Adam is a postdoctoral scholar at Stanford University, working with Leonidas Guibas. He received a Ph.D. in robotics from Carnegie Mellon University, where he worked with Katerina Fragkiadaki. He received his M.S. in Computer Science at Toronto Metropolitan University, working with Kosta Derpanis. Adam is a recipient of the NSERC PGS-D scholarship, and the Toronto Metropolitan University Gold Medal. His research interests lie in Computer Vision and Machine Learning, particularly for 3D understanding and fine-grained tracking.

Roy Fox, Assistant Professor and Director of the Intelligent Dynamics Lab at UC Irvine HDSI 123 and Zoom (Link below) Abstract: If learning from data is valuable, can learning from big […]

Abstract: How can models with more parameters than training examples generalize well, and generalize even better when we add even more parameters, even without explicit complexity control? In recent years, […]

Abstract: Deep learning is used across a broad spectrum of applications. However, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the […]

Abstract: "Despite ML models' impressive performance, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk, I overview my work on making ML “predictably reliable”---enabling developers to know when their models will work, when they will fail, and why.

To begin, we use a case study of adversarial inputs to show that human intuition can be a poor predictor of how ML models operate. Motivated by this, we present a line of work that aims to develop a precise understanding of the ML pipeline, combining statistical tools with large-scale experiments to characterize the role of each individual design choice: from how to collect data, to what dataset to train on, to what learning algorithm to use."