About

Mark Rogers is a Senior Staff Machine Learning Engineer at Stovell AI Systems, with over 5 years of relevant experience in the field of machine learning and artificial intelligence. He is a passionate individual who has devoted years to reverse-engineering the brain, and has successfully developed a novel Combinatorial Stochastic Process to model high-dimensional data as an evolving causal network with Bayesian Nonparametrics. Mark is highly skilled in leveraging advanced mathematics and massively-distributed computer architectures to create insightful models of real-world data. At Stovell AI Systems, Mark has successfully developed a Transformer model for a time-series forecasting application with a direct impact on company revenue, leveraging state-of-the-art machine-learning techniques. He has also built a quality, product-agnostic platform for distributed, data-parallel training of PyTorch models with Ray Train and Ray Tune on a large GPU cluster. Additionally, he has built a quality, product-agnostic data-engineering system atop Ray Core to convert raw, tabular data into an ingestible dataset with proprietary engineered features on a large CPU cluster. Mark's exploratory analysis of raw data with Jupyter notebooks and Matplotlib has been instrumental in the success of the company. In the past, Mark has worked as a Senior Machine Learning Engineer at Change Healthcare, where he was responsible for performing data engineering on the trading-partner dataset, which is in the neighborhood of a terabyte in size. He has also built a service which applies transformations to an input dataframe via PySpark. Mark has also worked at Abacus.ai as a Machine Learning Engineer, where he has implemented features that span the entire AutoML process, including efficiently preprocessing data with PySpark, designing efficient and practical AutoML algorithms, and exposing new inference capabilities to their public API. Mark has a BS in Electrical Engineering and Computer Science from the University of California, Berkeley, and an MA in Mathematics from San Francisco State University. He is highly skilled in ML, AI, Jupyter, Pytorch, Data Engineering, and Deep Learning.