The TWIML AI Podcast; Metric Elicitation and Robust Distributed Learning [web]
AI for Healthcare [slides (pdf)]
(with Applications to the COVID-19 Pandemic) at c3.AI Digital Transfornmation Institute [video] (Sep 2020)
(Applications and Challenges) at WCS Explore series [video] (Oct 2020)
Towards Machine Learning for Personalized Healthcare
at Illinois Big Data Summit [slides (pdf)] (Nov 2019)
Synthesizing fMRI using generative adversarial networks: cognitive neuroscience applications, promises and pitfalls (Tutorial)
at Neurohackacademy (U Washington) [video] (Aug 2018)
at DALI (Jan 2019)
at OHBM Education Course (Presented by Bliss Chapman, June 2019)
at IAS [video] (Sep 2020)
Probabilistic Models for Brain Data Analysis [slides (pdf)]
at UC Berkeley (July 2017)
at University of Sydney (Aug 2017)
short version at Big Data Neuroscience workshop (Sep 2017)
Time-varying dynamic brain connectivity (Tutorial) [slides (pdf)]
at PRNI (Jun 2016)
short version at Brainhack@Illinois (Mar 2017)
Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition [slides (pdf)]
at Beckman cognitive neuroscience brown bag (Oct 2016)
at Univ. of Sydney (Aug 2017)
Algorithmic Fairness: Why It's Hard and Why It's Interesting (With Olga Russakovsky) [web] (June 2022)
Algorithmic fairness and metric elicitation via the geometry of classifier statistics
at Harvard ML theory [video] (Nov 2020)
Towards algorithms for measuring and mitigating ML unfairness [slides (pdf)]
at Schwartz Reisman Institute for Technology and Society (University of Toronto) [video] (Dec 2020)
at Montreal AI Symposium (updated; September 2022)
Tutorial on Representation Learning and Fairness (with Moustapha Cisse) [slides (pdf)]
at NeurIPS [video] (Dec 2019)
Asynchrony and Fault-tolerance in Federated ML; Two Vignettes
at Google Seattle (June 2019)
Robust Federated and Distributed Learning
at ITA (Feb 2019)
at TTIC (Mar 2019)
at IBM Research [slides (pdf)] (Oct 2019)
Eliciting Machine Learning Metrics
at Kavli Frontiers of Science [video] (Feb 2019)
How effective is your classifier? Revisiting the role of metrics in machine learning
at Google Brain (March 2018)
at Purdue's Approximation Theory and Machine Learning workshop [video] (Sep 2018)
at Microsoft Research Cambridge [slides (pdf)] [video] (Sep 2019)
Interpretability … the who, what, why, and how.
at Machine Learning Summer School UCL [video (part 1)], [video (part 2)] (July 2019)
Metrics Matter, Examples from Binary and Multilabel Classification [slides (pdf)]
at Google Brain (July 2017)
at Facebook AI Research Paris (Aug 2017)
at MPI Tuebingen (Aug 2017)
Learning with Aggregated Data: A Tale of Two Approaches [slides (pdf)]
at UW Madison (Oct 2017) [video]
at CSL SINE Seminar (March 2017)
Frequency Domain Predictive Modeling with Aggregated Data [slides (pdf)]
at Information Theory and Applications Workshop (Feb 2017)
Beyond Accuracy: Scalable Classification with Complex Metrics [slides (pdf)]
at Georgia Tech (Nov 2016)
at Illinois Machine Learning Seminar (Jan 2017)
From probabilistic models to decision theory and back again
at TTIC (June 2016)
at Gatsby Unit, UCL (July 2016) [abstract (pdf)], [slides (pdf)]
at University of Amsterdam (July 2016)
Consistency Analysis for Binary Classification Revisited
ICML (Aug 2017) [video]