About Me

I am a fourth year PhD student in Mechanical and Aerospace Engineering at UC San Diego, working with Prof. Boris Kramer. My current research focus is data-driven reduced-order modeling. I have worked on applications of char combustion and semiconductor manufacturing, and now focusing more on an exciting topic of capillary wave turbulence!

Recent News

June, 2026 I attended the “2026 URSSI summer school” on research software engineering in Boston. I’m very grateful that I learned all the good software development practices, including software design, modularity, collaboration via GitHub, software testing, packaging/distributing Python software, etc. This valuable experience will help me carry these skills forward in my future career, allowing me to write more reliable, maintainable, and collaborative software as a researcher and a developer.

May, 2026 I’m looking forward to presenting our work “Physically consistent reduced-order modeling by enhancing Operator Inference with state constraints” at the WCCM-ECCOMAS 2026 conference in Munich! Please feel free to attend my talk in the session “MS016B: Scientific Machine Learning for Nonlinear Model Reduction II” on July 23rd, from 09:45–11:15am in Room H4–2 (Leopold).

May, 2026 I’m excited to share that my second journal paper, “Parametric Operator Inference to Simulate the Purging Process in Semiconductor Manufacturing”), has been accepted to IEEE Transactions on Semiconductor Manufacturing, and it is now available through Early Access! In this work, we apply parametric Operator Inference, a data-driven reduced-order modeling framework, to simulate the purging process in semiconductor manufacturing. This approach allows for fast and accurate predictions of the purging flow dynamics inside the PECVD chamber, with potential applications in particle contamination control in process optimization in the semiconductor manufacturing setting. The final published version will be available soon.

October, 2025 I’m very excited that my first Journal paper is published in JCP (“Physically consistent reduced-order modeling by enhancing Operator Inference with state constraints”). In this work, we embedded physics-preserving constraints to the Operator Inference reduced-order modeling for an application of char combustion, so the model always guarantees to generate physically consistent predictions on species concentration. This helped us to get an additional benefic of long-term stability, compared to other methods introduced in the paper.

September, 2025 I recently completed a summer internship in the Computational Fluid Dynamics group at ASML San Diego, where I worked on developing reduced-order modeling algorithms for the EUV light source. It was a fantastic experience to contribute to cutting-edge technology at one of the world’s leading semiconductor companies and to apply research skills I learned during my PhD.

July 21th-24th, 2025 I’m grateful to have attended the USNCCM conference in the beautiful city of Chicago, where I gave a talk on my work “Physically consistent reduced-order modeling by enhancing Operator Inference with state constraints”. It was inspring to see so many exciting works and to meet new friends in the community.

June 16th-25th, 2025 I am attending Structure-Preserving Scientific Computing and Machine Learning Summer School and Hackathon at the University of Washington, Seattle. I am very excited about this opportunity to learn more about many topics on scientific machine learning, such as dynamical low-rank algorithms, operator splitting methods, and neural operators/ODEs and their applications.

April 26th, 2025 It was a pleasure to give a talk at the Southern California Applied Mathematics Symposium 2025, hosted by UC Riverside, on “Physically consistent reduced-order modeling by enhancing Operator Inference with state constraints”.

April 8th, 2025 Our preprint “Parametric Operator Inference to Simulate the Purging Process in Semiconductor Manufacturing” (with Seunghyon Kang, Boris Kramer) is available online on arXiv.

February 7th, 2025 Our preprint “Physically consistent reduced-order modeling by enhancing Operator Inference with state constraints” (with Boris Kramer) is available online on arXiv.

September 20th, 2024 I had the pleasure of visiting Prof. Dongjin Lee at Hanyang University, South Korea, a former postdoctoral researcher from our group. I am very grateful for the chance to give a talk on Operator Inference—a data-driven reduced-order modeling approach—for a char combustion problem, hosted by his group.

September 9th-13th, 2024 I am grateful for the opportunity to attend the Model Reduction and Surrogate Modeling (MORe) 2024 conference, which was hosted by my university at the beautiful location, the Scripps seaside forum, in La Jolla.

April, 27th, 2024 It was great to attend the Southern California Applied Mathematics Symposium (SOCAMS) 2024, hosted by my university.

December 21st, 2023 I passed my Departmental Qualifying Exam (DQE) in linear systems theory, numerical linear algebra, and computational fluid dynamics. Many thanks to my advisor Prof. Boris Kramer, and committee members Prof. Patricia Hidalgo-Gonzalez and Prof. Oliver Schmidt.

July 24th-August 4th, 2023 I attended the Space Weather Summer School 2023 at the University of Michigan, Ann Arbor.

September, 2022 I moved to San Diego, CA from Seoul, South Korea to pursue a PhD in Mechanical and Aerospace Engineering at UC San Diego!