Large Language Models with Eyes, Arms and Legs Keynote, Learning for Dynamics and Control (L4DC), 2023
Foundation Models for Robotics Invited Talk, University of Southern California (USC), 2022
Introduction to Robot Learning Invited Talk, Research Week with Google, 2022
Flying, Driving and Walking Robots that Learn from Experience Invited Talk, International Conference on Signal Processing and Communications (SPCOM), 2020
Making Robots Useful in the Real World through Machine Learning Keynote Talk, North East Robotics Symposium (NERC), 2019
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, 2018
Shallow-versus-Deep: The great watershed in learning. William Pierson Field Lecture, Princeton University, 2017
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 2016
Structured Transforms for Small Footprint Deep Learning Invited Talk, Optimization Seminar, ORFE, Princeton University, 2015
Kernels, Random Embeddings and Deep Learning Invited Talk, Spectral Algorithms: From Theory to Practice, Simons Institute for 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 2014, Boston
Large-scale Learning with Kernels and libskylark Workshop on Algorithms for Modern Massive Datasets, UC Berkeley 2014
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, 2013
Learning Vector-valued Functions and Data-dependent Kernels for Manifold Regularization Partha Niyogi Memorial Conference, 2011
Large-scale Semi-supervised Linear SVMs SIGIR, 2006
Beyond the Point Cloud: from Transductive to Semi-supervised Learning ICML 2005