My research interests are in machine learning, high performance and distributed computing, data reduction, and data visualization.
I received my Ph.D.in physics from Rutgers University – New Brunswick in October 2017. During my Ph.D. work, I used machine learning to understand complex epistatic interactions among networks of correlated amino acid substitutions in protein sequence alignments, and I built distributed computational grids to run large parallel molecular dynamics simulations of protein-ligand binding. Here at JAX, I will apply machine learning techniques to new problems at the forefront of genomics and molecular biology.
Rutgers, The State University of New Jersey
Ph.D., physics
Adv: R.M. Levy
2010-2017
American University
B.S., physics, mathematics
2006-2010
This intermediate Python series explores the Numpy (short for Numerical Python) library. NumPy arrays are essential for nearly all data...
This intermediate Python series explores the Numpy (short for Numerical Python) library. NumPy arrays are essential for nearly all data...
This intermediate Python series explores the Numpy (short for Numerical Python) library. NumPy arrays are essential for nearly all data...
This intermediate Python series explores the Numpy (short for Numerical Python) library. NumPy arrays are essential for nearly all data...