About

Trevor Hastie is a John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University. His research focuses on statistical learning, data analysis, and their applications in various fields. He has made significant contributions to the field of statistics and has been involved in numerous research projects and collaborations.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Mathematics
• Statistics
• Medicine
• Internal medicine
• Psychology
• Data Mining
• Econometrics
• Biology
• Clinical psychology
• Engineering
• Psychiatry
• Bioinformatics
• Simulation
• Embedded system
• Applied mathematics
• Programming language
• Cognitive psychology
• Physical therapy
• Neuroscience
• Surgery
• Pathology

Selected publications

• Cross-Validation: What Does It Estimate and How Well Does It Do It?

  Journal of the American Statistical Association · 2023 · 368 citations

  • Computer Science
  • Statistics
  • Computer Science

  re-fit the model on the combined data, since this invalidates the confidence intervals.

  PublisherOA PDFDOI

• Wearable sensors enable personalized predictions of clinical laboratory measurements

  Nature Medicine · 2021 · 240 citations

  • Computer Science
  • Machine Learning
  • Artificial Intelligence
  DOI

• Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.1-1]

  2021 · 82 citations

  • Computer Science
  • Mathematics
  • Computer Science
  Publisher

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition

  2020 · 232 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  https://stars.library.ucf.edu/etextbooks/1452/thumbnail.jpg

  Publisher

• Molecular Transducers of Physical Activity Consortium (MoTrPAC): Mapping the Dynamic Responses to Exercise

  Cell · 2020 · 286 citations

  • Biology
  • Bioinformatics
  • Medicine
  DOI

• The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

  NeuroImage · 2020 · 52 citations

  • Psychology
  • Cognitive psychology
  • Neuroscience

  Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

  DOI

Recent grants

• Flexible Statistical Modeling

  NSF · $500k · 2014–2019

• Flexible Statistical Modeling

  NSF · $250k · 2020–2024

• Flexible Statistical Modeling

  NSF · $400k · 2010–2015

• Flexible Statistical Modeling

  NSF · $392k · 2005–2010

Frequent coauthors

• Robert Tibshirani

  306 shared

• J. Friedman

  77 shared

• Patrick O. Brown

  Stanford University

  58 shared

• Daniela Witten

  University of Washington

  58 shared

• Bradley Efron

  57 shared

• Gareth James

  Association of Surgeons of Great Britain and Ireland

  53 shared

• Thérese Sørlie

  44 shared

• Jerome H. Friedman

  44 shared

Awards & honors

• C.R. and Bhargavi Rao prize for Fundamental Contributions to…
• United States National Academy of Sciences
• Sigillum Magnum from University of Bologna