2024
Estimating Position-Dependent and Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories: Existing Methods and Future Outlook
Domingues T, Coifman R, Haji-Akbari A. Estimating Position-Dependent and Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories: Existing Methods and Future Outlook. Journal Of Chemical Theory And Computation 2024, 20: 4427-4455. PMID: 38815171, DOI: 10.1021/acs.jctc.4c00148.Peer-Reviewed Original ResearchKernel-based methodsMolecular dynamicsMolecular dynamics trajectoriesAnisotropic diffusion tensorPhysicochemical properties of materialsClosed-form analytical solutionMD trajectoriesMobility statisticsComputational chemistryHeuristic extensionMD simulationsProperties of materialsAlgorithmDynamics trajectoriesDiffusion tensorEstimated diffusivityVariable spaceMaterial propertiesDiscretization techniqueNatural extensionPosition-dependentFokker-Planck equationSpatial binsAnalytical solutionTracer particlesGene trajectory inference for single-cell data by optimal transport metrics
Qu R, Cheng X, Sefik E, Stanley III J, Landa B, Strino F, Platt S, Garritano J, Odell I, Coifman R, Flavell R, Myung P, Kluger Y. Gene trajectory inference for single-cell data by optimal transport metrics. Nature Biotechnology 2024, 1-11. PMID: 38580861, PMCID: PMC11452571, DOI: 10.1038/s41587-024-02186-3.Peer-Reviewed Original ResearchGene dynamicsGene programTrajectory inferenceBiological processesCell-cell graphDynamics of genesCell trajectory inferenceSingle-cell RNA sequencingSingle-cell dataCell state transitionsMyeloid lineage maturationDynamics of biological processesGene distributionRNA sequencingPseudotemporal orderingGene processingTrajectories of cellsGenesActivity of biological processesTechnical noiseGroups of cellsLineage maturationCellsConstruct cellsSequence
2023
Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories
Domingues T, Coifman R, Haji-Akbari A. Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories. The Journal Of Physical Chemistry B 2023, 127: 8644-8659. PMID: 37757480, DOI: 10.1021/acs.jpcb.3c03581.Peer-Reviewed Original ResearchMechanical observablesDiffusivity tensorEfficient correction schemeAnisotropic diffusivity tensorStochastic counterpartSame qualitative featuresStochastic trajectoriesVan Hove correlation functionRobust estimationCovariance estimatorMolecular simulation communityCorrelation functionsDiffusivity profilesRotational symmetryLennard-Jones fluidEstimatorQualitative featuresDiffusion mapsSpatial profileObservablesTensorCorrection schemeTransport propertiesProperty functionsPrevious paperRobust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Stochastic Trajectories
Domingues T, Coifman R, Haji-Akbari A. Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Stochastic Trajectories. The Journal Of Physical Chemistry B 2023, 127: 5273-5287. PMID: 37261948, DOI: 10.1021/acs.jpcb.3c00670.Peer-Reviewed Original ResearchTime discretizationStochastic trajectoriesMechanical observablesDiffusivity tensorAnisotropic diffusivity tensorPointwise estimatesRigorous generalizationRobust estimationCovariance estimatorDifferent functional formsLocal covarianceKernel-based approachEstimatorFunctional estimatesDiscretizationFunctional formOrthogonal functionsBulk systemKernel functionConfined systemSuch methodsObservablesTensorTransport propertiesCovariance-based estimatorGuido L. Weiss (1928–2021)
Hernández E, Wilson E, Coifman R, Maggioni M, Meyer Y, Ricci F, Šikić H, Soria F, Tabacco A, Torres R. Guido L. Weiss (1928–2021). Notices Of The American Mathematical Society 2023, 70: 1. DOI: 10.1090/noti2607.Peer-Reviewed Original Research
2022
Multiscale Decompositions of Hardy Spaces
Coifman R, Peyrière J. Multiscale Decompositions of Hardy Spaces. Applied And Numerical Harmonic Analysis 2022, 445-462. DOI: 10.1007/978-3-030-45847-8_20.Peer-Reviewed Original ResearchA Common Variable Minimax Theorem for Graphs
Coifman R, Marshall N, Steinerberger S. A Common Variable Minimax Theorem for Graphs. Foundations Of Computational Mathematics 2022, 23: 493-517. DOI: 10.1007/s10208-022-09558-8.Peer-Reviewed Original Research
2021
Wavelets and adapted waveform analysis
Coifman R, Wickerhauser M. Wavelets and adapted waveform analysis. 2021, 399-423. DOI: 10.1201/9781003210450-12.Peer-Reviewed Original ResearchDiffusion Earth Mover's Distance and Distribution Embeddings.
Tong A, Huguet G, Natik A, MacDonald K, Kuchroo M, Coifman R, Wolf G, Krishnaswamy S. Diffusion Earth Mover's Distance and Distribution Embeddings. ArXiv 2021 PMID: 33655017, PMCID: PMC7924278.Peer-Reviewed Original ResearchDoubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.
