Philip B. Graff is a data scientist with the Johns Hopkins University Applied Physics Laboratory. He works with graph analytics for a large-scale automated pattern discovery.

Philip obtained his PhD in physics from the University of Cambridge on a Gates Cambridge Scholarship, and a BS in physics and mathematics from the University of Maryland, Baltimore County. His PhD thesis implemented Bayesian methods for gravitational wave detection and the training of neural networks for machine learning.

Philip's post-doctoral research at NASA Goddard Space Flight Center and the University of Maryland, College Park, applied Bayesian inference to the detection and measurement of gravitational waves by ground and space-based detectors, LIGO and LISA, respectively. He also implemented machine leaning methods for improved gamma-ray burst data analysis. He has published books in the fields of astrophysical data analysis and machine learning.

Nishanth Upadhyaya has close to 10 years of experience in the area of analytics, Monte Carlo methods, signal processing, machine learning, and building end-to-end data products. He is active on StackOverflow and GitHub. He has a couple of patents in the area of item response theory and stochastic optimization. He has also won third place in the first ever Aadhaar hackathon organized by Khosla labs.