Richard Zhang
About Richard Zhang
Machine learning scientist with 9 (as of June 2023) years of industry experience specializing in algorithm innovation, with a background in theoretical probability and statistics, and tech lead of a team of up to 6 scientists and engineers, leading efforts to expand ML capabilities, develop novel algorithms and integrate with business partners, influencing key business decisions and serving as advisor with ML expertise for other teams. Experienced within technology and finance sectors, with a Masters in Statistics at Harvard, and a Bachelors in Mathematics and Finance from Stern. Selected favorite algorithms: Multivariate Statistics – High Dimensional (CAVR, GLASSO), Unsupervised (CCA, PCA), Classification (LDA) Computational Statistics – Monte Carlo (MCMC, SMC), Nonparametric (GP, GAM), Missing Data (EM) Machine Learning – Deep Learning (CNN, LSTM), Reinforcement Learning (TS, LSPI), Adversarial Learning (GAN) Statistical Learning – Unbiased (CTree, MOB), Boosting (Adaptive, Gradient), Rule Ensembles (RuleFit)