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
ExxonMobil
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
Lonza
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
KeyLogic
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.