MSc Immunology student and cofounder of fast.ai
Past: professor & director of University of San Francisco Center for Applied Data Ethics, data scientist, one of Forbes 20 Incredible Women in AI, early engineer at Uber, PhD in mathematics from Duke University
Interests: mathematical biology, data ethics, and machine learning
Current Location: Queensland, Australia
Mathematical Biology and AI in Medicine
- A Mathematical Model of Glutathione Metabolism, Journal of Theoretical Biology and Medical Modeling
- Medicine’s Machine Learning Problem, Boston Review
- Earned a PhD in mathematics from Duke University
- Howard Hughes Medical Institute Fellowship
- keynote speaker at Stanford’s Artificial Intelligence in Medicine symposium
Data Ethics
- Professor of Practice at Queensland University of Technology Centre for Data Science
- Founding director of Center for Applied Data Ethics (CADE) at University of San Francisco
- Reliance on Metrics is a Fundamental Challenge for AI, Patterns. Optimizing metrics is a central aspect of most current AI approaches, yet overemphasizing metrics leads to manipulation, gaming, and a myopic short-term focus.
- Created and taught data ethics course
- Wrote book chapters for:
Machine Learning and Data Science
- Co-founder of course.fast.ai
- Designed and taught graduate level courses on Natural Language Processing and Computational Linear Algebra in the USF Masters of Data Science program
- The New Era in NLP, Keynote at SciPy (Scientific Python) Conference 2019
- Keynote at ICML AutoML workshop, based on my popular series of AutoML posts
- Beginner friendly workshop on Word Embeddings (such as Word2Vec)
- Forbes 20 Incredible Women in AI
- Featured in book Women Tech Founders on the Rise
- Early data scientist and software engineer at Uber
