ResearcH.
"Un gran poder computacional conlleva una gran responsabilidad a la sociedad"
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Machine Learning Methods for Genomic Variant Detection
Machine Learning Methods for Genomic Variant Detection
To uncover genomic variation in related individuals, I develop machine learning tools that leverage known information in the prediction of this variation.
Optimization & Deep Learning Methods for Noisy Data
Optimization & Deep Learning Methods for Noisy Data
In the context of low-quality images and genomic data, I create and apply deep learning frameworks to accurately classify these data.
Statistical & Predictive Modeling of Disease Severity
Statistical & Predictive Modeling of Disease Severity
I also work on mathematical and statistical models for the prediction of Valley Fever (and other disease) severity using genomic and disease data.
JOIN MBG
JOIN MBG
I am currently looking for graduate (Master's) students to join the group! Desired background is in linear algebra, programming skills, and/or bioinformatics. If you are interested in joining the research team, please complete the following Google Form.
GITHUB
GITHUB
You can find my code (including tutorials) below
Collaborators
Collaborators
University of California, Merced
University of California, Merced
Suzanne Sindi, Assistant Professor of Applied Mathematics
Roummel Marcia, Associate Professor of Applied Mathematics
Lawrence Livermore National Lab
Lawrence Livermore National Lab
Omar DeGuchy, Data Scientist
University of Chicago
University of Chicago
David Uminsky, Senior Research Associate, Director for Data Science, Department of Computer Science
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