Press Coverage

My work and I have been fortunate enough to be featured or referenced in the press, including in:

• Stanford debuts first AI benchmark to help understand LLMs, Sharon Goldman, VentureBeat
• Federal Use of AI Tools Prompts Researchers to Build New Dataset, Josh Axelrod, Bloomberg Law (Oct. 2022)
• Importance Of AI Safety Being Smartly Illuminated Amid Latest Trends Showcased At Stanford AI Safety Workshop Encompassing Autonomous Systems, Lance Eliot, Forbes (July 2022)
• The Datasets We’re Looking At This Week, Jeremy Singer-Vine, FiveThirtyEight (July 2022)
• Machine Learning May Improve Audit Efficiency, Study Finds, Tim Shaw, Reuters (June 2022)
• Regulation needed for AI, technology environmental impact, Makenzie Holland, TechTarget (July 2022)
• Why data has a sustainability problem, Ashleigh Hollowell, VentureBeat (July 2022)
• Discovery in State Courts, Jay Tidmarsh, Jotwell (May 2022)
• Researchers Are Developing Tools to Calculate AI’s Carbon Footprint, Sara Castellanos, Wall Street Journal (July 2020)
• AI conference widely known as ‘NIPS’ changes its controversial acronym, Holly Else, Nature (Nov. 2018)
• We need to improve the accuracy of AI accuracy discussions, Danny Crichton, TechCrunch (Mar. 2018)
• Artificial intelligence faces reproducibility crisis, Matthew Hutson, Science (Feb. 2018)

Coverage in Course Syllabi

I’ve also been lucky that my work has been suggested or assigned as reading in course syllabi like:

• Tuebingen (Ethics in NLP)
• OpenAI (Spinning Up as a Deep RL Researcher)
• U.C. Berkeley (Visual Object and Activity Recognition)
• University of Houston (Philosophy of Deep Learning)
• MIT (Computational Sensorimotor Learning)
• ETH (Seminar in Deep Reinforcement Learning)
• USC (Text as Data)
• CMU (Computational Ethics for NLP)
• CMU (Advanced Topics in Multi-Modal Machine Learning)
• University of Wisconsin–Madison (Machine Learning)
• Stanford (Ethical and Social Issues in NLP)
• Columbia University (Machine Learning and Climate)
• Northwestern University (Explanation and Reproducibility in Data-Driven Science)