Past Seminars
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Victor M. Zavala
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Department of Chemical and Biological Engineering, University of Wisconsin-Madison
From Molecules to Supply Chains: Transforming Data to Decisions using Geometry, Optimization, and Machine Learning
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Moo Sun Hong
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Department of Chemical and Biological Engineering, Seoul National University
Toward Predictive Bioprocessing: Hybrid Modeling and Digitalization Across Upstream and Downstream Systems
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Joel Paulson
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Department of Chemical and Biological Engineering, University of Wisconsin-Madison
The Prior is (Almost) All You Need: Physics-Informed Bayesian Optimization for Process Systems Engineering
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David E. Bernal Neira
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Davidson School of Chemical Engineering, Purdue University
Perspectives of Quantum Computing in Chemical Engineering
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Yunhong Che
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Department of Chemical Engineering, Massachusetts Institute of Technology
Intelligent health diagnosis and prognosis for Lithium-ion batteries
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Jinwook Rhyu
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Department of Chemical Engineering, Massachusetts Institute of Technology
Interpretable Approaches for Optimizing the Pulse Diagnostics and Formation Protocols for Lithium-Ion Batteries
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David Y. Shu
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Massachusetts Institute of Technology
Environmental life-cycle assessment for sustainable energy systems
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Joseph K. Scott
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Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology
Decomposition Methods for the Guaranteed Global Solution of Nonconvex Stochastic Programs: State-of-the-Art and Future Outlook
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Ali Mesbah
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Department of Chemical and Biomolecular Engineering, University of California, Berkeley
Learning Interpretable Control Policies with Local Actions and Global Optimality
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Alexander Dowling
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Chemical and Biomolecular Engineering/Applied & Computational Mathematics & Statistics, University of Notre Dame
Optimizing Experiments: From Data-Driven to Intrusive Model-Based Methods
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Yankai Cao
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University of British Columbia
Decoding control: scalable MPC approximation with decision trees
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Georgia Stinchfield
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Carnegie Mellon University
Optimal design approaches for rapid, cost-effective manufacturing and deployment of chemical process families
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Sergio Lucia
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TU Dortmund
Nonlinear model predictive control for anything: Designing fast, efficient, and safe neural network controllers
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Leo Chiang
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Dow Chemical
The future is now: AI and humans working in the loop in process systems engineering
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Can Li
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Purdue University
Intersection of optimization and machine learning in process systems engineering
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Dirk Lauinger
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Massachusetts Institute of Technology
Robust optimization and the law: Reducing battery mineral use through vehicle-to-grid
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Kristen Severson
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Microsoft Research
Decreasing the environmental impact of concrete with data-driven design
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Robert McAllister
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TU Delft
Distributional uncertainty and model predictive control
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Francesco Destro
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Massachusetts Institute of Technology
Process systems engineering for gene therapy manufacturing
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Saif Kazi
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Los Alamos National Laboratory
On optimal control of hybrid dynamical systems using complementarity constraints – discretization and globalization
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Calvin Tsay
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Imperial College London
Optimization approaches for bounding and certifying neural networks
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Shengli (Bruce) Jiang
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Princeton University
Polymer physics meets machine learning: a synergistic approach to complex polymer design