Joel Paulson Laboratory for Advanced Optimization and Control
About
Education
- Ph.D., MIT, 2016
- M.S. CEP, MIT, 2013
- B.S., University of Texas at Austin, 2011
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Joel Paulson received his PhD in 2016 from the Massachusetts Institute of Technology (MIT), where he won an NSF Graduate Research Fellowship and multiple awards for research and outstanding teaching and mentoring.
His advisors were Professors Richard Braatz and Michael Strano. After completing his PhD, Dr. Paulson held a postdoctoral research position in Professor Ali Mesbah's group at the University of California, Berkeley.
While at UC Berkeley, he was a finalist for the 2017 International Federation of Automatic Control (IFAC) Conference Best Paper Award.
Professor Paulson has published several book chapters and over a dozen articles in such peer-reviewed journals as ACS Nano, Journal of Physical Chemistry Letters, Organic Process Research & Development, Journal of Process Control, and International Journal of Robust and Nonlinear Control.
KEY DISTINCTIONS
- NSF CAREER Award, 2023
- Winner, Application Paper Prize at IFAC World Congress, 2020
- Finalist for Young Author Prize at IFAC World Congress, 2017 National Science Foundation Graduate Research Fellowship, 2011-2016
- Director’s Student Presentation Award Finalist, AIChE CAST division, 2016
- School of Engineering Graduate Student Award for Extraordinary Teaching and Mentoring, MIT, 2015 (only 1 chosen per year across all departments)
- Outstanding Graduate Teaching Assistant Award, Chemical Engineering, MIT, 2015 Goodwin Medal Nominee, MIT, 2015
- Robert T. Haslam Fellowship, MIT, 2015
- Best Poster Presentation Award, Chemical Engineering, UT Austin, 2011 National Merit Scholarship, 2008
- Semifinalist, Siemens Research Competition, 2007
University of Texas at Austin:
- David H. Koch (1962) Fellow 2011 Best Poster Presentation Award, 2011. (Winner from over 50 posters.)
- Semifinalist, Siemens Research Competition, 2007
- TAMS Research Scholarship, 2007
- Bobby Bragan Scholarship, 2004
Research
LEARNING EFFICIENT REPRESENTATIONS OF MODEL PREDICTIVE CONTROL LAWS USING DEEP LEARNING
Innovation: Joel Paulson created a method for executing advanced control algorithms at a rate faster than the millisecond scale. Inspired from recent advances in machine learning and control theory, the method uses deep learning to efficiently approximate the behavior of complex optimization-based control schemes, while still providing strong theoretical guarantees in the presence of constraints and uncertainty
Impact: The proposed approach provides a path toward achieving high-performance in emerging safety-critical control applications including those that have traditionally been treated as “too challenging” to solve using state-of-the-art control methods.
Examples include the control of certain biomedical systems, unmanned vehicles, quadcopters, and humanoid robots. The method was recently demonstrated experimentally on an atmospheric pressure plasma device that can be used for biomedical purposes such as combating antibiotic-resistant bacteria, shrinking cancerous tumors, and accelerating the healing rate in chronic wounds.
- J.A. Paulson and A. Mesbah. Approximate closed-loop robust model predictive control with guaranteed stability and constraint satisfaction. IEEE Control Systems Letters. 4:719-724, 2020.
- A.D. Bonzanini, J.A. Paulson, D.B. Graves, and A. Mesbah. Safe dose delivery in fast sampling atmospheric plasmas using projection-based approximate economic MPC. IFAC World Congress, 2020.
FAST UNCERTAINTY PROPAGATION AND PARAMETER ESTIMATION IN COMPUTATIONALLY INTENSIVE GENOME-SCALE BIOLOGICAL MODELS USING MACHINE LEARNING
Innovation:
i. Construction and validation of mathematical models is biological systems involving genome-scale molecular networks is a very challenging problem. The task of uncertainty quantification (UQ) represents: (i) calibrating the model with experimental data and (ii) propagating uncertainties through the model to characterize the quality of the model predictions. Although many methods for UQ have been developed, the majority of them are intractable on experiment-to-evaluate computational models. I developed a novel metamodeling approach that can vastly accelerate UQ methods for dynamic genome-scale biological system models in the presence of high-throughput experimental data.
Potential Impact/Benefit to Society:
i. Modeling is known to have a big impact on process understanding and optimization; however, unless the accuracy of the model is rigorously understood, then the impact of the model is limited. Thus, the developed method helps to answer this question of accuracy. In particular, most process models are functions of several unknown parameters (such as heat transfer coefficients or rate constants) that must be estimated from data. Once this has been done using the proposed approach, we can easily decide if more experiments are required or even which experiments to perform in the future to gain a better understanding of the process.
ii. We applied the method to infer extracellular kinetic parameters in a batch fermentation reactor consisting of diauxic growth on E. coli on a glucose/xylose mixed media. To the best of our knowledge, due to the complexity of the model, this problem had been unable to be solved in the literature using standard methods. Our novel metamodel enabled this problem to be solved a factor of more than 800 times faster and provided significant physical insights that had previously been unknown (such as the reported data set was insufficient for uniquely estimating all parameters).
