Srinjoy Das is an Assistant Professor in Data Science at the School of Mathematical and Data Sciences at West Virginia University. He received his Ph.D. in Electrical Engineering and M.S. in Statistics from the University of California, San Diego in 2018, and was then a Postdoctoral Research Scholar at the Department of Mathematics at UCSD till 2021, when he joined WVU.
He has previously worked for Qualcomm Inc. and Atheros Communications in cellular communications and location technologies. His current research interests are focused on algorithms for predictive inference on time series and random fields, novel distance measures for accurate inference on Deep Generative models and time series and enabling cutting edge techniques involving adaptation of Generative Deep Learning algorithms for efficient implementation on resource-constrained edge computing platforms.
Fall 2021: DSCI 101 - Introduction to Data Science
Spring 2022: DSCI 221 - Reproducible Data Science Using R
Algorithms for Realtime inference on Generative Neural Networks with Ian Colbert (now at Advanced Micro Devices and Ph.D. candidate ECE, UCSD)
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA with Xinyu Zhang (now at Microsoft Research, Beijing)
Nonparametric Point and Interval Prediction Algorithms for Random Fields with Ivy Zhang (now at Sloan School of Management, MIT)
Approximate Computing for Deep Belief Network Classifiers with Siqiao Ruan (now at SEAS, Harvard)
A Statistical Investigation of Model Quality in Generative Systems with Alexander Potapov (now at Advanced Micro Devices)
Power Efficient Image Classification and Generation using Fixed Point Gibbs Sampling with Chih-Yin Kan (now at Foxconn Interconnect Technology)
Nonparametric Hypothesis Testing for Evaluating Generative Models with Ojash Neopane (now at Department of Machine Learning, CMU)