Machine Learning in the Basic Biomedical Sciences Postdoctoral Program

Participting Faculty - DS = Data Science expert - BB = Basic Biology expert

Giulio Agnetti BB/DS Cardiology/Cell Bio gagnett1@jhmi.edu Mass spectrometry of cytoskeletal components in cardiac disease		Dan Arking DS/BB DGM arking@jhmi.edu Multi-omics to identify gene regulatory networks underyling suceptibility to sudden cardiac death.		Mike Beer DS BME/DGM mbeer@jhu.edu Using machine learning to build sequence-based models of enhancer function and 3D chromatin conformation and regulatory network models of cell fate decisions in development and disease.		Muyinatu Bell DS ECE/BME/CS mledijubell@jhu.edu Deep learning models for ultrasound and photoacoustic image formation and COVID-19 feature detection
Seth Blackshaw BB Neuro/ICE/Opthamology sblack@jhmi.edu Regulatory logic of gene regulatory networks that control cell fate specification in central nervous system development and their disruption in disease.		Patrick Cahan BB/DS BME/ICE/MBG patrick.cahan@jhmi.edu Wnt signaling and epigenomic remodeling during iPSC differentiation with single cell Cut&Tag		Michael Caterina BB Neurosurg/Biological Chem. caterina@jhmi.edu Mechanisms of pathological pain and hyperalgesic signaling pathway reengineering.		Adam Charles DS BME/Center for Imaging Science adamsc@jhu.edu Data science for neural recordings and inferring meaningful activity from neural data across scales
Vikram Chib BB/DS BME vchib@jhu.edu Human systems neuroscience to understand decision-making and motivated performance		Luisa Cochella BB MBG mcochel1@jhmi.edu Temporal changes in heterochromatin during development - from imaging to sequencing based approaches		Kathleen Cullen BB/DS BME kathleen.cullen@jhu.edu Neural prosthesis and rehabilitation, computational neuroscience, perception, motor learning		Nick Durr DS BME/ECE/Wilmer/CBID ndurr@jhu.edu Machine learning and hardware development. Learned sensing techniques for pathology and endoscopy.
Andrew Ewald BB Cell Biology / Oncology andrew.ewald@jhmi.edu Imaging, genetic, and bioinformatic analyses of cancer cells in 3D culture assays recapitulating different stages of metastasis.		Jean Fan DS BME/Center for Computational Biology jeanfan@jhu.edu Spatial transcriptomics and proteomics data analysis to identify higher order structures in health/development vs cancer		Jill Fahrner BB DGM jfahrne1@jhmi.edu Elucidating mechanisms of Mendelian disorders of the epigenetic and chromatin machinery.		Elana Fertig DS Oncology ejfertig@jhmi.edu Integration of spatial single-cell with mathematical modeling. Computational immunology and tumors.
Luis Garza BB Dermatology/Cell Bio LAG@jhmi.edu Noncoding RNA and metabolomic features that control stem cells, differentiation and immunity in cells and tissue		Loyal Goff DS/BB DGM/Neuro loyalgoff@jhmi.edu Integration of histological, IF, and in situ imaging with spatial transcriptomic data during neural development and human models of neurodegeneration.		Kristine Glunde BB Radiology/Cancer Research kglunde1@jhmi.edu Big data analysis of mass spectrometry imaging data, data fusion and visualization of high-dimensional mass spectrometry imaging data		Austin Graves BB Neurosceince/BME agrave12@jhmi.edu Detecting millions of fluorescent puncta from in vivo two-photon images over time. Spike sorting high-channel-count ephys data from Neuropixels probes
Kasper D Hansen DS Biostatistics/Genetic Medicine/BME khanse10@jhu.edu Statistical genomics with applications in tranacriptomics and epigenomics		Richard Huganir BB Neurosceince rhuganir@jhmi.edu Dynamic image anaysis of 2-photon in vivo imaging of millions of synapses in the brain.		Takanari Inoue BB Cell Biology/BME jctinoue@jhmi.edu Design and develop molecular tools to synthetically engineer cell functions and behaviors for theranostic applications		Reza Kalhor BB/DS BME/MBG/Neurosciene/DGM kalhor@jhu.edu Experimental and computational methods for analyzing cell fate in complex organisms.
