Jacob C. Kimmel

Jacob C. Kimmel

Data Scientist @ Calico. Interested in aging, imaging, & machine learning.

Jacob C. Kimmel
jacobkimmel@gmail.com, (321)-536-1919, 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, 2018 - Present
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
Research and Mentoring Program Scholar, Burnett Research Scholar
Principal Investigator: Kiminobu Sugaya, PhD

University of California San Francisco, San Francisco, CA, 2014 Summer
Summer Research and Training Program, Amgen Scholar
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, 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
  2. Kimmel JC, Hwang AB, Marshall WF, Brack AS. Aging induces aberrant state transition kinetics in murine muscle stem cells 2019. bioRxiv. doi: https://doi.org/10.1101/739185. Featured in Company of Biologists: the Node.
  3. 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.
  4. 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 in PLoS Editor’s Collections: Cell Biology.
  5. Constant C, Kimmel JC, Sugaya K, Dogariu A. Optically Controlled Subcellular Diffusion. 2015. Frontiers in Optics & Laser Science.

Selected Presentations

  1. 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.
  2. 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.
  3. 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).
  4. 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.
  5. 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.

Academic Service

Reviewer for:

Honors and Awards

Outreach

Bay Area Maker Faire Exposition, 2016 - 2018

Science Education Partnership, UCSF/SFUSD, 2016 - 2017

Central Florida Society for Neuroscience, 2012 - 2015

High School Research Mentor, Satellite High School, 2013 - 2015

References

Wallace F. Marshall
Professor, UCSF
Wallace.Marshall@ucsf.edu

Andrew S. Brack
Associate Professor, UCSF
Andrew.Brack@ucsf.edu

Todd Nystul
Professor, UCSF
Todd.Nystul@ucsf.edu

Orion Weiner
Professor, UCSF
Orion.Weiner@ucsf.edu

Andrew G. York
Principal Investigator, Calico Life Sciences
agy@calicolabs.com

Eric Verdin
President & CEO, The Buck Institute
eric.verdin@buckinstitute.org

rss facebook twitter github youtube mail spotify instagram linkedin google pinterest medium vimeo