Dmitry Shribak

Dmitry Shribak is an accomplished software engineer and researcher with extensive experience in machine learning and anomaly detection. At Google, Dmitry innovated a non-stationary signal anomaly detection framework that improved accuracy from 75% to 84% and identified previously unknown anomalies in the Gemini optical network. Previous roles include machine learning engineer at Intel Corporation and student researcher at Argonne National Laboratory, where Dmitry co-authored several publications and developed models using convolutional neural networks to address complex imaging challenges. Dmitry also contributed groundbreaking work at Georgia Institute of Technology, authoring a paper for NeurIPS that introduced the Diffusion Spectral Representation algorithm, achieving notable success in reinforcement learning benchmarks. Dmitry is currently pursuing a PhD in Machine Learning at Georgia Institute of Technology.