Mason School Business Analytics Faculty Leads in Gender Diversity

Clinical Associate Professors Pamela Schlosser and Rachel Chung led Machine Learning I and Machine Learning II respectively during the fall 2020 academic session. It is the first time that female full-time faculty members at the Raymond A. Mason School of Business simultaneously instructed both of the machine learning courses for the Master of Science in Business Analytics (MSBA) programs.

Chung previously taught Machine Learning II during the 2019-2020 academic year, and also teaches business analytics in the full-time MBA program. Schlosser, who recently came to the Mason School from Roanoke College, is also teaching the topic at the undergraduate level.

Nationally, there is a significant gender gap among professors in the area of Operations and Information Systems Management (OISM), especially across the top university programs in the country. Less than a quarter of Artificial Intelligence (AI) professors are female and women make up only 12% of Machine Learning researchers.

The underrepresentation of female professors in STEM and analytics as a whole is a strategic focus of the Mason School and William & Mary at-large and compared to national statistics, the numbers are far more even; nearly 50 percent – or nine out of 20 – current OISM full-time faculty are women, including Chung and Schlosser.

But for Chung and Schlosser, bridging the gap of gender disparity is far less about an institutional numbers game and much more personal. Through their respective teaching and research, they hope to inspire and mentor female students to pursue STEM careers in business or academia, and make significant contributions to the overall field’s body of work.

Pursuing Technical Education

A native of Taiwan, Chung came to the United States in 1995 on a full scholarship to the University of Pittsburgh Ph.D. program where earned a Ph.D. in Psychology, a Ph.D. in Management Information Systems, and a Master’s of Science in Information Science. She also worked extensively for the Affect Analysis Group at the University of Pittsburgh that pioneered artificial recognition of facial expressions.

Over the last decade, she’s actively engaged with the business analytics community, serving as a visiting scholar at Vietnam University, on advisory boards, and as a faculty member in the OISM departments at several universities.

Since joining the Mason School faculty in 2019, Chung has instructed courses in machine learning, data analysis, and advanced modeling techniques. Her outstanding work with students was recognized in 2020 when she was awarded the MSBA Faculty Excellence Award.

“I’m still relatively new to the Mason School but I have enjoyed working with the students,” Chung said. “They are very committed, driven, and hard-working.”

Chung’s passion for teaching extends beyond the Mason School. She is currently working on a data science textbook with Dr. Jungwoo Ryoo of Penn State University – Altoona and MSBA graduate Rani Banjarian. Moreover, Chung is working with Banjarian and Chung’s sister Peggy Chung on designing and running Data Scientist Junior, a data science education program for children.

“We’ve discovered that children as young as nine years old love playing with data, especially using the visualization tool Tableau,” she said.

For Schlosser, who entered college several years after Chung arrived in the United States, little had been done to move the needle forward in terms of gender diversity as she pursued an information systems business degree at the University of Kentucky.

“There were five women out of 500 in one of my first classes I took in college and that was really intimidating,” Schlosser said.

Schlosser went on to pursue a master’s degree in accounting and computer information systems from Middle Tennessee State University and a Ph.D. in Management Information Systems from Clemson University before embarking on her career in teaching. Prior to coming to William & Mary, she served as an expert witness in legal disputes, and taught business and economics at Roanoke College.

“When I was going to school, I tried to get as many perspectives on analytics as possible because in 2009 there were a lot of programs that represented applied statistics but it wasn’t quite analytics,” she explained. “All of the colleges I went to and the programs I studied in were technical but I’ve positioned myself professionally to be in business programs which is interesting for machine learning because the problems I present to students are applied and complex, and use real data sets.”

Research-Driven Academics

While both professors are deeply committed to their machine learning and business analytics students at the Mason School, they are both additionally pursuing research related to the field.

Chung’s interests include financial fraud, business analytics, and knowledge management, and she has published in top journals and given numerous presentations at international conferences. She is actively working on a few studies, including one that examines the use of blockchain visualization in supply chain management alongside fellow Mason School professor of business analytics Dr. Amy Xia and Nicola Ibba, a 2020 graduate of the full-time MBA program.

She has also just finished presenting a work in progress on finance data visualization for the ICAIF ’20 ACM International Conference on AI in Finance, along with Dr. Stephanie Rosenthal of Carnegie Mellon University. They studied how accountants and data scientists differed in ways to validate financial data for AI modeling.

Schlosser’s initial interest in data research was related to statistics but it has since evolved to include research methods, business analytics, and human behaviors with technology. Her dissertation was focused on technology stress studies and comparing the physiological and psychological markers of her human test subjects.

“My research isn’t rooted in machine learning. It’s more about the psychological perspective of technology and how we use it to satisfy our needs,” Schlosser explained. “Over the last ten years since my dissertation, I have run numerous studies using both survey and experimentation methodologies. It is tricky to experiment with pandemic-related restrictions, but I’m looking forward to picking back up those studies when the time is right.”

Supporting Gender Diversity at the Mason School

At a time when diversity and inclusion is a conversation at the forefront of many major universities, improving gender disparity – particularly among students pursuing STEM degrees – is a component of that discussion.

Approximately 55 percent of university graduates are female but a little over a third pursue degrees in STEM. Women are even more vastly underrepresented in STEM, data science, and AI fields as females compose just 28 percent of the science and engineering workforce.

The Mason School tells a more promising story, however. In addition to a nearly 50 percent male to female gender breakdown among OISM faculty, enrollment of women in the residential MSBA program has held steady at, or slightly above, 30 percent since the program launched in 2017. The number of women in the online MSBA program has steadily increased from 14 percent female in the inaugural cohort to 25 percent in summer 2020.

Chung and Schlosser are among the faculty members who are leading by example by instructing some of the most technical courses the Mason School has to offer its students, and they are continuing to look for subtle ways to encourage more female students to pursue careers in analytics.

“As a woman of color, I care deeply about diversity and inclusion. I serve on the Diversity & Inclusion Committee at the Mason School for this particular reason,” Chung said. “I’ve always been inspired by Lenore Blum’s work on supporting female computer science students at Carnegie Mellon University, and I am trying to implement her research-based methodology which focuses on intentionally engineering the support system that typically emerges organically for male students in male-dominated fields.”

Schlosser says her experience as a female student in predominately male-led programs is what drives her to connect with her own female students and help break down barriers to entry into the field.

“I have women students come up to me and say, I like the field, but I’m the only girl in my class. I tell them, I’m a woman and I’m here in this field so if I could make it, you can too,” she said. “I think what’s great about William & Mary is that we have small class sizes so it’s not as intimidating as five against 500 but it can still be challenging. I tried not to think of myself as the only woman in the group, that was what it was. Now I can say, look around, there’s all these female professors and we really enjoy the field. It was a matter of challenging some peoples’ perspective of women in the workplace and over the last ten years it’s become very normal for women to be in professor positions.”

Forbes, "Women are the key to scaling up AI and data science," March 16, 2020