Konrad Kording

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PIK Professor

University of Pennsylvania

United States

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About

Dr. Konrad Kording is a PIK Professor at the University of Pennsylvania, USA. His research focuses on computational neuroscience, causal inference, and the application of artificial intelligence in understanding human behavior. Recent publications include work on the segmentation of fossils, neural signatures in social behavior, and the kinematics of dance, showcasing his diverse interests in both biological and computational systems.

Recent Grants

Grant: Open

Grassroots Rigor: making rigorous research practices accessible, meaningful, and building a community around them

Open Date: 2022-08-01

Close Date: 2027-07-31

Grant: Close

RI: Small: Modules for neural computation

Open Date: 2019-10-01

Close Date: 2022-09-30

Grant: Close

Quantifying causality for neuroscience

Open Date: 2019-09-19

Close Date: 2022-09-18

Grant: Close

Neuronal circuits for context-driven bias in auditory categorization

Open Date: 2019-09-15

Close Date: 2024-06-30

Grant: Close

Automated Assessment of Neurodevelopment in Infants at Risk for Motor Disability

Open Date: 2019-08-07

Close Date: 2024-04-30

Articles (21)

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Collaborators (27)

William Ngiam

Lecturer

University of Adelaide

AUSTRALIA

Konrad Kording

Research Interests

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Contact this professor

About

Recent Grants

Articles (21)

Automated segmentation of synchrotron-scanned fossils

Causal inference on human behaviour

Catalyzing communities of research rigour champions

Neural signatures of natural behaviour in socializing macaques

Computational kinematics of dance: distinguishing hip hop genres

Downstream network transformations dissociate neural activity from causal functional contributions

The neuroconnectionist research programme

From Puzzle to Progress: How Engaging With Neurodiversity Can Improve Cognitive Science

Catalyzing next-generation Artificial Intelligence through NeuroAI

Overfitting to ‘predict’ suicidal ideation

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Why the simplest explanation isn’t always the best

A role for cortical interneurons as adversarial discriminators

Regression discontinuity threshold optimization

Energy expenditure does not solely explain step length–width choices during walking

The relationship between text message sentiment and self-reported depression

Aligning latent representations of neural activity

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Quantifying causality in data science with quasi-experiments

A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders

Collaborators (27)

William Ngiam

Marcel van Gerven

Talia Konkle

Anne Churchland

David Mohr

Amy Pearson

Michael L. Platt

Kathryn Bonnen

Steven Kapp

Talia Morstead

Eva Dyer

Adrien Doerig

Kara Kerr

Adriene Beltz

Per Ahlberg

Jonah Meyerhoff

Ioana Marinescu

Melissa Rethlefsen

Louise Creechan

Michael Dougherty

Cristina Savin

Mahmoud M. Elsherif

Catherine Lebel

Blake Richards

Flavio Azevedo

Yoshua Bengio

David B. Allison

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