(4fa) Real Time Decision Making, Design and Optimization Under Uncertainty

The evolving landscape of the energy system ecosystem, with the increasing role of intermittent renewable resources and new technological paradigms, such as Electrification, require systematic consideration of both the design and operation of these new integrated energy systems. These systems are encumbered with inherent uncertainty such as intermittency, network effects, economics, operational uncertainty, technical feasibility. As the energy transition occurs, more processes (chemical, industrial, and residential) will be electrified and are more dependent on the reliability (and operational characteristics) of these evolving systems. Hence, this transition requires new methods and tools to respond and tame various aspects of this uncertainty to deliver safe, effective and reliable systems.

My research interests focus, is on solving these challenges through the development of methodologies, tools, and software for the PSE and the greater engineering fields.

Advancements in Design & Operation of Dynamic Systems under Uncertainty

The energy transition requires novel systems and combinations of energy technologies that have never been used in tandem, resulting in systems that have never been operated nor build. I aim to generate methods that combine aspects of high-fidelity modeling, data science, and optimization to tame the design and operational uncertainty of these systems, and provide novel solutions in a) energy systems engineering, b) food-energy-water-nexus, c) advanced process control.

Computational Advancements in Optimization for Real Time Systems

Building on the work during my Ph.D. I wish to further develop algorithms, tools, and software for the operation of real time systems. Some examples of real time decision making applications are optimal control, closed loop scheduling which are key to the operation of modern process systems. I will develop methodologies that improve the performance of real time decision making algorithms exploiting problem structure, and new computing paradigms (parallel computing, GPUs, etc.) into general and accessible tools for applications such as optimal control (and design), closed loop scheduling, and integration of data science methods and techniques into optimization.

Teaching Interests

Being a teaching assistant at Texas A&M University, I had the opportunity to TA or co-lecture 8 courses with students of diverse backgrounds, including Advanced Process Optimization three times. This has made me both competent and interested in designing and teaching different PSE courses, including process design, numerical methods, control, and optimization for both undergraduate and graduate students. I am also interested in teaching/creating classes focusing on the interaction between optimization and the different sub-fields in chemical engineering and how they can mutually leverage from each other, e.g., ``optimal design and control of process systems" and ``optimization, machine learning, and chemical process systems".

I am interested in promoting creativity in students and encouraging peer learning to guide them on a path to autonomous decision-making. I plan on promoting classes with a heavy programming and computing element, e.g., having coding assignments in industrially relevant languages as a key aspect of the classes. This gives them more experience in programming and resume items that better prepares them for industry and furthering their academic career.

Supervising students is not just a task but a role I deeply enjoy and eagerly look forward to. During my Ph.D, I had the opportunity to mentor and co-mentor 2 undergraduate students, 3 master students, and 6 Ph.D. students. This experience has been both enjoyable and gratifying, reinforcing my dedication to guiding students in their academic journey.