Loyola University Chicago

College of Arts & Sciences

Spotlight On: Swarnali Banerjee

Dr. Swarnali Banerjee, Assistant Professor in the Department of Mathematics and Statistics, received a NIH grant to support interdisciplinary research into the cause of UTIs

Swarnali Banerjee from the shoulders up smiling at the camera and standing in front of a whiteboard with math equations written on it

Dr. Swarnali Banerjee, Assistant Professor in the Department of Mathematics and Statistics in the College of Arts and Sciences at Loyola University Chicago, recently received a $410,867 grant from the National Institute of Health (NIH) to support her ongoing interdisciplinary research into the cause and treatment of urinary tract infections (UTIs) combining applied statistics with bioinformatics.

“This prestigious grant will empower Dr. Banerjee and her team to pursue a truly interdisciplinary and collaborative approach to aid in key scientific discoveries,” said Peter J. Schraeder, Dean of the College of Arts and Sciences at Loyola University Chicago. “Her innovative approach to bringing her expertise in applied statistics for this research will not only contribute to student enrichment at Loyola, but it will also lead to a positive impact on public health and women’s lives.”

Read on to learn more about Dr. Banerjee and her plans for the grant below.

What is the focus of your work at Loyola?

In the early years of my work, I was focused primarily on the theoretical aspects of probability within math and statistics. I would sit and try to prove different theorems. Over time, I slowly started working more and more with students. Eventually, I became involved with applied statistics projects. Working with people like doctors, psychologists, ecologists, and biologists on these applied projects helped me realize that my work can solve real problems and positively impact real people, like treating women with UTIs. I’m now working on about 12 interdisciplinary projects with undergrads, graduate students and academics across the Bioinformatics Program, the Department of Psychology, Stritch School of Medicine, and the School of Environmental Sustainability. The work varies quite a bit, but I love it. I have a problem, I have the data, and now I need to solve the problem. I’m able to have a balance of data-driven research and proving theorems. It’s like my own style of research.

What drew you to study and teach statistics?

I love probability and I love, love, love to teach. It wasn’t until I was in my PhD program that I came to love research, too. My family played a big role in my career today. I come from a big family of teachers and professors, so teaching is all I’ve known my whole life. My father is a mathematician and I saw him tutoring students all the time growing up. My mother said that when I was learning to talk, I would say things like “dx” and “dy” because my father was teaching derivatives in the next room.

I also think that great professors can make and shape your life. Statistics is a field that a lot of students must take classes in during their undergraduate degree and every college has a basic statistics class. Most people I’ve talked to went through stats and came out thinking it was really difficult. But there’s a flip side here. I fell in love with statistics when it first came to me in 11th grade. I had the most engaging professor, Professor Giri, for that first class. At that age, you usually just want class to be over, but I was actually sad when it ended – it was absolutely magical. I’ve tried to channel that professor and make my classes engaging. I’ve interacted with so many students who take my class and then start thinking about switching into stats and want to know what I’m teaching next semester. It makes me feel ecstatic to see that.

What are your plans for the grant you received from the National Institute of Health?

I started working on this project with Dr. Catherine Putonti from the Bioinformatics Program a few years ago to try to understand why UTIs happen so commonly in women. There is no single, defined reason or cause why UTIs happen or why they can happen so often. Many papers by different sources, including ours, have identified that uropathogenic E. coli or “UPEC” may be a cause. And through many projects in Dr. Putonti’s lab, they have identified certain bacteria that may inhibit the growth of E. coli, like Lactobacillus.

But the research question for this grant proposal was how? How is it exactly that these different bacteria inhibit the growth of E. coli? To understand the mechanics of it, we created a software tool that works to identify if the sheer presence of Lactobacillus growing in the urinary tract can trigger dormant phages (bacterial viruses) in urinary E. coli strains to replicate and kill their E. coli host. We also want to know how Lactobacillus does this. Is it through production of hydrogen peroxide? or lactic acid, which lowers the pH level of the urinary tract?

This grant will allow us to collect data at a larger scale with more granularity. While the experimental application and bioinformatics side of this project is important, the data analysis portion is so central. We can only make sense of this data through statistics. We can come up with statistical answers through modeling to identify the why and how. This is why our research is truly interdisciplinary. This work has been really exciting for me because I really feel like this work matters and could eventually help a lot of women. 

Will there be any opportunity in this project for student involvement?

Absolutely. It’s always been a blessing to have students working on this project with us. Although this project has advanced parts, there are definitely portions across bioinformatics and statistics where we can involve all student levels.

The first year of this project will include a lot of data collection. Once data comes in, the statistics portion will have undergraduate-friendly research topics like tests, data wrangling, and exploratory data analysis. More advanced research questions, maybe more suited to graduate students, will include things like time series modeling. I think students will enjoy that there’s an unknown element to this research, so we will just have to see what the data tells us. 

What else is on the horizon that you are excited for?

A couple of things! I really love teaching my statistical consulting course. I’m so grateful that Loyola has a course like this. It’s the capstone course for our Applied Statistics graduate students. During a semester, a student is part of a small group that works with real world clients that give us data and research questions to solve with me as a mentor. While this course is great for students who might want to go into academia or research, it’s fantastic for people going into industry. In class, you see these perfect datasets where everything falls into place. But in a job, there will be unique challenges that you don’t encounter in the classroom. It also gives students the opportunity to talk to people who aren’t statistics experts, like medical students or people who own real estate companies. Students get real-life experience by learning how to communicate with the client and figure out how to approach the client’s challenges mathematically. 

The other thing I’d like to mention is my recent work and papers applying sequential analysis, a typically theoretical area of statistics, to machine learning. The talk of today in my field is mostly about big data and how we need data compression to process this big data. I wanted to figure out how I can use the algorithms that I have built in sequential analysis and apply them in high dimensional data for data compressions. This work is exciting because it’s taking a theoretical idea and applying it to relevant, contemporary issues in a novel way that the field hasn’t explored yet.