Eugene Kholmovski BB BME/ADVANCE ekholmo1@jhu.edu Deep learning on cardiac MRI and CT for segmentation and feature extraction		Justus Kebschull BB/DS BME/Neuroscience kebschull@jhu.edu Comparative connectomics and transcriptomics to understand evolution and organizational principles of vertebrate brains		Deok-Ho Kim BB/DS BME/Cardiology/ME/Neurology dhkim@jhu.edu Human microphysioloigcal systems (organ/tissue chip, organoid) for disease modeling, drug screening, and precision medicine.		Daeyeol Lee BB Neuroscience/PBS daeyeol@jhu.edu Dimensionality reduction and dynamic modeling of high-dimensional neural recordings using recurrent/deep neural networks.
Vasiliki Machairaki BB DGM vmachai1@jhmi.edu Differentiating human iPSCs to neural precursors and neurons from Alzheimer's disease patients.		Erika Matunis BB Stem Cell Biology ematuni1@jhmi.edu 3D and 4D Image analysis of an intact stem cell niche during regeneration		Diane Peters BB Pharmacology dpeter54@jhmi.edu Biomarkers and therapeutic targets for inflammatory bowel disease; preclinical drug development for colitis and visceral pain.		Steven Salzberg DS BME/Center for Computational Biology salzberg@jhu.edu Genome annotation and assembly, especially RNA-seq analysis and identification of protein-coding genes and isoforms through multiple methods
Aleksander Popel DS/BB BME apopel@jhu.edu Applications of Machine Learning to Cancer Systems Biology, including multiplexed Digital Pathology and Quantitative Systems Pharmacology for Immuno-Oncology		Sri Sarma DS BME/Neuroscience/Neurology ssarma2@jhmi.edu Establish novel EEG biomarkers using machine learning to enable rapid and accurate diagnosis and treatment of epilepsy.		Alan Scott BB DGM afscott@jhmi.edu Literature searching, extraction and text summarization in support of online genetic database.		Marshall G Hussain Shuler BB Neuroscience shuler@jhmi.edu Neural signatures of learning algorithms and representational architectures using optical high density electrophysiological recordings in mice.
Web Stayman DS BME/ECE web.stayman@jhu.edu Develop machine learning approaches for image formation and understanding with a focus on control and optimization for specific clinical tasks		Genevieve Stein-O'Brien DS Neuroscience gsteinobrien@jhmi.edu Transfer learning of developmental programs in disease processes. Information content of omics for regulator inference. Oscillator/switch model of transcriptional regulation of progenitor competence		Jeremias Sulam DS BME/MINDS jsulam1@jhu.edu Robust and explainable machine learning methods for biomedical data, with applications in radiology and digital pathology		Sean Taverna BB Pharmacology stavern1@jhmi.edu Epigenetic changes in neurons and a developmental model upon disruption of histone methylation pathways; ChIP-seq, transcriptomic, IF data, biochemistry
Winston Timp BB/DS BME/MBG/DGM wtimp@jhu.edu Sequencing Technology Development		Natalia Trayanova DS/BB BME/ADVANCE ntrayanova@jhu.edu Machine learning to develop risk predictors of heart disease and adverse cardiac events from imaging and biomarker data		Ali Uneri DS BME ali.uneri@jhu.edu Combining deep image analysis (x-ray, CT, US) and reinforcement learning for applications in image-guided robotic interventions		René Vidal DS Mathematical Institute for Data Science / Biomedical Engineering rvidal@jhu.edu Develop explanatory machine learning methods for medical diagnosis, rehabilitation therapy, autism, Tourette syndrome.
Shigeki Watanabe BB Cell Biology / Neuroscience shigeki.watanabe@jhmi.edu Machine vision to segment electron micrographs of synapses and discern synaptic phenotypes in neurodegenerative models.		Cynthia Wolberger BB Biophysics cwolberg@jhmi.edu Protein structure prediction.		Wojtek Zbijewski DS/BB BME wzbijewski@jhu.edu Machine Learning to relate microstructural imaging biomarkers of bone observed at different scales.