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 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 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 Optimization Seminar, ORFE, Princeton University, 2015
Kernels, Random Embeddings and Deep Learning 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