Business Professor Cites Passion as Asset in Teaching

“Teaching cannot be just a job. Instructors have to be genuinely interested and passionate about the course they’re teaching, and genuinely care about the students,” said Durai Sundaramoorthi, Senior Lecturer in Data Analytics at Olin Business School at Washington University in St. Louis. Professor Sundaramoorthi teaches a statistics course to undergraduate students, and a predictive analytics course for graduate students.

Professor Sundaramoorthi’s research focuses on Business Analytics, Data Mining, Optimization, Simulation, and Simulation-based Optimization. His findings have diverse applications including optimizing nurse-to-patient assignments at a regional hospital in Texas, and helping select ideal seed varieties for farmers.

Professor Sundaramoorthi has B.S. in Mechanical Engineering from Bharathiar University in India. He earned an M.S. in Industrial Engineering from the University of Texas at Arlington and a Ph.D. in Industrial Engineering (Analytics) from the University of Texas at Arlington. He conducted his doctoral research in the Center on Stochastic Modeling, Optimization, and Statistics (COSMOS).

Professor Sundaramoorthi previously taught at the engineering school at Missouri University of Science & Technology. He joined the faculty at Olin Business School in 2011. In 2015, Professor Sundaramoorthi won an Emerson Excellence in Teaching Award from Washington University in St. Louis.

In an interview with the Teaching Center, Professor Sundaramoorthi discussed his approach of helping students find solutions to complex problems without revealing the answer. He also emphasized how passion for a subject can translate into good teaching.

How did you become interested in data analytics and management?

It started when I was an undergraduate mechanical engineering student in India. I took a course in junior year called “Operations Research.” I was always interested in math since I was a kid, but operations research was a different kind of math; it was very practical. It asked questions like, how do you optimally assign people a task, or optimally utilize resources? The instructor in the course asked me to make a presentation on a topic in operations research. After I spent time focusing on the topic, I realized I liked that area of math.

After graduation, I forgot about the project. I started to consider pursuing a Master’s degree in in the U.S. In U.S., there’s a field called Industrial Engineering, or Systems Engineering, which isn’t as common outside the U.S. In industrial systems engineering, we don’t make or design anything; we make products more efficient. We use techniques like operations research to make things efficient. For example, many navigation apps today use operations research to find the best possible routes for users. Once I realized that Operations Research and Data Analytics is a major component of Industrial Engineering curriculum, I chose to pursue my graduate education in Industrial Engineering.

What is an example of a project you’ve worked on using data analytics?

 In 2016, an agriculture company called Syngenta organized an analytics competition. My colleagues and I participated. They gave us data on seed varieties, and we built models to predict the yield of these varieties under different weather conditions.

Then, considering factors such as the expected yield of the seeds, how much risk the farmer wants to take, and different weather scenarios, we created a portfolio of seed varieties for the farmers, similar to stock investment. We won the Olin Award for this project in 2018. We also have a paper about the project that’s still under a review.

What influenced you to become an instructor?

 During graduate school, I worked as an intern for a couple companies. The internships were exciting and I got to apply what I learned, but there were restrictions on my work. Interns and, in general, all the employees had to focus on the problems the company wanted us to solve. I noticed that my professors were free to work on different problems.

When I started teaching, it was in the engineering school at Missouri University of Science & Technology. I wasn’t very satisfied teaching in an engineering school because it’s not as applied as business school. So, I made the switch.

What are some of the challenges of teaching machine learning, and how do you address them?

Recently, I’ve focused on teaching a machine learning class for graduate students. McKelvey School of Engineering also offers machine learning courses, but the business school class does not focus as much on mathematics. Our focus is not to dive deep into mathematical aspects of the methodology, so I have to strike a balance between mathematical rigor, intuition behind the methodology, and business applications. I can sense that some students want to go deeper into math, and others don’t want to go to the level of math I use in class. There’s a spectrum of students, which is a challenge. At the end what I do is choose a good textbook, and get to know my students to decide how mathematically rigorous I can be in a course.

What is your favorite class to teach, and why?

I’ve always loved teaching statistics, but I’ve taught it more than 50 times now. I don’t have many challenges, so I’ve decided to focus on teaching machine learning. I don’t want my boredom to affect my teaching. If I’m bored with a subject, students can sense that.

I feel now about predictive analytics the way I felt about teaching statistics when I came to WashU. It’s a much more complex class than undergrad statistics, but I’m intrigued by details and methodologies. Things change. You might be interested in something for a number of years, and then you want to move on.

Team-based learning is an integral part of education at the business school. How do you help students learn in these situations?

In our Center for Analytics and Business Insights and Center for Experiential Learning, we get projects from companies for student teams to work on. At the end of the semester, they present a solution or recommendations based on what they find in their research. Most of the projects are data intensive. I advise students throughout the process.

Students use a program for machine learning while they’re working on the projects, and sometimes problems arise in syntax or computation. I would never sit with students and debug their code, because that’s part of learning. I give them hints instead. If students are working on complex computation, I advise them to break the code into smaller chunks to check parts before they move to the next one.

What are some of your proudest teaching moments?

Recently, I’ve tried to involve students in my research. They gain experience working on data-driven, real-world projects. When they get a job or internship, several of them come back and say, your course was very useful, we use what we learned at work. I’m very excited about that feedback.

I once worked at an open admissions school. Many students were nontraditional college students. People say it’s hard to teach students like that, especially in a course like statistics, but I never believed it. A few students took the class and ended up publishing papers with me and making presentations at conferences. Some students went on to pursue graduate studies at good schools. One student who co-authored a paper with me went on to get a full ride to pursue an MBA at Drake University. When you are able to play a part in making such an impact on someone’s life, it keeps you in academia.

Who are some of your favorite teachers, and why?

Dr. Victoria Chen (Professor of Industrial Engineering at UT Arlington) is one of my favorite teachers. She was one of the co-advisors on my dissertation. I follow her style of teaching a lot. For example, in a stats class she was teaching, she would bring partially-filled handouts, and students would fill in the blanks as she covered material. I do this now, too, because it keeps students engaged with a topic. Dr. Chen would also stick to what she was passionate about. Unless she was genuinely interested in a topic, she didn’t pursue it.

Another instructor I had in grad school, Dr. Bill Corley (Professor of Industrial and Manufacturing Systems Engineering at UT Arlington), is a genius. I took 12 courses with him and I would always ask questions during class. He gave me an award for keeping him on his toes. He would say, “Ideas are cheap.” The lesson there is, you have to keep working on the idea until something materializes from it.

Dr. Jay Rosenberger also inspired me a lot. He was my other dissertation co-advisor. During my doctoral dissertation, I was stunned by his coding skills in optimization. I was stuck on a code for a few weeks. He sat with me and got it going in a couple of hours. He is outstanding in optimization and coding. He comes from Professor George Dantzig’s academic family. I proudly say that I also belong to George Dantzig’s academic family because of being Dr. Jay Rosenberger’s student.

Another favorite teacher is my high school math teacher, Mohammad Ali. I was doing extremely well in math class. I would score 100% in math but score below average in other subjects. He got me to focus in all the subjects. He challenged me to be well rounded person. That helped me a lot.

What is your advice for aspiring instructors?

Teaching cannot be just a job. Instructors have to be genuinely interested and passionate about the course they’re teaching, and genuinely care about the students.

Interview has been edited and condensed.