Developed timelapse image analysis methods for oncology applications, enabling multi-cell tracking and analysis over many days
Automated quantification of yeast cell aging using convolutional neural networks
Built an automated targeting system for laser ablation microscopy with sub-millisecond timing
Developed a cell type identification method for single cell RNA-seq data using deep neural networks
University of California San Francisco, San Francisco, CA, 2015 - 2018 PhD Candidate Principal Investigators: Wallace Marshall, Andrew Brack Thesis: Inferring stem cell state from cell behavior
Developed Heteromotility biological motion analysis package, including feature extraction, unsupervised clustering, and time-series analysis tools to quantify dynamic state transitions in cellular systems
Quantified rates of muscle stem cell activation with single cell resolution for the first time using Heteromotility
Developed Lanternfish deep learning package to enable discrimination of cell states from cell motility measurements and prediction of cell motility behaviors
Demonstrated classification of stem cell differentiation states and cancerous transformation detection using Lanternfish
IBM Research, Cell Engineering Group, San Jose, CA, 2017 Fall Deep Learning Research Intern Principal Investigator: Simone Bianco
Developed rapid convolutional neural network (CNN) based image segmentation methods for processing of high-throughput timelapse imaging data
Implemented a Natural Evolution Strategies (NES) optimization framework to improve multi-cell tracking
Implemented a software interface to custom microscopy hardware, allowing for automation of high-throughput timelapse imaging experiments
University of Central Florida, Orlando, FL, 2012 - 2015 Research and Mentoring Program Scholar, Burnett Research Scholar Principal Investigator: Kiminobu Sugaya, PhD
Characterized induced pluripotent reprogramming in mesenchymal stem cells using machine-learning based image analysis, investigated cell sorting applications
Examined the use of polarized optical torques to guide cell motility in collaboration with U. of Central Florida’s optics research center
Investigated a pathway for producing neuronal cells from adipose derived stem cells by modulation of Nanog
University of California San Francisco, San Francisco, CA, 2014 Summer Summer Research and Training Program, Amgen Scholar Principal Investigator: K. Mark Ansel, PhD
Investigated the role of microRNA-29 in the regulation of cytokine production in T-cells
Awarded the summer program’s Best Presentation Award
National Institute on Aging, Baltimore, MD, 2012 Summer Summer Fellow, Image Informatics and Computational Biology Unit Principal Investigator: Ilya G. Goldberg, PhD
Identified differential aging rates across the anatomy of C. elegans using machine learning based image classification
Investigated the relationship between cognitive and age related morphologies in the primate hippocampus
Data Analysis: Experimental design, linear modeling, time series analysis, multivariate statistics Machine Learning: Supervised discrimination, unsupervised clustering, feature engineering Deep Learning: Deep convolutional and recurrent neural networks, PyTorch, Tensorflow Image Analysis: Image segmentation, classification, particle tracking NGS: Single cell and bulk RNA-seq analysis, samtools, IGV Programming: Python (scipy, scikit-learn, statsmodels), R, Matlab, git, bash, LaTeX Experimental Biology: Quantitative microscopy, single cell RNA-seq, primary cell culture, FACS, molecular biology methods
Kimmel JC, Penland L, Rubinstein ND, Hendrickson DH, Kelley DR, Rosenthal AZ. A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging. 2019. Genome Research. doi: 10.1101/gr.253880.119. Featured by Genome Research and in Company of Biologists: the Node
Kimmel JC, Brack AS, Marshall WF. Deep convolutional and recurrent neural networks for cell motility discrimination and prediction. 2019. In press, IEEE Transactions on Computational Biology and Bioinformatics., doi: 10.1109/TCBB.2019.2919307. Preprint featured in Company of Biologists: the Node.
Constant C, Kimmel JC, Sugaya K, Dogariu A. Optically Controlled Subcellular Diffusion. 2015. Frontiers in Optics & Laser Science.
Kimmel JC, Hwang A, Brack AS, Marshall WF. Inferring cell state dynamics with machine learning models. Invited presentation to the Machine Learning in Cell Biology Group meeting at ASCB-EMBO 2018. San Diego, CA. 2018.
Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring stem cell states from cell motility behavior reveals a dynamic state system and broken detailed balance. Selected presentation to the Quantitative Biology meeting. Honolulu, HI. 2018.
Kimmel JC, Brack AS, Marshall WF. Deep neural networks for cell motility analysis. Presentation to Nvidia Deep Learning in Biomedicine Workshop. San Francisco, CA. 2018. (Nvidia Most Innovative Use of Deep Learning in Biomedicine Award).
Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring stem cell states from cell motility behavior reveals a dynamic state system and broken detailed balance. Selected oral presentation to the Northern California Computational Biology Society. UC Santa Cruz, Santa Cruz, CA. 2017.
Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring cell state from cell motility behavior. Selected oral presentation to the NSF Quantitative Cell Biology Network Workshop. Allen Institute for Cell Science, Seattle, WA. 2016.
IEEE Journal of Biomedical and Health Informatics
PLoS Computational Biology
Honors and Awards
Nvidia Best Presentation Award, Nvidia Deep Learning in Biomedicine, 2018