar Series in Modern Artificial Intelligence is hosted by the Department of Electrical and Computer Engineering at NYU Tandon. Organized by Professor Anna Choromanska, the series aims to bring together faculty and students to discuss the most important research trends in the world of AI. The speakers include world-renowned experts whose research is making an immense impact on the development of new machine learning techniques and technologies and helping to build a better, smarter, more-connected world.
View the AI Seminar Series Playlist on YouTube
Amazon Web Services
"AutoGluon: Empowering (Multimodal) AutoML for the Next 10 Million Users"
February 8, 2023
University of Milan and Polytechnic University of Milan, Italy
"Online learning, bandits, and digital markets"
March 28, 2023
"Convex Analysis at Infinity: An Introduction to Astral Space"
"Discovering an agent's controllable latent state"
Columbia University, New York Times
"Data Science at The New York Times"
University of British Columbia
"Geometric Duality in Optimization"
Karl J. Friston
Queen Square Institute of Neurology, University College London
Dr. Fay Cobb Payton
North Carolina State University
"Coding, Coded & Counting: A Bias Continuum"
University of Washington
"Towards a Theory of Generalization in Reinforcement Learning"
Founding Director of AI for the People
"Elections, Online Chatter and Content Moderation"
Facebook and McGill University
"Building Reproducible, Reusable, and Robust Deep Reinforcement Learning Systems"
Danielle Belgrave and Niranjani Prasad
Microsoft Research Cambridge
"Machine Learning for Personalised Healthcare: Opportunities, Challenges and Insights"
Jan Kautz, VP of Learning and Perception Research at NVIDIA
"Generative Models for Image Synthesis"
February 13, 2020
Gabor Lugosi, Pompeu Fabra University
"Archeology of Random Trees"
Thursday, March 5, 2020
Leon Bottou, Facebook AI Research
"Learning Representations Using Causal Invariance"
Francis Bach, INRIA, Paris France
"Distributed Machine Learning over Networks"
Raia Hadsell, Head of Robotics Research at DeepMind
"Challenges for Deep Reinforcement Learning in Complex Environments"
Martial Hebert, Carnegie Mellon University
"Research challenges in using computer vision in robotics systems"
Tony Jebara, Netflix
"Machine Learning for Personalization"
Manuela Veloso, JP Morgan Chase
"Towards a Lasting Human-AI Interaction"
Eric Kandel, Columbia University
"The Biology of Memory and Age-Related Memory Loss"
Anima Anandkumar, Caltech
"The AI Trinity: Data + Algorithms + Infrastructure"
Kai-Fu Lee, Sinovation Ventures
"The Era of Artificial Intelligence"
David Blei, Columbia University
"The Blessings of Multiple Causes"
Richard J. Roberts, New England Biolabs, Inc.
"The Path to the Nobel Prize"
Yann LeCun, Facebook AI Research
"Obstacles to Progress in Deep Learning & AI"
Yoshua Bengio, Montreal Institute for Learning Algorithms
"GANs and Unsupervised Representation Learning"
Stefano Soatto, UCLA Vision Lab
"The Information Knot Tying Sensing and Action"
Vladimir Vapnik, Columbia University
"Rethinking Statistical Learning Theory"