Snapshots from summer internships across the country

Lauren Smith

Aug 29, 2023

Over the summer, graduate students from the Department of Chemical Engineering interned in a variety of industries and labs across the country. A few of them shared snapshots from their experiences.

Sergio Bugosen

MS in AIE-ChE student

Los Alamos National Laboratory

Bugosen worked on chemical process modeling and optimization.

Seolhee Cho

Ph.D. student

Cho collaborated with National Energy Technology Laboratory to develop an expansion planning toolset for power systems. She restructured expansion planning code and added new modeling and solution capabilities. Cho also attended the Pan-American Advanced Studies Institute on Optimization and Data Science for Net-Zero Carbon and Sustainability (PASI2023) and the 11th World Congress of Chemical Engineering (WCCE11), in Buenos Aires, Argentina in June

Saaksshi Jilhewar

MS in AIE-ChE student

Universal Fuel Technologies

Working remotely as a process engineering intern, Jilhewar delved into existing calculation tools to understand their complexity and suggest improvements. She also collaborated to refine calculation algorithms. After a thorough study of existing algorithm applications, Jilhewar proposed alternative software tools and platforms. Additionally, she developed a system to efficiently manage experimental and calculated data, streamlining workflow.

Tom Krumpolc

Ph.D. student


As an intern with the real-time optimization group, Krumpolc developed novel, rigorous process models for refinery equipment, with the goal of determining the optimal set of target conditions for safe and economic operation subject to daily fluctuations in operating conditions.

Saroj Sathish

MS in AIE-ChE student

Shell Technology Center

Sathish was a process optimization team intern in Houston, TX. He developed and implemented computational models to predict reaction rates and product yields, explored optimization techniques for parameter estimation, and analyzed experimental data to validate and refine models.

Divyam Shah

MS-BTPE student


Shah was a product development intern at a research and development site in Tampa, FL. He optimized and developed new formulations and novel drug delivery systems.

Daniel Ovalle Varela

Ph.D. student

Verition Fund Management

Ovalle Varela worked as a quantitative analysis intern in New York City.

Javal Vyas

MS in AIE-ChE student


Vyas worked remotely as an intern with the process systems engineering team, making ML surrogates and embedding them in the larger optimization framework. He contributed tutorials to the Institute for Design of Advanced Energy Systems (IDAES) github repository. He also made surrogate model comparisons for the Pareto-project.

Xiaoxiao (Lory) Wang

Ph.D. student

NXP Semiconductors

Wang's internship project was related to uncertainty quantification in machine learning models. She helped to build a ML model for making predictions on the wafer process output. Wang implemented an algorithm to compute model uncertainty to better understand the model confidence level on each prediction. She also built an interactive dashboard to detect outliers in the process parameters and monitor real-time production.