The Ken Kennedy Institute's May Member of the Month, Dr. Eric Chi, is an Associate Professor of Statistics at Rice University. Dr. Chi received BA and PhD degrees from Rice University and an MS from the University of California, Berkeley. His PhD studies were funded through a Department of Energy Computational Sciences Graduate Fellowship (DOE CSGF). As part of this fellowship, he completed research practica at Sandia National Laboratories and Lawrence Berkeley National Laboratory. After his PhD, he completed postdoctoral positions in the Human Genetics department at UCLA and the Digital Signal Processing group at Rice University. Before joining Rice, he was an Assistant Professor in the Department of Statistics at North Carolina State University (2015-2021).
Dr. Chi's research areas include; statistical learning, numerical optimization, and their application to analyzing large and complicated modern data in biological science and engineering applications.
What is your favorite book?
That’s a difficult one so I’ll do what we do in research when we run into difficult questions and answer a more feasible question in the neighborhood of the original question. A recent book that I greatly enjoyed reading is “Our Dearest Mother” which is written by Wendy Chan, the mother of our own Jesse Chan from CAAM. The book is about Jesse’s grandmother and her early life in China, the danger and challenges she faced due to the Chinese civil war, and the amazing escape with her family to Taiwan to save and restart their life. It’s not a long read, but it puts lots of things in perspective.
How do you explain your research in one sentence?
I develop computational algorithms finding low dimensional and interpretable latent structure in data.
What is your favorite aspect of your research?
I enjoy the interplay between theory and practice. There’s a simple joy in developing algorithms that behave well on paper but also admit robust and scalable realizations in code.
What challenges do you see in your research that you didn't expect?
One challenge that was hard to anticipate until I became a PI was writing compelling (and fundable!) grants. I have certainly not figured this all out, but I have to say that I have learned to enjoy (in some part) the process of grant writing. It forces me to take a step back and visualize my research efforts over a longer scale and to take a hard assessment of the impact and value of the work I do. I wouldn’t say what goes on between start and finish is all fun (it’s not), but it can also be energizing to go through the exercise of convincing yourself (and hopefully a panel of reviewers!) that you’re doing something that’s worth supporting.
How do you see computation and data advancing in the future?
It’s pretty remarkable what machine learning can do with today’s vast data resources and computational capabilities, but it’s also starting to get concerning. For example, while it’s cool to see deep learning methods used to transfer artistic styles onto photographs, it’s also somewhat disturbing to see how successful these kinds of strategies can potentially be at producing deep fakes. Efforts to work out the ethics and responsible use in designing and using these algorithms will become increasingly more important.
