David Carlson

Assistant Professor

Duke University

United States

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David Carlson is an Assistant Professor at Duke University in the United States. His research interests encompass deep learning applications in medical imaging and environmental science, along with machine learning methodologies for neural engineering. Recent articles include studies on single-cell RNA-seq data annotation, medical image segmentation, and the impact of urban vegetation on heat island effects.

Articles (15)

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2021

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2021

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2020

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2020

Collaborators (3)

Ricardo Chavarriaga

Head Swiss office, CLAIRE initiative on Human-Centred Artificial Intelligence

Zürcher Hochschule für Angewandte Wissenschaften

SWITZERLAND

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Tara Chandrasekhar

Assistant Professor

Duke University Medical Center

UNITED STATES

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Fabien Lotte

FRANCE

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David Carlson

Research Interests

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Articles (15)

The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering*

Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data

Efficient few-shot medical image segmentation via self-supervised variational autoencoder

Prevalence of bias against neurodivergence-related terms in artificial intelligence language models.

A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow

Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat

Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference

Designing electrodes and electrolytes for batteries by leveraging deep learning

Electome network factors: Capturing emotional brain networks related to health and disease

Model selection to achieve reproducible associations between resting state EEG features and autism

Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery

Geometric deep learning enables 3D kinematic profiling across species and environments

Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology

Estimating ground-level PM<inf>2.5</inf> using micro-satellite images by a convolutional neural network and random forest approach

Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder

Collaborators (3)

Ricardo Chavarriaga

Tara Chandrasekhar

Fabien Lotte

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