Joel Paulson

Assistant Professor

Ohio State University

United States

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Joel Paulson is an Assistant Professor at The Ohio State University in the United States. His research focuses on AI-driven methodologies, Bayesian optimization, and the dynamics of mixing in microfluidic systems. Recent publications include explorations of robust Bayesian optimization techniques and machine learning applications for material discovery and chemical design.

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2025

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2023

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2023

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2022

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2022

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2022

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2021

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2020

Collaborators (4)

Bhavik Bakshi

Richard M. Morrow professor emeritus

Ohio State University

UNITED STATES

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Farshud Sorourifar

Ohio State University

UNITED STATES

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Giorgio Rizzoni

Ohio State University

UNITED STATES

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Ali Mesbah

Associate Professor

University of California, Berkeley

UNITED STATES

View details

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Joel Paulson

Research Interests

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About

Articles (20)

Adaptive subspace Bayesian optimization over molecular descriptor libraries for data-efficient chemical design

AI-Driven scenario discovery: Diffusion models and multi-armed bandits for building control validation

BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty

Why your next breakthrough needs fewer experiments

Experimental and computational investigation of mixing dynamics in millifluidic jet mixing reactors

SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification

Real-Time Synergies between Homeostatic Technological and Homeorhetic Ecological Systems by Multiscale MPC and Bayesian Optimization

Optimal input design for guaranteed fault diagnosis of nonlinear systems: An active deep learning approach

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning

Physics-Enhanced Neural Ordinary Differential Equations: Application to Industrial Chemical Reaction Systems

Understanding Rapid PET Degradation via Reactive Molecular Dynamics Simulation and Kinetic Modeling

Computationally efficient integrated design and predictive control of flexible energy systems using multi‐fidelity simulation‐based Bayesian optimization

Predicting the Solubility of Organic Energy Storage Materials Based on Functional Group Identity and Substitution Pattern

Formal Certification Methods for Automated Vehicle Safety Assessment

Constrained robust Bayesian optimization of expensive noisy black‐box functions with guaranteed regret bounds

Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty

COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information

Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees

Approximate Closed-loop Robust Model Predictive Control with Guaranteed Stability and Constraint Satisfaction

Collaborators (4)

Bhavik Bakshi

Farshud Sorourifar

Giorgio Rizzoni

Ali Mesbah

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