Smita Krishnaswamy, PhD
Associate Professor of Genetics and of Computer ScienceCards
Appointments
Contact Info
Yale School of Medicine
100 College Street
New Haven, CT 06510
United States
About
Titles
Associate Professor of Genetics and of Computer Science
Biography
Smita Krishnaswamy is an Associate professor in Genetics and Computer Science. She is affiliated with the applied math program, computational biology program, Yale Center for Biomedical Data Science and Yale Cancer Center. Her lab works on the development of machine learning techniques to analyze high dimensional high throughput biomedical data. Her focus is on unsupervised machine learning methods, specifically manifold learning and deep learning techniques for detecting structure and patterns in data. She has developed algorithms for non-linear dimensionality reduction and visualization, learning data geometry, denoising, imputation, inference of multi-granular structure, and inference of feature networks from big data. Her group has applied these techniques to many data types such as single cell RNA-sequencing, mass cytometry, electronic health record, and connectomic data from a variety of systems. Specific application areas include immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. Smita has a Ph.D. in Computer Science and Engineering from the University of Michigan.
Appointments
Computer Science
Associate Professor TenureFully JointGenetics
Associate Professor TenureFully Joint
Other Departments & Organizations
- Cancer Immunology
- Center for Biomedical Data Science
- Center for Infection and Immunity
- Center for RNA Science and Medicine
- Computational Biology and Biomedical Informatics
- Computational Biology and Bioinformatics
- Computer Science
- Genetics
- Human and Translational Immunology Program
- Interdepartmental Neuroscience Program
- Krishnaswamy Lab
- Neuroscience Track
- Wu Tsai Institute
- Yale Cancer Center
- Yale Center for Genomic Health
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
Education & Training
- Postdoctoral Fellowship
- Columbia University (2015)
- PhD
- University of Michigan (2008)
- MS
- University of Michigan (2004)
- BA
- Kalamazoo College, Mathematics (2002)
- BS
- University of Michigan (2002)
Research
Overview
Medical Subject Headings (MeSH)
ORCID
0000-0001-5823-1985- View Lab Website
Krishnaswamy Lab
Research at a Glance
Yale Co-Authors
Publications Timeline
Dhananjay Bhaskar, PhD
Adam de Havenon, MD, MSCI
Betty R. Lawton, PhD
Caroline Hendry, PhD
Caroline Zeiss, DACVP, DACLAM
Courtney Elizabeth Gibson, MD, MS, FACS
Publications
2024
Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue.
Venkat A, Carlino M, Lawton B, Prasad M, Amodio M, Gibson C, Zeiss C, Youlten S, Krishnaswamy S, Krause D. Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue. Genome Research 2024, 34: 837-850. PMID: 38977309, PMCID: PMC11293540, DOI: 10.1101/gr.278215.123.Peer-Reviewed Original ResearchAltmetricConceptsCell statesMitochondrial transcript abundanceParathyroid glandsHuman parathyroidCell-cell communication analysisRNA expression analysisSingle-cell analysisTranscriptional dynamicsTranscript abundanceExpression dynamicsRNA transcriptomeEpithelial cell statesCell abundanceExpression analysisPseudotime analysisStrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records
Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. Npj Digital Medicine 2024, 7: 130. PMID: 38760474, PMCID: PMC11101464, DOI: 10.1038/s41746-024-01120-w.Peer-Reviewed Original ResearchAltmetricConceptsElectronic health recordsWeighted F1MIMIC-IIIClinical decision support systemsMulti-class classificationNatural language processingMIMIC-III datasetHealth recordsMachine learning classifiersDecision support systemArtificial intelligence toolsVascular neurologistsLearning classifiersBinary classificationCross-validation accuracyLanguage processingMeta-modelIntelligence toolsStroke prevention effortsAcute ischemic strokeStroke etiologySupport systemStroke etiology classificationClassification toolClassifierInferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Steach H, Viswanath S, He Y, Zhang X, Ivanova N, Hirn M, Perlmutter M, Krishnaswamy S. Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. Lecture Notes In Computer Science 2024, 14758: 235-252. DOI: 10.1007/978-1-0716-3989-4_15.Peer-Reviewed Original ResearchConceptsSingle-cell resolutionMetabolic networksStructure of metabolic networksBiological processesGlobal metabolic networkMetabolic stateMeasure gene expressionGenomic informationTranscriptomic dataTranscriptome dataPost-translationallyEpigenetic modificationsMultimodal regulationGene expressionSingle-cellTissue scaleBiological featuresCellsTranscriptomeMetabolomicsTranscriptionFlux ratesMultiomicsScRNAseqBiologySupervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Hodgson L, Li Y, Iturria-Medina Y, Stratton J, Wolf G, Krishnaswamy S, Bennett D, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Communications Biology 2024, 7: 591. PMID: 38760483, PMCID: PMC11101463, DOI: 10.1038/s42003-024-06273-8.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsGene programAlzheimer's diseaseLate-onset Alzheimer's diseaseAD risk lociCell type-specificSingle-nucleus RNA sequencingRisk lociAD brainAlzheimer's disease progressionSnRNA-seqRNA sequencingAD pathophysiologySignaling cascadesTranscriptome modulationProgressive neurodegenerative diseaseCell-typeGWASNeurodegenerative diseasesNeuronal lossGlial cellsTranscriptomeLociGenesPseudo-trajectoriesDisease progressionGeometric scattering on measure spaces
