Modern Artificial Intelligence
ECE special seminar series
addressing the most important new research in the world of artificial intelligence (AI)
About
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
Fall 2023
Maria Florina Balcan
Carnegie Mellon University
"Machine Learning for Algorithm Design"
September 26, 2023
Spring 2023
Alex Smola
Amazon Web Services
"AutoGluon: Empowering (Multimodal) AutoML for the Next 10 Million Users"
February 8, 2023
Nicolò Cesa-Bianchi
University of Milan and Polytechnic University of Milan, Italy
"Online learning, bandits, and digital markets"
March 28, 2023
Ulrike von Luxburg
University of Tuebingen, Germany
"Explainability and regulation"
May 9, 2023
Fall 2022
Robert Schapire
Microsoft Research
"Convex Analysis at Infinity: An Introduction to Astral Space"
John Langford
Microsoft
"Discovering an agent's controllable latent state"
Chris Wiggins
Columbia University, New York Times
"Data Science at The New York Times"
Fall 2021
Michael Friedlander
University of British Columbia
"Geometric Duality in Optimization"
Karl J. Friston
Queen Square Institute of Neurology, University College London
"Deep Inference"
Dr. Fay Cobb Payton
North Carolina State University
"Coding, Coded & Counting: A Bias Continuum"
Spring 2021
Sham Kakade
University of Washington
"Towards a Theory of Generalization in Reinforcement Learning"
Mutale Nkonde
Founding Director of AI for the People
"Elections, Online Chatter and Content Moderation"
Joelle Pineau
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"
Spring 2020
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
Fall 2019
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"
Spring 2019
Martial Hebert, Carnegie Mellon University
"Research challenges in using computer vision in robotics systems"
Fall 2018
Anima Anandkumar, Caltech
"The AI Trinity: Data + Algorithms + Infrastructure"
Kai-Fu Lee, Sinovation Ventures
"The Era of Artificial Intelligence"
Richard J. Roberts, New England Biolabs, Inc.
"The Path to the Nobel Prize"
Fall 2017
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"