from Los Angeles, California
Princeton University

Research:  Daniel performs HPC physics simulations on petascale supercomputers including Blue Waters to generate datasets used to train AI algorithms which he is developing for time-series signal processing, using deep neural networks that exploit deep-learning-optimized GPUs, to enable real-time analysis of highly noisy Big Data from the LIGO detectors and telescopes such as LSST for multimessenger astrophysics.

Daniel is a 3rd year PhD student in Astronomy and a Computational Science and Engineering Fellow at the University of Illinois at Urbana-Champaign. He obtained his Bachelor's degree in Engineering Physics, with Honors, from IIT Bombay. He is a Research Assistant in the Gravity Group at the National Center for Supercomputing Applications (NCSA). He is also a member of the LIGO, NANOGrav, and Dark Energy Survey (DES) collaborations, and an LSST Data Science Fellow.