Landa B, Coifman RR, Kluger Y. Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. SIAM Journal On Mathematics Of Data Science 2021, 3: 388-413. PMID: 34124607, PMCID: PMC8194191, DOI: 10.1137/20m1342124.Peer-Reviewed Original ResearchStochastic normalizationHeteroskedastic noiseGaussian kernelHigh-dimensional settingsMatrix convergesAmbient dimensionDifferent noise variancesEuclidean spaceData pointsNoise varianceSymmetric normalizationCertain normalizationAffinity matrixClean counterpartsPairwise distancesKernelNoiseData analysis techniqueSingle-cell RNA-sequencing dataParticular directionSpaceWidespread approachConvergesMatrixHeteroskedasticity
2020
Local conformal autoencoder for standardized data coordinates
Peterfreund E, Lindenbaum O, Dietrich F, Bertalan T, Gavish M, Kevrekidis IG, Coifman RR. Local conformal autoencoder for standardized data coordinates. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 30918-30927. PMID: 33229581, PMCID: PMC7733838, DOI: 10.1073/pnas.2014627117.Peer-Reviewed Original ResearchSome Extensions of E. Stein’s Work on Littlewood–Paley Theory Applied to Symmetric Diffusion Semigroups
Coifman R, Goldberg M. Some Extensions of E. Stein’s Work on Littlewood–Paley Theory Applied to Symmetric Diffusion Semigroups. The Journal Of Geometric Analysis 2020, 31: 6781-6795. DOI: 10.1007/s12220-020-00428-9.Peer-Reviewed Original ResearchAuthor Correction: Visualizing structure and transitions in high-dimensional biological data
Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, Yim K, van den Elzen A, Hirn MJ, Coifman RR, Ivanova NB, Wolf G, Krishnaswamy S. Author Correction: Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 2020, 38: 108-108. PMID: 31896828, DOI: 10.1038/s41587-019-0395-5.Peer-Reviewed Original Research
2019
Two-sample statistics based on anisotropic kernels
Cheng X, Cloninger A, Coifman RR. Two-sample statistics based on anisotropic kernels. Information And Inference A Journal Of The IMA 2019, 9: 677-719. PMID: 32929389, PMCID: PMC7478116, DOI: 10.1093/imaiai/iaz018.Peer-Reviewed Original ResearchVisualizing structure and transitions in high-dimensional biological data
Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, Yim K, Elzen AVD, Hirn MJ, Coifman RR, Ivanova NB, Wolf G, Krishnaswamy S. Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 2019, 37: 1482-1492. PMID: 31796933, PMCID: PMC7073148, DOI: 10.1038/s41587-019-0336-3.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencing datasetsSingle-cell RNA sequencingUnique biological insightsRNA sequencing datasetsGerm layer differentiationMain developmental branchesHigh-throughput technologiesGut microbiome dataRNA sequencingUndescribed subpopulationsHigh-dimensional biological dataSequencing datasetsBiological insightsDevelopmental branchesHARMONIC ANALYTIC GEOMETRY ON SUBSETS IN HIGH DIMENSIONS - EMPIRICAL MODELS
COIFMAN R. HARMONIC ANALYTIC GEOMETRY ON SUBSETS IN HIGH DIMENSIONS - EMPIRICAL MODELS. 2019, 391-424. DOI: 10.1142/9789813272880_0018.Peer-Reviewed Original ResearchManifold learning with bi-stochastic kernels
Marshall N, Coifman R. Manifold learning with bi-stochastic kernels. IMA Journal Of Applied Mathematics 2019, 84: 455-482. DOI: 10.1093/imamat/hxy065.Peer-Reviewed Original Research
2018
Ad Honorem Yves Meyer
Coifman R, Mallat S, Jaffard S, Olevskii A, Cohen A. Ad Honorem Yves Meyer. Notices Of The American Mathematical Society 2018, 65: 1. DOI: 10.1090/noti1756.Peer-Reviewed Original ResearchPhase Unwinding, or Invariant Subspace Decompositions of Hardy Spaces
Coifman R, Peyrière J. Phase Unwinding, or Invariant Subspace Decompositions of Hardy Spaces. Journal Of Fourier Analysis And Applications 2018, 25: 684-695. DOI: 10.1007/s00041-018-9623-5.Peer-Reviewed Original ResearchParsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study
Dsilva C, Talmon R, Coifman R, Kevrekidis I. Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study. Applied And Computational Harmonic Analysis 2018, 44: 759-773. DOI: 10.1016/j.acha.2015.06.008.Peer-Reviewed Original ResearchNonlinear dynamical systemsDiffusion mapsLocal linear regressionNonlinear manifold learning algorithmDynamical systemsDynamical behaviorStochastic modelIntrinsic geometryEmbedding coordinatesSystem dimensionalitySynthetic data setsEigendirectionsComplex data setsParsimonious representationData setsSuch algorithmsManifold learning algorithmReal dataTrue dimensionalityManifold learningLearning algorithmAlgorithmCoordinatesDimensionalityManifold