Katherine Heller
Katherine is a research scientist in Responsible AI at Google Research, and a member of Context in AI Research (CAIR) team. She works on Machine Learning (ML) research in Healthcare, Vision, Language, and Creativity, focusing on incorporating values for Transparency, Inclusivity, Fairness, and Robustness in our research. Prior to joining Google, she was Statistical Science faculty at Duke University, where she developed a sepsis detection system now in use at Duke University Hospital, and a nationally released iOS app which tries to complete the picture of peoples' Multiple Sclerosis course between clinic visits.
Katherine received a BS in CS and Applied Math from SUNY Stony Brook, an MS in CS from Columbia University, and a PhD in Machine Learning from the Gatsby Computational Neuroscience Unity at UCL. She was then a postdoc on an EPSRC fellowship in Engineering at the University of Cambridge, and an NSF postoc fellow in Brain and Cognitive Sciences at MIT.
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Google
Development and Evaluation of ML Models for Cardiotocography Interpretation
Nicole Chiou
Nichole Young-Lin
Abdoulaye Diack
Christopher Kelly
Sanmi Koyejo
NPJ Women's Health (2025)
AfriMed-QA: A Pan-African Multi-Specialty Medical Question-Answering Benchmark Dataset
Tobi Olatunji
Abraham Toluwase Owodunni
Charles Nimo
Jennifer Orisakwe
Henok Biadglign Ademtew
Chris Fourie
Foutse Yuehgoh
Stephen Moore
Mardhiyah Sanni
Emmanuel Ayodele
Timothy Faniran
Bonaventure F. P. Dossou
Fola Omofoye
Wendy Kinara
Tassallah Abdullahi
Michael Best
2025
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Heather Cole-Lewis
Nenad Tomašev
Liam McCoy
Leo Anthony Celi
Alanna Walton
Chirag Nagpal
Akeiylah DeWitt
Philip Mansfield
Sushant Prakash
Joelle Barral
Ivor Horn
Karan Singhal
Nature Medicine (2024)
TRINDs: Assessing the Diagnostic Capabilities of Large Language Models for Tropical and Infectious Diseases
Nenad Tomašev
Chintan Ghate
Steve Adudans
Oluwatosin Akande
Sylvanus Aitkins
Geoffrey Siwo
Lynda Osadebe
Eric Ndombi
2024
TRINDs: Assessing the Diagnostic Capabilities of Large Language Models for Tropical and Infectious Diseases
Nenad Tomašev
Chintan Ghate
Oluwatosin Akande
Geoffrey Siwo
Steve Adudans
Sylvanus Aitkins
Lynda Osadebe
Eric Ndombi
Odianosen Ehiakhamen
2024
The Case for Globalizing Fairness: A Mixed Methods Study on the Perceptions of Colonialism, AI and Health in Africa
Iskandar Haykel
Chirag Nagpal
Aisha Walcott-Bryant
Sanmi Koyejo
2024
The Case for Globalizing Fairness: A Mixed Methods Study on the Perceptions of Colonialism, AI and Health in Africa
Iskandar Haykel
Chirag Nagpal
Aisha Walcott-Bryant
Sanmi Koyejo
2024
Batch Calibration: Rethinking Calibration For In-Context Learning And Prompt Engineering
Lev Proleev
Diana Mincu
International Conference on Learning Representations (ICLR) (2024)
Nteasee: A qualitative study of expert and general population perspectives on deploying AI for health in African countries
Iskandar Haykel
Kerrie Kauer
Florence Ofori
Tousif Ahmad
2024
