Voluntary Faculty
Voluntary faculty are typically clinicians or others who are employed outside of the School but make significant contributions to department programs at the medical center or at affiliate institutions.
Voluntary rank detailsDavid Carlson, MD
Clinical ProfessorDownloadHi-Res Photo
About
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Titles
Clinical Professor of Psychiatry, Psychiatry; Training and Supervising Analyst, Western New England Institute for Psychoanalysis
Appointments
Education & Training
- Resident
- Yale (1962)
- MD
- Yale University (1958)
Research
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Overview
Medical Research Interests
Character; Empathy; Humanities; Mental Processes; Personality Development; Psychological Theory
Research at a Glance
Publications Timeline
A big-picture view of David Carlson's research output by year.
18Publications
272Citations
Publications
2025
Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data
Zhang H, Jiang Z, Zhang S, Tu L, Carlson D. Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data. Pattern Recognition 2025, 168: 111724. DOI: 10.1016/j.patcog.2025.111724.Peer-Reviewed Original ResearchCitationsConceptsSingle-cell RNA-seq dataHigh-throughput single-cell RNA sequencingGene pathway informationCell type annotationRNA-seq dataSingle-cell datasetsSingle-cell RNA sequencingGene expression vectorScRNA-seqPathway informationDiverse speciesRNA sequencingExpression vectorAnnotation methodGene expressionDropout eventsExpression patternsGenesSpeciesType annotationsAnnotationCellsComprehensive intra-Cell levelSequenceUse of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli
Isaev D, Major S, Carpenter K, Grapel J, Chang Z, Di Martino M, Carlson D, Dawson G, Sapiro G. Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli. Scientific Reports 2025, 15: 30963. PMID: 40846872, PMCID: PMC12373809, DOI: 10.1038/s41598-025-10511-2.Peer-Reviewed Original ResearchAltmetricConceptsMachine learning modelsHuman annotatorsLearning modelsComputer vision featuresMulti-layer perceptronVision-based featuresSupervised machine learning modelsComputer vision analysisHead poseVision featuresInattention detectionAutomatic toolHuman attentionVision analysisModel adaptationResearch communityElectroencephalography recordingsVideoVideo cameraComputerAnnotationReceiver operating characteristic curveOperating characteristics curveVisual stimuliPerceptronMOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation
Liu Y, Shi W, Fu C, Jiang Z, Hua Z, Carlson D. MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation. 2025, 1891-1902. DOI: 10.1145/3711896.3737056.Peer-Reviewed Original ResearchEfficient few-shot medical image segmentation via self-supervised variational autoencoder
Zhou Y, Zhou F, Xi F, Liu Y, Peng Y, Carlson D, Tu L. Efficient few-shot medical image segmentation via self-supervised variational autoencoder. Medical Image Analysis 2025, 104: 103637. PMID: 40449308, DOI: 10.1016/j.media.2025.103637.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsFew-shot medical image segmentationMedical image segmentationUnlabeled imagesVariational autoencoderImage segmentationMulti-modality medical image datasetEnd-to-end modelDice scoreFully-supervised methodsMedical image datasetsSelf-supervised learningImproving feature extractionEnd-to-endSecond-best methodSegmentation taskFeature extractionImage datasetsData augmentationSource codePrevent overfittingTraining dataReconstruction taskStructural priorsSegmentation qualityLabeled atlasesBig, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks
Calhoun Z, Bergin M, Carlson D. Big, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks. 2025 DOI: 10.5194/icuc12-491.Peer-Reviewed Original ResearchConceptsGaussian process regressionPersonal weather stationsFlexible machine learning techniquesScalable Gaussian processesWeather stationsMachine learning techniquesLow-cost sensorsGaussian processClimate monitoringComplex spatiotemporal dependenciesNearest neighbor Gaussian processLearning techniquesPWS dataLarge datasetsMachine learningNoisy dataScalable approximationDensity of weather stationsSensor placementSensor measurementsSpatiotemporal datasetsUrban climate networkDatasetUrban heat stressProcess regressionThe NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering
Carlson D, Chavarriaga R, Liu Y, Lotte F, Lu B. The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. Journal Of Neural Engineering 2025, 22: 021002. PMID: 40073450, PMCID: PMC11948487, DOI: 10.1088/1741-2552/adbfbd.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and Concepts
2024
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
Jain V, Mukherjee A, Banerjee S, Madhwal S, Bergin M, Bhave P, Carlson D, Jiang Z, Zheng T, Rai P, Tripathi S. 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. Atmospheric Environment 2024, 338: 120798. DOI: 10.1016/j.atmosenv.2024.120798.Peer-Reviewed Original ResearchCitationsConceptsFine particulate matterGround-based measurementsPM2.5 concentrationsParticulate matterPredictions of fine particulate matterPrediction mapsAmbient air quality monitoring networkImpact of fine particulate matterAir quality monitoring networkSources of PM2.5Estimate PM2.5 concentrationsPM2.5 exposure assessmentGround measurementsQuality monitoring networkDeterminants of PM2.5Satellite-based estimatesDaily PM2.5High-resolution predictionPM2.5Monitoring networkLowest root mean square errorHuman healthPost-monsoonExposure assessmentRoot mean square errorModel selection to achieve reproducible associations between resting state EEG features and autism
Carson W, Major S, Akkineni H, Fung H, Peters E, Carpenter K, Dawson G, Carlson D. Model selection to achieve reproducible associations between resting state EEG features and autism. Scientific Reports 2024, 14: 25301. PMID: 39455733, PMCID: PMC11511871, DOI: 10.1038/s41598-024-76659-5.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsElectroencephalography spectral powerCustom machine learning modelsPredictive performanceGamma powerMachine learning modelsRegularized generalized linear modelModel selectionBiomarker discoverySpectral powerMidline regionMultiple featuresLearning modelsFunctional connectivity featuresPosterior midline regionsRefining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat
Calhoun Z, Black M, Bergin M, Carlson D. Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat. Environmental Science & Technology Letters 2024, 11: 845-850. DOI: 10.1021/acs.estlett.4c00296.Peer-Reviewed Original ResearchCitationsAltmetricConceptsUrban heatUrban heat island dataUrban heat islandMeasured air temperatureLong time scalesCitizen science dataHeat islandHeat extremesIsland dataObserved temperatureAir temperatureTime scalesHeat riskPreferential samplingCitizen scientistsScience dataPoor neighborhoodsCitizen science approachNorth CarolinaSpatial statisticsCitizensNeighborhoodNOAALocationNorthDesigning electrodes and electrolytes for batteries by leveraging deep learning
Sui C, Jiang Z, Higueros G, Carlson D, Hsu P. Designing electrodes and electrolytes for batteries by leveraging deep learning. Nano Research Energy 2024, 3: e9120102. DOI: 10.26599/nre.2023.9120102.Peer-Reviewed Original ResearchCitationsAltmetric
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