Directors

STEMistryLab Directors have more than 25 years of experience in Artificial Intelligence (AI), STEM education at all levels and ages, scientific computing, modeling & simulation, and research in a variety of settings, including universities, small business high-tech startups, US national laboratories, US federal and state governments, and large corporations.

Michael Johnson, Ph.D. - STEMistryLab Founder and President

• Education
• Ph.D. Physical Chemistry, University of Utah
• B.S. Physics, Chemistry, Math, University of Nebraska - Kearney
• Minden Public Schools, Minden, Nebraska
• Past Projects
• National Science Foundation Principal Investigator, Award #0339996, Innovative Methodology for Accelerated Quantum Molecular Dynamics

Michael R. Salazar, Ph.D. - STEMistryLab Vice President, Chair and Professor Of Chemistry, Union University, Jackson Tennessee

• Education
• Ph.D. Physical Chemistry, University of Utah
• Postdoc - Los Alamos National Laboratory
• B.S. Chemistry, New Mexico State University
• Past Projects
• American Chemical Society, Petroleum Research Fund Principal Investigator, Theoretical Investigations into the Reactive Collisions of Closed Shell Positive Ions with Isotopic Hydrogen
• American Chemical Society, Petroleum Research Fund Principal Investigator, The Development of Accelerated Quantum Molecular Dynamics for Complex Gas-Phase Reactive Systems

Jerome Soller, Ph.D. - STEMistryLab Director, President and CEO of CogniTech Corporation, Salt Lake City, Utah

• President and CEO of CogniTech Corporation
• Education
• Ph.D. Computer Science, University of Utah
• B.S. Electrical Engineering, The Johns Hopkins University
• Past Projects
• Air Force Research Laboratory Principal Investigator, Contract #FA864922P0766, Multi-Domain Scheduling, Coordination, Deconfliction, and Contingency Handling for Crowded Air Space
• Commercial Customer, provided consulting and assistance in developing an architecture for semantic search of legal databases, including search engines, machine learning (ML) and other approaches to encoding text to improve search performance, and cloud deployment
• Satori Association, led the development of an open source automated Machine Learning (ML) environment for pre-processing and analyzing multivariate time series data, including training, testing, selecting, and combining a variety of diverse classical time series, Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) to enable accurate and robust time series forecasting/prediction
• Current Projects - Responsible and Ethical AI
• Contributing to three committees within the Utah Responsible AI Community Consortium, managed by the University of Utah and in collaboration with multiple organizations at the State of Utah. The scope of these committees includes: AI legislation and policy; AI risk assessment and mitigation frameworks; AI standards; best practices for AI use; and K-12, university, and professional education for responsible and ethical AI.