Mark Rogers
Data Scientist
Redondo Beach, California, United States
Summary
👤
Senior🎓
Top SchoolMark Rogers is an AI research leader based in Boston with a decade of hands-on experience building scalable ML systems and production-grade language models, currently leading AI research at Cisco. He blends deep mathematical foundations (MA in Mathematics, EECS BS from UC Berkeley) with practical systems engineering—designing distributed data pipelines, PyTorch training platforms, and latency-optimized inference stacks at scale. His research roots in reverse-engineering cognition produced a novel Combinatorial Stochastic Process for modeling high-dimensional causal networks with Bayesian nonparametrics, a perspective he applies to NLP and AI defense. Mark has repeatedly translated cutting-edge models into revenue-impacting products—from Transformer forecasting and AutoML to cybersecurity NLU—and has operationalized them on large GPU and CPU clusters using Ray, PySpark, and cloud-native tooling. Notably, he pairs theoretical innovation with production rigor, often owning the full lifecycle from feature engineering of terabyte-scale data to deployable microservices.
10 years of coding experience
8 years of employment as a software developer
Cisco Data Science Program, Cisco Data Science Program at North Carolina State UniversityMaster of Business Administration (M.B.A.) MBA with emphasis in Business Analytics & Data Science, Master of Business Administration (M.B.A.) MBA with emphasis in Business Analytics & Data Science at Santa Clara University Leavey School of BusinessBachelor's degree Mathematics, Bachelor's degree Mathematics at Santa Clara University
English, Spanish
Github Skills (82)
parallel10
python10
sagemaker10
metal10
amazon-sagemaker10
mlops10
deep-learning10
gpu10
ray10
gpu-acceleration10
optimization10
spirv10
tvm10
opencl10
javascript10
Programming languages (4)
Github contributions (5)
markrogersjr/ray
Aug 2022 - Sep 2022
A unified framework for scalable computing. Ray is packaged with scalable libraries for data processing (Ray Datasets), training (Ray Train), hyperparameter tuning (Ray Tune), reinforcement learning (RLlib), and model serving (Ray Serve).
Contributions:2 PRs, 31 pushes, 2 branches in 13 days
servetrainingreinforcementhyperparameter-tuningserving
markrogersjr/financier
Feb 2020 - Mar 2020
Contributions:47 pushes, 1 branch in 23 days
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