2026
Humans, Humanoids & Those Three Laws
Keynote, Inaugural Symposium on Humanity-driven Robotics, Princeton University
Talks
2025
Aligning AI-controlled Humanoids for Safe Interaction with Humans
Invited Talk, Responsibly Developing Foundation Models for Robotics, Humanoids
2023
2022
Foundation Models for Robotics
Invited Talk, University of Southern California (USC)
2022
Introduction to Robot Learning
Invited Talk, Research Week with Google
2020
Flying, Driving and Walking Robots that Learn from Experience
Invited Talk, International Conference on Signal Processing and Communications (SPCOM)
2019
Making Robots Useful in the Real World through Machine Learning
Keynote Talk, North East Robotics Symposium (NERC)
2018
The Learning Continuum for Robotics: from optimal control to blackbox policy search
Keynote Talk, Workshop on Algorithmic Foundations of Robotics (WAFR)
2018
Learning to Control Complex Dynamical Systems
Invited Talk, New York University
2017
Shallow-versus-Deep: The great watershed in learning
William Pierson Field Lecture, Princeton University
2016
Geometric Reasoning in 3D Environments using Sum-of-squares Programming
Invited Talk, Workshop on Large-scale SDPs for Robotics, Control and Machine Learning, 55th IEEE Conf. on Decision and Control
2016
Real-time Learning and Inference on Emerging Mobile Systems
Invited Talk, IEEE Signal Processing Society Summer School on Big Data and Machine Learning
2016
Kernels, Random Embeddings and Deep Learning
William Pierson Field Lecture, Princeton University
2016
Real-time On-device Learning and Inference with Structured Transforms
Invited session on Statistical Machine Learning and Optimization, CISS
2015
Structured Transforms for Small Footprint Deep Learning
Invited Talk, Optimization Seminar, ORFE, Princeton University
2014
Kernels, Random Embeddings and Deep Learning
Invited Talk, Spectral Algorithms: From Theory to Practice, Simons Institute for the Theory of Computing, UC Berkeley
2014
Scaling up Kernel Methods with Randomization and Distributed Computation
Tradeoffs in Big Data Modeling, Invited session at Joint Statistical Meetings, Boston
2014
Large-scale Learning with Kernels and libskylark
Workshop on Algorithms for Modern Massive Datasets, UC Berkeley
2013
Finding Nonlinear Structure in Big Data
Rochester Big Data Forum, University of Rochester
2013
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Best Paper Award, Uncertainty in Artificial Intelligence
2011
Learning Vector-valued Functions and Data-dependent Kernels for Manifold Regularization
Partha Niyogi Memorial Conference
2006
2005