Jacob C. Kimmel

Jacob C. Kimmel

Computational Fellow @ Calico. Interested in aging, genomics, imaging, & machine learning.

Jacob C. Kimmel
jacobkimmel@gmail.com

San Francisco, California

CV PDF

Education

Ph.D. — Dept. Biochemistry & Biophysics, UC San Francisco, 2015 - 2018
Funding: NSF Graduate Fellowship, PhRMA Informatics Fellowship, NIH T32

B.S. (Honors), Biotechnology, University of Central Florida, 2012 - 2015
Distinctions: Summa Cum Laude, Top Honors, University Honors
GPA: 4.0 / 4.0

Marine Biological Laboratory, Woods Hole, MA, 2016 Fall
Course: Computational Image Analysis in Cell and Developmental Biology

Experience

Calico Life Sciences, South San Francisco, CA, 2020 - Present.
Computational Fellow, Computing

Calico Life Sciences, South San Francisco, CA, 2018 - 2020.
Data Scientist, Computing

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

IBM Research, Cell Engineering Group, San Jose, CA, 2017 Fall
Deep Learning Research Intern
Principal Investigator: Simone Bianco

University of Central Florida, Orlando, FL, 2012 - 2015
Burnett Research Scholar, Research and Mentoring Program Scholar
Principal Investigator: Kiminobu Sugaya, PhD

University of California San Francisco, San Francisco, CA, 2014 Summer
Amgen Scholar, Summer Research and Training Program
Principal Investigator: K. Mark Ansel, PhD

National Institute on Aging, Baltimore, MD, 2012 Summer
Summer Fellow, Image Informatics and Computational Biology Unit Principal Investigator: Ilya G. Goldberg, PhD

Skills

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

Selected Publications

  1. Kimmel JC, Kelley DR. scNym: Semi-supervised adversarial neural networks for single cell classification. 2020. bioRxiv. doi: https://doi.org/10.1101/2020.06.04.132324.
  2. Kimmel JC, Hendrickson DG, Kelley DR. Differentiation reveals the plasticity of age-related change in murine muscle progenitors. 2020. bioRxiv. https://doi.org/10.1101/2020.03.05.979112.
  3. Kimmel JC, Hwang AB, Marshall WF, Brack AS. Aging induces aberrant state transition kinetics in murine muscle stem cells. 2020. Development. https://doi.org/10.1242/dev.183855. Featured in Company of Biologists: the Node. Chosen as a Research Highlight by Development: Muscling in on Stem Cell Aging.
  4. Kimmel JC. Disentangling latent representations of single cell RNA-seq experiments. 2020. bioRxiv. https://doi.org/10.1101/2020.03.04.972166.
  5. 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 on the cover of Genome Research.
  6. 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.
  7. Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance. 2018. PLoS Computational Biology 14(1): e1005927. https://doi.org/10.1371/journal.pcbi.1005927. Featured as an Editor’s Pick in PLoS Editor’s Collections: Cell Biology.
  8. Constant C, Kimmel JC, Sugaya K, Dogariu A. Optically Controlled Subcellular Diffusion. 2015. Frontiers in Optics & Laser Science.

Selected Presentations

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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 speaker at the Quantitative Biology meeting. Honolulu, HI. 2018.
  7. 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).
  8. 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 speaker to the Northern California Computational Biology Society. UC Santa Cruz, Santa Cruz, CA. 2017.
  9. 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.

Academic Service

Reviewer for:

Honors and Awards

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