Farhad Hormozdiari
Farhad is a research scientist in the genomics team at Google Health, where he combines genetic data and machine learning techniques to improve disease predictions for a diverse set of populations. His long-term research aims are to better healthcare outcomes and to lower costs. Prior to Google, Farhad was a postdoctoral fellow at Broad Institute and Harvard T.H. Chan School of Public Health working on understanding the biological mechanisms of diseases. Farhad obtained his PhD in computer science at UCLA while working on statistical methods to detect causal variants for a wide range of diseases. Farhad has published over 70+ peer-reviewed journals including Nature, Nature Genetics and Science. Farhad has won many awards including the Best Paper in ISMB/ECCB 2015.
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Google
Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction
Babak Behsaz
Zachary Ryan Mccaw
Davin Hill
Robert Luben
Dongbing Lai
John Bates
Howard Yang
Tae-Hwi Schwantes-An
Yuchen Zhou
Anthony Khawaja
Andrew Carroll
Brian Hobbs
Michael Cho
Nature Genetics (2024)
Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models
Babak Behsaz
Babak Alipanahi
Zachary Ryan Mccaw
Davin Hill
Tae-Hwi Schwantes-An
Dongbing Lai
Andrew Carroll
Brian Hobbs
Michael Cho
Nature Genetics (2023)
Multimodal LLMs for health grounded in individual-specific data
Anastasiya Belyaeva
Krish Eswaran
Shravya Shetty
Andrew Carroll
Nick Furlotte
ICML Workshop on Machine Learning for Multimodal Healthcare Data (2023)
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
Zachary R. Mccaw
Nicholas A. Furlotte
Andrew Carroll
Babak Alipanahi
Nature Communications (2022)
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
Andrew Carroll
Babak Alipanahi
Zachary Ryan Mccaw
Nick Furlotte
Nature Communications (2022)
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Babak Alipanahi
Babak Behsaz
Zachary Ryan Mccaw
Emanuel Schorsch
D. Sculley
Lizzie Dorfman
Sonia Phene
Andrew Walker Carroll
Anthony Khawaja
American Journal of Human Genetics (2021)
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Dan Moldovan
Ben Adlam
Babak Alipanahi
Alex Beutel
Christina Chen
Jon Deaton
Matthew D. Hoffman
Shaobo Hou
Neil Houlsby
Ghassen Jerfel
Yian Ma
Diana Mincu
Akinori Mitani
Andrea Montanari
Christopher Nielsen
Thomas Osborne
Rajiv Raman
Kim Ramasamy
Jessica Schrouff
Martin Gamunu Seneviratne
Shannon Sequeira
Harini Suresh
Victor Veitch
Steve Yadlowsky
Xiaohua Zhai
D. Sculley
Journal of Machine Learning Research (2020)
