Tutorials & Talks

Other Media

  • The TWIML AI Podcast; Metric Elicitation and Robust Distributed Learning
    [web]

Selected Presentations (Healthcare, Neuroscience and Biological Imaging)

  • 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

  • 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)

  • 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)

Selected Presentations (Machine Learning)

  • 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)

  • Tutorial on Representation Learning and Fairness (with Moustapha Cisse)
    [slides (pdf)]

  • 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 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)

  • 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

  • Consistency Analysis for Binary Classification Revisited