Talks
- March 15, 2018 CASOS @ Carnegie Mellon University: Uncovering Factions in Parliamentary Voting with Probabilistic Latent Variable Models (slides)
- Feb 28, 2017 Carnegie Mellon University: Videoplace—an artificial reality (slides)
- April 13, 2016 Data Science Pop-up Austin: Surfing Silver, Dynamic Bayesian Forecasting for Fun and Profit (slides)
- Nov 10-12, 2015 IBM Datapalooza: Visualizing Petabytes of Data with Spark and D3.js
- Nov 10-12, 2015 IBM Datapalooza: Better Search at Scale, Leveraging Spark for Contextual NLP
- Nov 10-12, 2015 IBM Datapalooza: Machine Learning Ensembles on Spark
- Sept 22, 2015 Eventbrite Tech Talk: Better Search at Scale with Spark (video)
- June 10, 2015 Data Science Pop-up Chicago (Panel): Do you need context or is data enough? (video)
- June 2, 2015 Hirepalooza (moderator): Intern to CTO
- June 1, 2015 Hirepalooza (panel): The Art of Recruiting Senior Tech Talent
- May 23, 2015 Gray Area Festival (panel): Civic Data
- Nov 23, 2014 PyData NYC: On Building a Data Science Curriculum (slides)
- Oct 20, 2014 IOTAconf: Distributed Machine Learning: Architectures to Leverage Streams of Data (video)
- July 21, 2014 GraphLab: Modern Education in a Postmodern world (slides)
- May 4, 2014 PyData SV: Data Engineering 101, Building your First Data Product (slides)
- April 1, 2014 SF Data Science Meetup (moderator): Data Science for Social Good (video)
- Oct 2, 2013 DataWeek: Why I Teach (Data Science) (video)
- May 13, 2013 Ignite SF: Meta-Learning, What Quantum Theory has to Teach us about Education (video)
Tutorials
- Mar 16, 2020 Pearson Live Training: Debugging Data Science (part 2): Tuning Models, Engineering Features, and Improving Performance (slides and code)
- Mar 9, 2020 Pearson Live Training: Debugging Data Science (part 1): Evaluating Machine Learning in Practice (slides and code)
- Jan 22, 2020 Pearson Live Training: Debugging Data Science (part 2): Tuning Models, Engineering Features, and Improving Performance (slides and code)
- Jan 8, 2020 Pearson Live Training: Debugging Data Science (part 1): Evaluating Machine Learning in Practice (slides and code)
- Oct 17, 2019 Pearson Live Training: Causal Inference in Data Science (slides and code)
- Sept 26, 2019 Pearson Live Training: Debugging Data Science (slides and code)
- June 26, 2019 Pearson Live Training: Debugging Data Science (slides and code)
- May 9, 2018 Human-Computer Interaction Institute: Machine Learning for HCI (slides)
- May 22, 2016 Moogfest: Sonifying Data (slides and code)
- April 24, 2016 Gray Area Festival: Data Sonification with p5.js (slides and code)
- Nov 15, 2015 ODSC: Hands-on with D3.js: Civic Impact (video)
- Oct 14, 2015 Rich Data Summit: Better Search at Scale: Leveraging Spark for Contextual NLP”
- Oct 7, 2015 Data Science Pop-up Seattle: Building Data Science Applications w/ Spark (in Python!)
- Sept 28, 2015 DataWeek: Interactive Data Visualization with D3.js
- July 24, 2015 PyData Seattle: Scalable Data Pipelines w/ Luigi (video)
- May 24, 2015 Gray Area Festival: Civic Impact through Data Visualization (slides | code)
- Feb 18, 2015 Strata: Building Interactive Data Visualizations (video | slides | code)
- Sept 30, 2013 DataWeek: Intro to Data Science and Machine Learning (slides | code)
- Aug 16, 2013 SF Machine Learning Meetup: Data Science Dos and Don’ts (video)
Media
- Oct 29, 2015 Software Engineering Daily: Galvanize Data Science with Jonathan Dinu and Ryan Orban
- Nov 3, 2014 Venture Beat: Why LinkedIn’s data science reorg actually makes a lot of sense
- April 1, 2014 Venture Beat: Data scientists need their own GitHub. Here are four of the best options
- Aug 15, 2013 ITworld: Data visualization, Beneficial but perilous
Miscellaneous Slides
