Cory McLean
Cory is a senior staff software engineer in Google Research who leads the Genomics research team. His research interests broadly include applying machine learning to the analysis and interpretation of genomic data and publishing tools and methods as open-source software. Prior to Google, Cory was at 23andMe where he developed algorithms and tools to improve identity-by-descent detection, haplotype phasing, and genotype imputation, and the application of genetic association study results to drug development. Cory received a PhD in computer science from Stanford, where he developed computational methods to understand vertebrate gene regulation, and a BS in computer science from MIT.
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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)
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Sebastien Baur
Christina Chen
Mariam Jabara
Babak Behsaz
Shravya Shetty
Goodarz Danaei
Diego Ardila
PLOS Global Public Health, 4(6) (2024), e0003204
Towards a Personal Health Large Language Model
Anastasiya Belyaeva
Nick Furlotte
Zhun Yang
Chace Lee
Erik Schenck
Yojan Patel
Jian Cui
Logan Schneider
Robby Bryant
Ryan Gomes
Allen Jiang
Roy Lee
Javier Perez
Jamie Rogers
Cathy Speed
Shyam Tailor
Megan Walker
Jeffrey Yu
Tim Althoff
Conor Heneghan
Mark Malhotra
Shwetak Patel
Shravya Shetty
Jiening Zhan
Yeswanth Subramanian
Daniel McDuff
arXiv (2024)
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)
Longitudinal fundus imaging and its genome-wide association analysis provides evidence for a human retinal aging clock
Sara Ahadi
Kenneth A Wilson Jr,
Drew Bryant
Orion Pritchard
Ajay Kumar
Enrique M Carrera
Ricardo Lamy
Jay M Stewart
Avinash Varadarajan
Pankaj Kapahi
Ali Bashir
eLife (2023)
Accurate human genome analysis with Element Avidity sequencing
Andrew Carroll
Bryan Lajoie
Daniel Cook
Kelly N. Blease
Kishwar Shafin
Lucas Brambrink
Maria Nattestad
Semyon Kruglyak
bioRxiv (2023)
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)
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)
DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer
Aaron Wenger
Andrew Walker Carroll
Armin Töpfer
Ashish Teku Vaswani
Daniel Cook
Felipe Llinares
Gunjan Baid
Howard Cheng-Hao Yang
Jean-Philippe Vert
Kishwar Shafin
Maria Nattestad
Waleed Ammar
William J. Rowell
Nature Biotechnology (2022)
Knowledge distillation for fast and accurate DNA sequence correction
Anastasiya Belyaeva
Joel Shor
Daniel Cook
Kishwar Shafin
Daniel Liu
Armin Töpfer
Aaron Wenger
William J. Rowell
Howard Yang
Andrew Carroll
Maria Nattestad
Learning Meaningful Representations of Life (LMRL) Workshop NeurIPS 2022
