Hi! I am a machine learning practitioner. Currently I am an applied scientist in Amazon.com, Inc.

Previously I was a graduate student in Department of Statistics at the University of Chicago. My dissertation advisor was Professor Per A. Mykland. My research in graduate school was supported by Ph.D. fellowship and the Stevanovich Fellowship from the University of Chicago, the National Science Foundation grants DMS 14-07812 and DMS 17-13129. After graduate school, I was a postdoctoral researcher in Department of Statistics, Rice University, where I was supported by U.S. Office of Naval Research and worked with Professor Philip A. Ernst.

My previous research include stationarity tests of microstructure noise, nonparametric inference of functionals of covariance matrices using time-domain (pre-averaging) and frequency-domain (Fourier-Malliavin) techniques. My previous research offer two nonparametric frameworks that can extract dynamic cross sections and dependence structures from massive datasets, utilize low-latency noisy measurements and handle missing observations, enhance accuracy and statistical efficiency by leveraging the power of signal processing, statistics and stochastic calculus. My research has won the Stevanovich Fellowship and has been presented in summer school, department seminar, workshop and international conferences. There is another reseach bio from Stevanovich Center at the University of Chicago. Enchanté!

Contact

richard[dot]chen[dot]ryc[at]gmail[dot]com