During the summer of 2020, undergraduate student Enrique “Rick” Nueve was part of the Science Undergraduate Laboratory Internships (SULI) program at the U.S. Department of Energy’s (DOE) Argonne National Laboratory. What started out as a summer internship has turned into a year-long research experience with the SAGE project — and the opportunity to develop, publish and present two separate research papers.
Nueve presented his research paper on nowcasting in January and April, covering his summer research, and will present a second research paper — covering his current research on active learning techniques — this May in a workshop. Having graduated from Northern Illinois University in July 2020, he plans to start his doctorate this fall.
“I think that through the internship at Argonne, I was able to get out of my comfort zone and was able to build some self-reliance to solve complex problems.” — Rick Nueve, SULI Intern
In the following Q&A, Nueve shares his internship experience at Argonne, and how it has shaped his path forward as a scientist and researcher.
- Thanks for taking the time to share your experiences, Rick. To start off, how did you become interested in science?
I really became interested in science during my freshman year of high school. Before then, I actually wasn’t personally interested in school to begin with. During my freshman year, I took a general science course that covered some basic kinematics and chemistry. It was the first time in school that I thought I was learning something that was somewhat applicable or could be used. At the time, I was very pragmatic in my thinking. This led me then to taking more science-related classes — and in particular, math classes. I actually found I enjoyed math more and ended up majoring as an undergraduate in statistics.
- How did you learn about the Science Undergraduate Laboratory Internship, and why did you choose to take part in the program?
During my senior year of college, I worked on a time-series classification research problem — identifying what a sequence of data samples will look like given a limited set of potential outcomes. The data were very unruly, high-dimensional, non-stationary, sampled irregularly, etc. With my limited knowledge at the time, I had no clue what kind of model or data preparation method to use on the data. I attended a rather small institution for undergraduates, so nobody with this kind of expertise was available.
On the other hand, I knew about Argonne National Laboratory, given that I am from Illinois, and I decided to start searching for someone to email to discuss this problem with. I ended up getting a reply from Charlotte Haley in Argonne’s Math and Computer Science Division. This researcher was actually the one who informed me about the SULI program and recommended that I apply. I did so and then ended up getting the position for the summer of 2020.
- What did you research during your summer internship in 2020? How did you contribute to the ongoing scientific developments in that field?
I ended up getting a position on the SAGE project, an initiative to develop a cyber-infrastructure for AI at the edge, essentially running machine learning models on devices like routers that remotely connect up with a network. As an undergraduate, I taught myself some basic machine learning and did it as a hobby, which led me to working as a research assistant in machine learning. This gave me the background needed for the SAGE project.
I was tasked with designing a model that used multiple sources of data as input over an edge device. This was really a demo project but I wanted to make the most of it and try to publish a paper based on the work. This led me to developing a method for nowcasting net radiation over an edge device and publishing a paper on the work.
- What was it like to do an internship at Argonne?
It was rewarding but also challenging. By no means does undergraduate coursework prepare you for independent research. There is almost no training, and although frustrating, this is necessary so that you learn to solve problems on your own. In research, you are going to try to answer questions that nobody knows the answer to. In school, there is always a safety net. If you get stuck on a problem, you are able to ask your professor. I think that through the internship at Argonne, I was able to get out of my comfort zone and build some self-reliance to solve complex problems.
- Were you able to handle the challenges of working remotely for the internship?
Yes, I was able to handle it but it did come with some unique challenges. Given that I was fresh out of undergraduate studies, I didn’t know too much and often came up against many tough challenges. It’s important to work through the problems yourself in order to grow — yet, I also recommend reaching out to fellow interns and your advisors. Remote doesn’t necessarily have to mean alone.
- Tell me about how your time with Argonne ended up expanding beyond the original summer internship.
At the time, I had just finished my undergraduate studies and had plans to attend graduate school. However, in order to raise my chances of getting into a competitive program, I wanted to bring up my entrance exam scores, and I planned to take a year between undergraduate and graduate school to do that. I told my advisor at the time this, and Argonne offered me the chance to stay on for the coming year until returning to graduate school. This was a great opportunity. I had time to study for the entrance exams, continued getting research experience, and ended up with offers for doctorate programs for this coming fall.
- How has the rest of your time with Argonne gone for you since the end of the summer? What scientific areas are you exploring now?
It has been a great learning experience and also provided a lot of personal development. The SULI program is ten weeks; however, my new appointment is for a year. The research I do now has a much longer timeframe, which brings unique challenges. There are challenges on a mental level to try to stay focused and develop insights over an extended period of time, but also motivational challenges. Research is by no means a straight line. There are lots of ups and downs.
In work I do now, the topics are in the areas of active learning and out-of-distribution detection over an edge network. I greatly enjoy these topics and the novelty of the problems.
- Tell me about the couple of science research papers you have written using your experiences at Argonne. What steps did you need to take to publish each of them? How did your mentors help you through the process? How did you prepare for presenting your research at conferences, and how did you feel after the presentations?
I am so happy that I was able to publish, and it felt great after presenting. This is my first publication where I was the primary author. Honestly, during the process, given that I had never published before, I wondered if I was going to be able to pull it off. Never before had I done something so open-ended. To be successful, I did a lot of initial planning of what the research was going to provide in terms of insight to the scientific community. In my head, I thought I needed to come up with some revolutionary method, but that was not the case. Most scientific publications are not revolutionary, and given where I am in my career, I needed to scale down my aspirations.
One of my advisors, Rajesh Sankaran, told me that a publication can contribute in three key ways: if it provides more insight into how to solve a problem, how one way does not work to solve a problem, or how to propose a new problem. By no means did the method I made up have state-of-the-art results; however, it was rigorously backed by scientific principles and proposed a truly novel way of addressing a problem. My method will not replace current methods; however, given its insights into a unique approach, it can serve as a stepping stone for future work.
- How have your experiences at Argonne and your work on your research papers helped you grow and develop as a science, technology, engineering and math (STEM) researcher?
A key lesson I learned is that patience and persevering in science is what is crucial to solving problems. I need to be patient and non-judging of myself through the process. There is no one path to solving a problem. Honestly, I’ve learned that having a sharp emotional intelligence is just as important to solving problems as a domain background.
- How does your research with Argonne fit with your career goals? What do you have in mind for your future?
I plan to begin a doctorate program in the fall in Computer Science in the area of machine learning theory. I love machine learning and all the new applications that are being found; however, I think the area of theory is a bit soft at the moment. I am interested in learning guarantees dependent on the used model but also dependent on what kind of data is used.
- What has been the best part of working with Argonne?
I have been able to explore new areas that I never had the time to in school. I learned what areas interest me and what areas I have no interest in.
- You have been involved in some amazing and critical scientific fields where research is ongoing. Where do you see the science you’ve studied going in the future?
I hope to see further progress in the application of machine leaning in a deployed setting. A lot of academic work in machine learning is benchmarking over clean data sets. Areas such as active learning, mode interpretability (understanding why machine learning models make certain predictions), online learning and concept drift detection need further work so that machine learning can be used in commercial applications.
- Thank you again for your time, Rick. Finally, do you have any outside interests or hobbies?
I really like sci-movies and just trying new things with friends. Monday to Friday, I am a scientist, but on weekends, I like to watch movies and get coffee with friends.
This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI); Laboratory Directed Research and Development (LDRD); and the National Science Foundation (NSF).
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