Calico Life Sciences, South San Francisco, CA, 2021 - Present Principal Investigator
Leading a laboratory combining computational and experimental methods to repurpose developmental programs for therapeutic applications
Calico Life Sciences, South San Francisco, CA, 2020 - 2021. Computational Fellow, Computing
Leading a research program combining computational and experimental approaches to address age-related diseases
Developed scNym semi-supervised adversarial neural networks for classifying cell types in single cell genomics, improving the state-of-the-art – scnym.research.calicolabs.com
Lead the development of a computational & experimental platform for pooled screening of cell identity reprogramming strategies, successfully completing a company-wide goal – reprog.research.calicolabs.com
Calico Life Sciences, South San Francisco, CA, 2018 - 2020. Data Scientist, Computing
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
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
Constant C, Kimmel JC, Sugaya K, Dogariu A. Optically Controlled Subcellular Diffusion. 2015. Frontiers in Optics & Laser Science.
Kimmel JC, Kelley DR. scNym: Semi-supervised adversarial neural networks for single cell classification. Selected speaker at the International Conference on Machine Learning (ICML), Workshop on Computational Biology. Virtual. 2020. Contributor Award for the best reviewed submissions.
Kimmel JC, Kelley DR. scNym: Semi-supervised adversarial neural networks for single cell classification. Selected speaker at Intelligent Systems for Molecular Biology (ISMB), Machine Learning in Computational and Systems Biology session. Virtual. 2020.
Kimmel JC, Penland L, Rubinstein ND, Hendrickson DG, Kelley DR, Rosenthal AZ. Cell type and tissue-specific aging trajectories. Invited speaker for California QB3 Institute’s Aging and the Single Cell event. San Francisco, CA. 2019.
Kimmel JC, Penland L, Rubinstein ND, Hendrickson DG, Kelley DR, Rosenthal AZ. Cell type and tissue-specific aging trajectories. Invited speaker at Mission Bay Capital Biolabs. San Francisco, CA. 2019.
Kimmel JC, Hwang A, Brack AS, Marshall WF. Inferring cell state dynamics with machine learning models. Invited speaker for the Machine Learning in Cell Biology Group meeting at ASCB-EMBO 2018. San Diego, CA. 2018.
Kimmel JC, Brack AS, Marshall WF. Deep neural networks for cell motility analysis. Poster 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 cell state from cell motility behavior. Selected speaker for the NSF Quantitative Cell Biology Network Workshop. Allen Institute for Cell Science, Seattle, WA. 2016.