World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
6354
World Ranking
10204
National Ranking
4290

Overview

Katherine A. Heller is a researcher affiliated with Google in the United States. Their work spans the fields of Computer Science and Medicine, with a notable focus on Artificial Intelligence and its applications in healthcare and related domains.

The main fields of study for Katherine A. Heller include:

  • Computer Science
  • Medicine

Within these fields, they have contributed substantially to several subfields, namely:

  • Artificial Intelligence
  • Applied Psychology
  • Health Informatics
  • Experimental and Cognitive Psychology
  • Public Health, Environmental and Occupational Health

The key topics that characterize their research incorporate:

  • Digital Mental Health Interventions
  • Artificial Intelligence in Healthcare and Education
  • Machine Learning in Healthcare
  • Mental Health Research Topics
  • Explainable Artificial Intelligence (XAI)
  • Topic Modeling
  • Ethics in Clinical Research

Katherine A. Heller has authored numerous scholarly papers, published primarily in venues such as arXiv (Cornell University), Nature Medicine, and other interdisciplinary journals. Some of their recent publications include:

  • "Underspecification Presents Challenges for Credibility in Modern Machine Learning" (2020) - arXiv (Cornell University)
  • "The value of standards for health datasets in artificial intelligence-based applications" (2023) - Nature Medicine
  • "Machine learning for early detection of sepsis: an internal and temporal validation study" (2020) - JAMIA Open
  • "Tackling bias in AI health datasets through the STANDING Together initiative" (2022) - Nature Medicine
  • "Healthsheet: Development of a Transparency Artifact for Health Datasets" (2022) - 2022 ACM Conference on Fairness, Accountability, and Transparency

The researcher frequently collaborates with a set of co-authors, indicating joint involvement in related research areas. Some of the frequent co-authors include:

  • Negar Rostamzadeh
  • David Benrimoh
  • Kelly Perlman
  • Sonia Israel
  • Joseph Mehltretter

Publication venues where Katherine A. Heller has contributed multiple works are:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Medicine
  • 2022 ACM Conference on Fairness, Accountability, and Transparency
  • JMIR Formative Research

Best Publications

  • Do no harm: a roadmap for responsible machine learning for health care.

    Jenna Wiens;Suchi Saria;Mark Sendak;Marzyeh Ghassemi

  • Bayesian hierarchical clustering

    Katherine A. Heller;Zoubin Ghahramani

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses

    Katherine Heller;Krysta Svore;Angelos D. Keromytis;Salvatore Stolfo

  • Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

    Kristin M. Corey;Sehj Kashyap;Elizabeth Lorenzi;Sandhya A. Lagoo-Deenadayalan

  • Modelling Reciprocating Relationships with Hawkes Processes

    Charles Blundell;Jeff Beck;Katherine A. Heller

  • A Shared Vision for Machine Learning in Neuroscience

    Mai Anh T. Vu;Tülay Adalı;Demba Ba;György Buzsáki

  • Bayesian Sets

    Zoubin Ghahramani;Katherine A. Heller

  • The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling

    Sinead Williamson;Chong Wang;Katherine A. Heller;David M. Blei

  • Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

    Mark P Sendak;William Ratliff;Dina Sarro;Elizabeth Alderton

  • An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

    Jiefeng Jiang;Jeffrey Beck;Katherine Heller;Tobias Egner

  • Sequence Information for the Splicing of Human Pre-mRNA Identified by Support Vector Machine Classification

    Xiang H.-F. Zhang;Katherine A. Heller;Ilana Hefter;Christina S. Leslie

  • Analyzing the role of model uncertainty for electronic health records

    Michael W. Dusenberry;Dustin Tran;Edward Choi;Jonas Kemp

  • Learning to detect sepsis with a multitask Gaussian process RNN classifier

    Joseph Futoma;Sanjay Hariharan;Katherine Heller

  • Bayesian modeling of flexible cognitive control.

    Jiefeng Jiang;Katherine Heller;Tobias Egner

  • Bayesian Exponential Family PCA

    Shakir Mohamed;Zoubin Ghahramani;Katherine A. Heller

  • Bayesian and L1 Approaches to Sparse Unsupervised Learning

    Shakir Mohamed;Katherine A. Heller;Zoubin Ghahramani

  • Massive computational acceleration by using neural networks to emulate mechanism-based biological models

    Shangying Wang;Kai Fan;Nan Luo;Yangxiaolu Cao

  • An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection.

    Joseph Futoma;Sanjay Hariharan;Katherine A. Heller;Mark P. Sendak

  • Bayesian rose trees

    Charles Blundell;Yee Whye Teh;Katherine A. Heller

  • A comparative evaluation of two algorithms for Windows Registry Anomaly Detection

    Salvatore J. Stolfo;Frank Apap;Eleazar Eskin;Katherine Heller

  • Machine learning for early detection of sepsis: an internal and temporal validation study.

    Armando D Bedoya;Joseph Futoma;Joseph Futoma;Meredith E Clement;Kristin Corey;Kristin Corey

Frequent Co-Authors

Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Charles Blundell
Charles Blundell DeepMind (United Kingdom)
Sagar V. Parikh
Sagar V. Parikh University of Michigan–Ann Arbor
Dustin Tran
Dustin Tran Google (United States)
Edoardo M. Airoldi
Edoardo M. Airoldi Temple University
Thomas L. Griffiths
Thomas L. Griffiths Princeton University
Hanna Wallach
Hanna Wallach Microsoft (United States)
Yee Whye Teh
Yee Whye Teh University of Oxford
Sherif Karama
Sherif Karama McGill University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to various related fields that can be studied online. Many students find that earning an online degree for mechanical engineering closely complements a computer science background, especially for those passionate about robotics or automation.

A strong foundation in mathematics can also lead to the best online physics degree, which is ideal for those who want to apply computational thinking in scientific research and technology development.

For those focused on the world of data, pursuing a data scientist degree provides highly marketable skills in analytics, machine learning, and big data—fields that are in high demand across industries.

Alternatively, students may consider the technical applications of a computer science education by earning an online bachelor’s in electrical engineering, further expanding their career opportunities within hardware and software integration.

Each of these online pathways offers flexible options for students seeking strong career prospects in STEM fields while balancing other commitments.

Best Scientists Citing Katherine A. Heller

Trending Scientists

Recently Published Articles