The Next Challenge - Profile Rui Fang

As CSEM Graduate Student Rui Fang approaches the end of her fifth year at the Oden Institute for Computational Engineering and Sciences, her journey through the realm of scientific machine learning reveals a narrative of persistence, discovery, and ambition. Rui, a dedicated Computational Science and Engineering Mathematics (CSEM) graduate student, has been navigating the intricate world of Hamiltonian systems under the mentorship of Richard Tsai, Oden Institute core faculty and mathematics professor.

Rui’s research is rooted in the field of scientific machine learning. Her focus is on optimizing simulations for Hamiltonian systems, a fundamental class of physical systems in classical mechanics. The goal of her work is to tackle the computational inefficiencies that arise in multi-scale simulations, where both time and spatial scales vary significantly.

“Simulating multi-scale problems requires very fine grids, which can be computationally expensive,” Rui explains. “We’re working on improving efficiency through parallelization in time and using machine learning techniques to approximate numerical integrators. It’s a challenging but exciting endeavor.”

Parallelization in time, one of Rui’s main strategies, involves dividing computational tasks into smaller chunks that can be processed simultaneously. This is particularly tricky due to the causal nature of time-based simulations, where each time step depends on the previous one. On the other hand, incorporating machine learning models—especially deep neural networks—into simulations aims to reduce the cost of evaluating complex functions, offering a novel approach to enhancing computational performance.

Beyond her research, Rui has been enjoying some new hobbies. 

“I recently learned how to crochet and knit,” said Rui. “ I started crocheting for the first time in 2023 when I finished my thesis proposal, because, you know, it's quite a milestone. I wanted to find something new to fill my time with. I started crocheting some toys, and several months later, I began to learn how to knit and started knitting myself scarves and sweaters. That's very fun.”

 

Though her schedule is now too jam-packed to sing in a choir as she did while pursuing her masters degree at Harvard University, Rui still has fun singing karaoke with her friends. She also feeds her love of music by playing classical piano and guitar. 

“Now, I am practicing the Goldberg Variations,” said the grad student. “I just barely started the second iteration. I think it's very difficult to control the tempo and dynamics for that piece. There is a lot of emotion in it.” Difficult it may be, but this is far from the first time that Rui has applied herself to a challenging puzzle. 

Her academic journey has been complemented by industry experience, having interned with both Amazon and Google. At Amazon, she worked as an Applied Scientist Intern, focusing on fraud detection using machine learning, an experience that expanded her skills in applying theoretical knowledge to practical problems. During her time at Google, she contributed to building a recommendation system for Google Cloud products. 

Looking ahead, Rui is leaning towards a career in industry, where she hopes to continue to bridge the gap between theoretical research and practical application. As she dives into this next chapter of her career, Rui Fang’s dedication to her work and passion for new challenges will no doubt serve her well.