Jacob Kimmel

Building a therapeutics firm to develop epigenetic reprogramming medicines

* Led a computational and experimental biology laboratory focused on repurposing developmental programs to address aging and age-related disease * Led the development of a computational & experimental platform for pooled screening of transient cell reprogramming strategies -- reprog.research.calicolabs.com

* Led 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 upon state-of-the-art performance -- scnym.research.calicolabs.com

* Led an investigation of cell type and tissue environment influences on aging using single cell genomics across three murine tissues -- mca.research.calicolabs.com * Led an investigation of skeletal muscle aging using single cell genomics & dynamical systems modeling that revealed a critical point where old stem cells fail to differentiate -- myo.research.calicolabs.com * Developed timelapse image analysis methods for oncology applications, enabling multi-cell tracking and feature quantification over many days * Supported drug discovery programs across multiple therapeutic modalities with functional genomics methods

Completed a PhD in the Laboratory of Cell Geometry in the Center for Cellular Construction. Applied quantitative imaging and machine learning to investigate how stem cells make decisions and how these decisions change with age. * Developed Heteromotility, a software tool to quantify cell motility behavior and determine cell state dynamics from time lapse cell imaging data * Quantified rates of muscle stem cell activation with single cell resolution for the first time * Developed Lanternfish, a software package to apply deep convolutional neural networks to cell motility data * Developed machine learning classification tools to discriminate cell states based on observable cell behaviors * Performed single-cell RNA-sequencing to identify heterogeneous muscle stem cell states during aging and myogenic activation * Developed machine learning methods to determine stem cell age based on RNA-sequencing data Awarded support based on proposals to the following organizations: * National Science Foundation * PhRMA Foundation * Nvidia * Chan Zuckerberg Biohub * Morowitz Discovery Fellowship

Advised by Simone Bianco. Applied deep learning methods to the inference of cellular states for biosensor and biomedical applications. * Developed rapid convolutional neural network (CNN) based image segmentation methods for processing of high-throughput timelapse imaging data * Developed CNN-assisted cell tracking approaches * Implemented a Natural Evolution Strategies (NES) optimization framework for tuning multi-object trackers * Implemented a software interface to custom microscopy hardware, allowing for automation of high-throughput timelapse imaging experiments

Thesis: "Inferring cell state from cell behavior"