Chew J, Hirn M, Krishnaswamy S, Needell D, Perlmutter M, Steach H, Viswanath S, Wu H. Geometric scattering on measure spaces. Applied And Computational Harmonic Analysis 2024, 70: 101635. DOI: 10.1016/j.acha.2024.101635.Peer-Reviewed Original ResearchCitationsConceptsConvolutional neural networkGeometric deep learningDeep learningNeural networkSuccess of convolutional neural networksModel of convolutional neural networkMeasure spaceScattering transformData-driven graphsInvariance propertiesRiemannian manifoldsNon-Euclidean structureUndirected graphWavelet-based transformCompact Riemannian manifoldsData structuresRate of convergenceSpherical imagesNetwork stabilityHigh-dimensional single-cell dataData setsDirected graphDiffusion-mapsSigned graphGraphDirected Scattering for Knowledge Graph-Based Cellular Signaling Analysis
Venkat A, Chew J, Rodriguez F, Tape C, Perlmutter M, Krishnaswamy S. Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis. 2024, 00: 9761-9765. DOI: 10.1109/icassp48485.2024.10446530.Peer-Reviewed Original ResearchParasympathetic neurons derived from human pluripotent stem cells model human diseases and development
Wu H, Saito-Diaz K, Huang C, McAlpine J, Seo D, Magruder D, Ishan M, Bergeron H, Delaney W, Santori F, Krishnaswamy S, Hart G, Chen Y, Hogan R, Liu H, Ivanova N, Zeltner N. Parasympathetic neurons derived from human pluripotent stem cells model human diseases and development. Cell Stem Cell 2024, 31: 734-753.e8. PMID: 38608707, PMCID: PMC11069445, DOI: 10.1016/j.stem.2024.03.011.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsAutonomic nervous systemSjogren's syndromeParasympathetic neuronsFamilial dysautonomiaWhite adipocytesAutoimmune disease Sjogren's syndromeHuman pluripotent stem cellsHuman pluripotent stem cell (hPSC)-derived neuronsHuman developmental studiesPluripotent stem cellsSARS-CoV-2 infectionSchwann cell progenitorsAutonomic neuropathyCell progenitorsStem cellsModel systemNervous systemSARS-CoV-2Human diseasesDysfunctionNeuronsDifferentiation paradigmOrgan developmentNeuropathyDrug discovery studiesLearnable Filters for Geometric Scattering Modules
Tong A, Wenkel F, Bhaskar D, Macdonald K, Grady J, Perlmutter M, Krishnaswamy S, Wolf G. Learnable Filters for Geometric Scattering Modules. IEEE Transactions On Signal Processing 2024, 72: 2939-2952. DOI: 10.1109/tsp.2024.3378001.Peer-Reviewed Original ResearchConceptsGraph neural networksGraph classification benchmarksEncode graph structureData exploration tasksGeometric scattering transformGraph wavelet filtersClassification benchmarksLearned representationsLearnable filtersLearning parametersGraph structureNeural networkExploration tasksWavelet filtersBand-pass featureBiochemical domainNetworkAdaptive tuningWaveletGraphPredictive performanceScattering modulationScattering transformMathematical propertiesFilterAssessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Liao D, Liu C, Christensen B, Tong A, Huguet G, Wolf G, Nickel M, Adelstein I, Krishnaswamy S. Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480166.Peer-Reviewed Original ResearchCitationsConceptsMutual information neural estimatorMutual informationHigh-dimensional simulation dataHigh-dimensional dataNeural network representationSpectral entropyCIFAR-10Information-theoretic measuresClass labelsSTL-10Classification networkNeural representationSelf-supervisionSupervised learningIntrinsic dimensionalityClassification accuracyNeural networkAmbient dimensionNoise-resilientNeural estimatorNetwork initializationData geometryNetwork representationOverfittingNetworkBayesian Spectral Graph Denoising with Smoothness Prior
Leone S, Sun X, Perlmutter M, Krishnaswamy S. Bayesian Spectral Graph Denoising with Smoothness Prior. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480177.Peer-Reviewed Original ResearchConceptsPresence of noisy dataGraph signal processingMaximum A PosterioriAffinity graphDenoised featuresGaussian noiseNoisy dataHigh-dimensionalComplex dataAlgorithm's abilityA-posterioriModel of noise generationSmoothness priorsRestored signalDistributed noiseSignal processingAlgorithmImage dataGraphFrequency domainNoiseNoise generationDenoisingWhite noiseSmoothing
Academic Achievements & Community Involvement
honor Excellence in Science Early-Career Investigator Award
National AwardFederation of American Societies for Experimental Biology (FASEB)Details10/03/2022United States
News
News
- August 30, 2024
Kavli Institute for Neuroscience Celebrates 20 Years with Symposium on Sept. 20
- May 03, 2024
Hao, Chen, and Bhaskar Honored With 2024 Kavli Postdoctoral Fellowship
- March 25, 2024
Dr. Smita Krishnaswamy on Yale Cancer Answers
- December 13, 2023
Informatics program transformation brought student to YSPH
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Yale School of Medicine
100 College Street
New Haven, CT 06510
United States
Computer Science
17 Hillhouse Avenue
New Haven, CT 06511
United States
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Yale Only Smita Krishnaswamy, PhD