OUR PEOPLE
Mohammad Freigoun
Ph.D.
He/Him
Office: +1-301-291-2503
Academic Credentials
- Ph.D., Chemical Engineering, Arizona State University, 2021
Professional Honors
- Engineering Graduate Fellowship, Arizona State University, 2021
- American Control Conference & ASU Graduate College Travel Awards, 2017
- Tau Beta Pi Association, ASU Chapter, 2016
- Outstanding Teaching Assistant Award, School for Engineering of Matter, Transport & Energy, Arizona State University, 2016
- Outstanding Employee, Tabuk Pharmaceuticals Manufacturing Company, 2013
Professional Affiliations
- Institutes of Electrical and Electronics Engineers — IEEE
Dr. Freigoun is a decision optimization scientist specializing in modeling causal, data-generating dynamical systems, including linear, nonlinear, and hybrid, mixed logical dynamical systems. He offers expertise in both open- and closed-loop system identification, encompassing model estimation algorithms, parsimonious modeling (i.e., model reduction) techniques, model validation, input signal design, time- and frequency-domain analysis, and digital signal processing.
Dr. Freigoun's background and interests also span advanced process control and automation, including predictive control (MPC/HMPC), optimal control (LQR/LQG), IMC controller design and tuning rules, LMI controller synthesis, robust control and loop shaping, and state observers. He has delivered adaptive solutions to client and partner challenges by reformulating them as constrained decision problems amenable to state-of-the-art optimization solvers. His previous projects include designing an end-to-end adaptive supply chain optimization algorithm, as well as advancing the design of personalized behavioral medicine interventions deployed via mobile health (mHealth) technologies.
Additionally, Dr. Freigoun brings over 10 years of technical experience in dynamic modeling and optimization software development environments, including IDEs and languages such as MATLAB & Simulink, Python, AMPL (CPLEX, GUROBI, XPRESS, SCIP, Knitro, etc.), COMSOL Multiphysics, SQL, and .NET. He also utilizes High Performance Computing (HPC) clusters to execute efficient parallel computing algorithms for extensive modeling, simulations, and data analysis.
At Ä¢¹½tv, Dr. Freigoun is dedicated to maximizing client productivity through process optimization, product design and testing, data science, risk management, quality control and assurance, and digital transformation. He supports partners across various projects by leveraging his expertise in approaching large-scale decision problems and high-complexity engineering design challenges via dynamic modeling, mathematical programming, uncertainty/sensitivity analysis, and machine learning methods.
Dr. Freigoun received his Ph.D. in Chemical Engineering from Arizona State University, specializing in developing and applying advanced system identification and control systems engineering algorithms and principles in mHealth technologies. His research contributions included the development of a convexification framework for estimating grey-box state-space models of linear and quadratic structures.