I'm a scientist currently based in San Francisco.
I co-founded NewLimit to
develop new therapies based on reprogramming to reduce the burden of age-related disease.
Before that, I was in Seattle at AWS working new and diverse
technologies to
make the world a better place.
Before that, I was also in Seattle at the Allen Institute
for Cell Science where I built predicitve models to estimate the outcome of new experiments and
describe how cells change their organization under different conditions.
Before that, I was in grad school at Carnegie Mellon University
as a student of Robert Murphy. My work
focused on applying generative models to determine how cells respond to perturbations.
Even before that, I developed regulated document management software at Amgen.
NewLimit - Co-Founder • 2022 - 2024
Using ML to design cell reprogramming therapies for age-related diseases.
Amazon - Special Projects • 2021 - 2022
Sequencing engineering via machine learning and language models for biotechnology applications.
Amazon - Special Projects • 2020 - 2021
Technical lead on a "speedboat" from PRFAQ to full-initiative funding for entirely new projects at
Amazon. Demonstrated the technical feasibility
of a new products and services. Contributed to projects in audio, video, and media applications.
Amongst other things, this project was fully funded and resulted in a patent for
facial pose retargeting.
Allen Institute for Cell Science • 2016 - 2020
With collaborators from the Allen Institute for Brain Science, I've been developing tools to determine relationships across image modalities. We use a very simple deep learning model to predict the organization of many types of subcellular structures from three-dimensional brightfield images. We also show that this method works for electron micrographs, allowing us to solve very difficult image registration problems.
You can read our manuscript from September 2018 at
Nature Methods.
The software to build and run our models is
here.
Allen Institute for Cell Science • 2016 - 2022
I've been working in collaboration with Rory Donovan-Maiye to build integrated models of single cells. We use some cool deep learning tricks and conditional generative adversarial networks (GANs) to fuse data from multiple fluorescence microscopy experiments into a coherent model of sub-cellular structure localization in single cells.
Our manuscript for 3D models can be found in
PLOS Computational Biology.
Our software can be found
here.
Carnegie Mellon University• 2015 - 2017
To efficiently determine the effects of perturbations on cells (drugs), we need to build quantitative automated analysis tools to analyze the results of experiments. With collaborators at the University of Freiburg, we designed a pipeline to detect dose-dependent responses in plant cells in experiments with low numbers of replicates and high experimental variation. By labeling different components, we learn when and where these drugs affect the cell.
Our paper can be found in Cytometry.
Carnegie Mellon University• 2014 - 2017
When cells get hungry they eat themselves. With different collaborators at the University of Freiburg, we showed that depending on how they get hungry they eat themselves in different ways. We characterized how, when, and where this happens. Our publication made it on the cover of the journal Autophagy.
The manuscript can be found in the journal Autophagy.
Carnegie Mellon University• 2012 - 2016
A large part of my Ph.D. was working on CellOrganizer, a
generative model that describes how cells are put together. That resulted in a few projects:
A non-parametric model of how cell and nuclear shape are coordinated across cell states and drug
conditions was published in Molecular Biology of the
Cell.
Two models of how compartments of the cell are organized with the cell superstructure were
published in PLOS
Computational Biology and Cytometry.