Congratulations to Dimitri Alston for receiving the Academic Year ’22-23 Graduate TA Award in recognition of his exceptional performance as a TA in the Department of Chemical & Biomolecular Engineering.

Congratulations to Robert Gottlieb for receiving the 2023 Research Excellence Award from the Department of Chemical and Biomolecular Engineering!

On April 19, the UConn Board of Trustees voted to approve Prof. Stuber’s promotion to Associate Professor with Tenure.

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Congratulations to former PSOR Lab member, Matthew Wilhelm for his recently published article in the Journal of Optimization Theory and Applications! This paper develops a new and improved approach for relaxing composite bilinear functions (i.e., f(x)=g(x)h(x)) that leads to a significant speedup in solution times for deterministic global optimization. Check it out: https://doi.org/10.1007/s10957-023-02196-2 or Read-only share link

Congratulations to Dr. Matthew Wilhelm for successfully defending his PhD dissertation!

The PSOR Lab is excited to welcome our newest graduate students, Nia Samuels (MS Student) and Dimitri Alston (PhD Student). Welcome to the lab!

Congratulations to Matt Wilhelm and Chenyu Wang for their recently published article in the Journal of Global Optimization! This paper develops and analyzes convex and concave envelopes of artificial neural network activation functions for deterministic global optimization applications. Check it out: https://doi.org/10.1007/s10898-022-01228-x

Congratulations to Chenyu Wang and Sam Morgenstern on their recently published paper in AIChE Journal. This paper develops a framework for optimizing the therapy for cancer patients with solid tumors. https://doi.org/10.1002/aic.17747

Congratulations to Dr. Chenyu Wang for successfully defending his PhD dissertation!

Congratulations to Chenyu Wang and Matt Wilhelm on their recently published paper in Industrial & Engineering Chemistry Research. This paper formalizes a special class of robust optimization problems for systems modeled using machine learning models and first-principles models. https://pubs.acs.org/doi/10.1021/acs.iecr.2c00113