### Invited Industry Talks

Resilient Machine Learning (Super Data Science, ODSC West 2022, Brown Big AI Seminar)

What Does It Take To Build Resilient ML Systems? (Scale AI - Transform X)

Productionizing Machine Learning (Abnormal Engineering Stories)

Academia, Startups, and Enterprise: A Cross-Analysis of Work and Goals (ODSC Europe, 2021)

Translating PhD Research into ML Applications (Super Data Science, 2021)

Machine Learning At Twitter (Super Data Science, 2020)

Life after Production, a Tale of Technical Debt (Banana Data Podcast, 2019)

AI Maturity in Industry (EGG NYC 2019)

Bigger Problems than Big Data (DSGO 2018, MWC Americas 2018)

Real World Data Science Strategy (ODSC East 2018, DSGO 2017, Global Big Data Conference 2017)

In the Lab: AI and Machine Learning for Media Industry (Variety Innovate Summit 2017)

Changing Human Behavior Through a Driving App (Super Data Science, 2017)

Making Deep Learning Work on Messy Sensor Data (MIT Lincoln Lab 2017, Deep Learning Summit Boston 2017)

The Power and Pains of Sensor Data (ODSC East 2017)

Machine Intelligence for Driver Safety (Machine Intelligence Summit New York 2016)

### Academic Talks

Kan Extensions for Generalizations (NYC Category Theory Seminar October 2021)

Generalized Optimization (Categories and Companions Symposium June 2021)

Category Theory in Machine Learning (Applied Category Theory 2021)

Categorical Stochastic Processes and Likelihood (Applied Category Theory 2020)

Functorial Manifold Learning and Overlapping Clustering (NYC Category Theory Seminar December 2020)

Incremental Monoidal Categories (Category Theory Octoberfest 2019, NYC Category Theory Seminar November 2019)