Prof. Alexander W. Dowling

Principal Investigator

Photo of Prof. Alexander W. Dowling
Office
256 Nieuwland Science Hall
Phone
574-631-4041
Email
adowling@nd.edu
University of Notre Dame
Collegiate Associate Professor of Energy and the Environment, 2025 – current

Associate Professor of Chemical & Biomolecular Engineering (primary), 2023 – current
Assistant Professor of Chemical & Biomolecular Engineering (primary), 2017 – 2023
Concurrent Faculty of Applied & Computational Mathematics & Statistics, 2022 – current

Education and Training
Postdoctoral Fellow, UW-Madison, 2015 – 2017
Ph.D., Carnegie Mellon University, Chemical Engineering, 2015
B.S.E., University of Michigan, Chemical Engineering, 2010

Long Biosketch
Alexander (Alex) Dowling is the Tony and Sarah Earley Collegiate Associate Professor of Energy and the Environment at the University of Notre Dame (Indiana, USA). He is in the Department of Chemical and Biomolecular Engineering,  with a concurrent appointment in Applied and Computational Mathematics and Statistics.

Prof. Dowling's research combines chemical engineering, computational optimization, machine learning, and data science, organized in three research themes: (1) molecular-to-systems (multiscale) modeling and optimization, (2) optimal design of experiments and statistical inference, and (3) machine learning for bridging timescales. Application domains include energy markets and infrastructure, integrated energy systems, carbon sequestration, sustainable hydrogen, critical mineral recycling, advanced separations (membranes, ionic liquids), and systems biology. His research group contributes to several open-source scientific computing projects, including the Institute for the Design of Advanced Energy Systems (IDAES). Within the Pyomo project, Prof. Dowling's group leads the development of instructive uncertainty quantification and optimal experiment design capabilities.

Prof. Dowling has been recognized with an NSF CAREER award (2019), the CAST Outstanding Young Researcher Award from AIChE (2025), the Junior Sargent Medal from IChemE (2023), the university-wide Mentoring Award from the Graduate Student Government (2023), the James A. Burns, C.S.C., Award (2025), and two R&D 100 awards. He holds a B.S.E. from the University of Michigan - Ann Arbor and a Ph.D. from Carnegie Mellon University, all in chemical engineering.

Short Biosketch
Alexander (Alex) Dowling is the Tony and Sarah Earley Collegiate Associate Professor of Energy and the Environment at the University of Notre Dame (Indiana, USA). His research combines chemical engineering, computational optimization, machine learning, and data science, organized in three research themes: (1) molecular-to-systems (multiscale) modeling and optimization, (2) optimal design of experiments and statistical inference, and (3) machine learning for bridging timescales. Application domains span sustainable energy, environmental management, and systems biology. Prof. Dowling has been recognized with several awards, including the NSF CAREER award, the CAST Outstanding Young Researcher Award, the Junior Sargent Medal, and the James A. Burns, C.S.C., Award. He holds a B.S.E. from the University of Michigan - Ann Arbor and a Ph.D. from Carnegie Mellon University, all in chemical engineering.

Curriculum Vitae

Seminar Information

Title: Optimizing Experiments: From Data-Driven to Intrusive Model-Based Methods
 
Abstract: Laboratory and computational experiments are often time and resource-intensive, which motivates the fundamental question: how to optimally design an experimental campaign (e.g., sequence of experimental conditions) or an experimental apparatus (e.g., select sensors) to maximize the value of information gained under a constrained budget? In this seminar, I will share our recent experiences using data-driven Bayesian optimization, classical model-based experiment design, and hybrid approaches. This will include an overview of recent methodological advances in uncertainty quantification and optimal experiment design in the computational open-source Pyomo ecosystem. I will summarize the key benefits of each approach, offer recommendations on selecting the most suitable method for a specific problem, and provide best practices.
 
References:
  • Bayesian optimization for chemical products and functional materials, Current Opinion in Chemical Engineering, 2022, 36, p. 100728 [link]
  • High-performance thermoelectric composites via scalable and low-cost ink processing, Energy & Environmental Science, 2024 [link]
  • Bayesian Optimization of Low-Temperature Nonthermal Plasma Jet Sintering of Nanoinks, ACS Applied Materials & Interfaces, 2024 [link]
  • Pyomo.DoE: An Open-Source Package for Model-Based Design of Experiments in Python. AIChE Journal, 2022, 68(12), p. e17813 [link]
  • Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements. Computers & Chemical Engineering, 2024 [link]
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