Aurora, Illinois, United States
Current roles: Co-Lead, RDE Incubation Group Former Lead, Time Series Foundation Model Group Delivering both POC and production-ready ML and AI solutions for business problems. 20 yrs experience with data, analysis, and modeling in both academia and industry; extensive experience leading and working in collaborative cross-disciplinary teams, strong mentorship and teamwork. Excellent communication and presentation skills developed in years of presentations to both expert and general audiences. Experience in biotech/healthcare, energy, and chemical manufacturing industries. Business Achievements: -Led a team in developing a set of agentic AI tools to streamline RD&E operations saving ~$10M for 2026 alone. -Developed a set of interconnected models forecasting pandemic-related personnel absences in critical power-distribution infrastructure systems; system allowed reduction of power interruptions caused by lack of specialized personnel by up to 75%. -Used sets of mentoring exercises to discover, recruit and onboard new talent while producing POCs to forecast corrosion in water pipes, improve automatic translation of technical documents, reduce injury-causing errors in lab work, detect equipment damaged in operation etc. -Set up code review, MLOps and DevOps processes for QA, codebase integrity, improved security and upskilling of team members. -Led development of agentic AI feedback RL solutions, allowing deployed AI agents to improve via human feedback and anticipate individual user expectations. Academic Achievements (pre 2017): -Invented two algorithms based on Bayesian inference, combinatorics, statistics and information theory for specific biophysical molecule detection problems - at time of publication these were state-of-the-art and retained that position for 5+ years. -Developed a ground breaking theory of exothermic enzyme motion based on mathematical modeling/hypothesis testing, supported by microscale hydrodynamics simulations in non-typical Reynolds numbers and published in Nature, a scientific journal that accepts only 8% of submissions. ML/AI particular skills and knowledge: Time Series, Causal Analysis/Hypothesis Testing, Bayesian Statistics, PINNS (Physical and Chemical modeling), KANs, Bandits, Algorithm Development and Solutioning, MLOps, DevOps, LLMOps, Agentic AI. Tech stack skills: Python, SQL, C++, PySpark, Azure, Databricks, AI, AI agents, Foundation Models.
Worked on Machine Learning pipelines for the classification of cancer patients into groups that would /would not benefit from immunotherapies.
Interfaced with experimental scientists, technicians and subject matter experts to explore whether their research goals would benefit from advanced analysis of the data they collected Established collaborations with other research groups to harness expertise from many areas in order to solve problems that spanned research disciplines Supervisor for teams of students working with supplied data, mentoring them and evaluating their results Responsible for publishing research results in scientific journals and presenting them in conferences Administrator for the Group Computing Resources; includes building and maintaining a database.
Responsible for evaluating experimental data sets and developing novel inference-based data analysis algorithms, using approaches such as maximum entropy and Bayesian non-parametrics Responsible for building software packages applying these algorithms Supervisor of a number of graduate students assisting with coding and software product testing