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- J.A. Paulson, M. Martin-Casas, and A. Mesbah. Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. PLOS Computational Biology, 15, e1007308, 2019.
Current Members
- Godstand Aimiuwu, aimiuwu.6@osu.edu 2023-date
- Godstand Aimiuwu earned his B.Eng. degree in Chemical Engineering from the University of Benin, Nigeria, in 2017. Post-graduation, he secured a position as a Senior Process Engineer at the Dangote Petroleum Refinery and Petrochemicals, while also serving as a part-time consultant focused on the design and troubleshooting of chemical processes. Currently, he’s a first-year Ph.D. student in the Chemical Engineering department at Ohio State University and recently became a member of Jessica Winter and Paulson's group, with an aim to learn the techniques of Bayesian Optimization in Scalable Nanomanufacturing.
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- Nate Massa, massa.71@osu.edu 2023-date
- Nate Massa received his B.S. in Chemical Engineering from the University of Iowa in 2023 and is a first-year PhD graduate student in Chemical Engineering at The Ohio State University. During his undergraduate studies Nate worked in a high atmosphere aerosol experimental laboratory under Dr. Stanier, mostly performing data analysis and visualization to help draw conclusions from experiments. Nate just joined the Paulson group this year and is excited to learn methods of computational black and grey box optimization.
- Kevin Donnelly, donnelly.235@osu.edu 2022-date
- Kevin Donnelly received his B.S. in Chemical Engineering (summa cum laude) from West Virginia University in 2022 with a minor in Law & Legal Studies. He began his Ph.D. work at The Ohio State University (OSU) in Fall 2022 under the co-advisory of Dr. Joel Paulson (OSU) and Dr. Bhavik Bakshi (ASU). His work is focused on climate-resistant process design of the food-energy-water nexus and data-driven optimization methods.
- Madhav Reddy, muthyala.7@buckeyemail.osu.edu 2022-date
- Madhav Reddy Muthyala is a second-year master's student who is transitioning to a Ph.D. program from Spring 2024 under the guidance of Dr. Paulson. He earned his B.Tech in Chemical Engineering from Jawaharlal Nehru Technological University, Hyderabad in 2020. Prior to commencing his studies at OSU, he gained professional experience in the fields of machine learning and software development. Currently, his research focuses on the development of interpretable machine learning models for advancing next-generation materials discovery, as well as high-dimensional machine learning and uncertainty quantification.
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- Wei-Ting (Jonathan) Tang, tang.1856@.osu.edu 2022-date
- Wei-Ting (Jonathan) Tang is a second-year PhD student in Dr. Paulson’s group. He received B.S. and M.S. in Chemical Engineering at National Taiwan University (NTU) from 2020 and 2022. He did research at the Process System Engineering lab at NTU before joing the group, in which he focused on optimization of distillation processes. Currently, his research mainly focuses on development of deterministic global optimization algorithm for Gaussian Processes and application of Bayesian optimization to chemical reactor design.
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- Google Scholar
- Ting Yeh Chen, chen.11026@osu.edu 2021-date
- Ting-Yeh Chen received her B.S. and M.S. in Chemical & Material Science Engineering from National Central University, Taiwan in 2019. She starts her Ph.D. journey at The Ohio State University in Fall 2021 advised by Dr. Joel Paulson. Her research focus on uncertainty quantification, closed-loop experimental design and machine learning & image analysis for bio-systems.
- Akshay Kudva, kudva.7@osu.edu 2020-date
- Akshay Kudva received his B.tech in Chemical Engineering from Vellore Institute of Technology, Vellore in 2018, and M.tech with a specialization in Process Design Engineering from the University of Petroleum and Energy Studies (with highest honors) in 2020. Prior to joining OSU in 2020, he worked on mathematical optimization of process designs in pulp and fibre manufacturing industry. His present research revolves around multi-level optimization for decision making in the presence of uncertainty.
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- Google Scholar
- Farshud Sorourifar - sorourifar.1@osu.edu 2020-date
- Farshud Sorourifar is a National Science Foundation Graduate Research Fellow pursuing a Ph.D. advised by Professor Paulson. He received his B.S. in chemical engineering from the University of California, Berkeley, in 2020, and his M.S. in chemical engineering from the Ohio State University in 2023. During his tenure as a graduate student, he has held appointments at the Dow Chemical Company, Mitsubishi Electric Research Laboratories, and NASA's Quantum Artificial Intelligence Laboratories. His research is the the area of decision-making under uncertainty, which considers optimization and controls for applications in materials discovery, quantum computing, and next-generation energy and process systems.
- Google Scholar
- Cong Wen (Kevin) Lu, lu.2318@osu.edu 2019-date
- Kevin Lu is 5th PhD candidate with a focus on greybox optimization – algorithms that integrated both physics-based and data-driven models in the presence of constraints and uncertainty. He received his Bachelors in Chemical Engineering from the University of Texas at Austin in 2019, and a Masters in Chemical Engineering from OSU in 2021. In addition to his research, Kevin has engaged many leadership activities, including: Chemical Engineering Graduate Council (CEGC), Council of Graduate Student (CGS), and COAM.
2023/09/03 - BBQ at Dr. Paulson's house
2023/03/23 - Lowrie Banquet
