Tom Heskes

Professor

Radboud University

Netherlands

Research Interests

Explore related searches

Professors in Netherlands Professors in Statistics Statistics professors in Netherlands

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Professor Tom Heskes is affiliated with Radboud University Nijmegen in the Netherlands. His research encompasses various domains, including machine learning, graph neural networks, and probabilistic modeling, with a focus on applications in energy systems and healthcare. Recent articles authored by him explore topics such as neural network regression, uncertainty evaluation in machine learning, and monitoring of Parkinson's disease tremors.

Articles (17)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (9)

Marielle Stoelinga

University of Twente

NETHERLANDS

View details

Jan Braaksma

Manager chair / Associate Professor Asset Management & Management Engineering

University of Twente

NETHERLANDS

View details

Alessandro Di Bucchianico

Eindhoven University of Technology

NETHERLANDS

View details

Yordan Raykov

Aston University

UNITED KINGDOM

View details

Tiedo Tinga

University of Twente

NETHERLANDS

View details

Nils Jansen

Associate Professor

Radboud University

NETHERLANDS

View details

Bram Ton

University of Twente

NETHERLANDS

View details

Rob Basten

Professor at Eindhoven University of Technology

Eindhoven University of Technology

NETHERLANDS

View details

Wouter B. Teeuw

Professor (lector) Ambient Intelligence

Saxion University of Applied Sciences

NETHERLANDS

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Tom Heskes

Research Interests

Explore related searches

Contact this professor

About

Articles (17)

Confident neural network regression with Bootstrapped Deep Ensembles

Graph neural networks for assessing the reliability of the medium-voltage grid

Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach

AI, insurance, discrimination and unfair differentiation: an overview and research agenda

Optimal training of Mean Variance Estimation neural networks

How to evaluate uncertainty estimates in machine learning for regression?

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

Automatic inference of fault tree models via multi-objective evolutionary algorithms

Fast sky localization of gravitational waves using deep learning seeded importance sampling

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life

Spectral Ranking of Causal Influence in Complex Systems

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data

PrimaVera: Synergising Predictive Maintenance

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach

Disentangling drivers of spatial autocorrelation in species distribution models

Identification and validation of risk factors for antisocial behaviour involving police

Collaborators (9)

Marielle Stoelinga

Jan Braaksma

Alessandro Di Bucchianico

Yordan Raykov

Tiedo Tinga

Nils Jansen

Bram Ton

Rob Basten

Wouter B. Teeuw

Make your next move.