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Peter Glynn: Strategic investments in the people of MS&E have been central to its strength

The former MS&E chair recalls building strength in computational social science and other areas by capitalizing on expertise in mathematical models and behavioral sciences.

Peter W. Glynn led the Department of Management Science and Engineering from 2011 through 2015, overseeing an entity created 11 years prior by merging the departments of Industrial Engineering, Operations Research, and Engineering-Economic Systems.

Professor Peter Glynn | Image courtesy of STVP

An expert in computational algorithms and optimization, Professor Glynn is currently the Thomas Ford Professor in MS&E and a Fellow of the Institute of Mathematical Statistics and INFORMS. In 2012 he was elected to the National Academy of Engineering. Today, Professor Glynn advises doctoral students, pursues research in stochastics, statistics, and simulation, and teaches courses MS&E 221 and 324 in Stochastic Modeling and Stochastic Methods in Engineering.

What were among the most important initiatives you undertook as chair of MS&E?

I led the department with then-Associate Chair Stephen R. Barley (now an Emeritus Professor of Technology Management at the University of California, Santa Barbara). We'd consult every day, and he deserves a major share of the credit.

Our department was given the opportunity to make five junior faculty hires. Hiring is an investment in people who will hopefully have career durations of 30 or 40 years, and this has enormous long-term implications for a department. Steve and I led the department faculty through months of deliberations to craft a strategic plan for our hiring.

Lots of departments in the world do behavioral sciences well, lots do computational and mathematical modeling well, but not so many have both strengths. We recognized that we were a unique department with both. That meant that we could do things that would be challenging to implement within other departments at Stanford or elsewhere.

During my term, we saw that with the popularization of social networks and social media, there were new scientific questions about healthy social communities and new sociological insights that could be inferred from the data collected through digital social communities. These questions would be important to the world at large. "Computational social science" is the exact name for the area and we made an early investment in it. We hired two professors.

Computational social science leverages our strengths in quantitative model building on the one hand and behavioral sciences on the other, allowing crossover research at the intersection. Computational social science will be an important area going forward for decades to come. It's also a source of intellectual glue within MS&E and will help in recruiting others who want to work with both behavioral faculty and model-building faculty. It's a glue that supports the further development of MS&E as a distinctive Stanford department.

What other areas did MS&E develop?

We also hired in finance, a historical strength of MS&E going back to the 1980s. We were one of the first engineering departments in the country to recognize that the key questions in quantitative finance were becoming more data-driven. The historical focus in other engineering schools was on computational finance, more in the spirit of computationally intensive models used to price options and other instruments. But we decided to go with hiring faculty with deep understanding of financial data and the econometric tools needed to analyze big data sets. Other departments have followed Stanford's lead in moving their research activities in that direction.

The trend more recently is to use research in data science, machine learning, and artificial intelligence to generate insights into financial data sets to help investors make better decisions. Some of these insights have regulatory implications, too. The environment for research in data-informed decision making continues to grow across the entire management science discipline, especially within algorithmic decision making.

Did the MS&E curriculum change under your leadership?

At the doctoral level, we created a new seminar course that changed the PhD culture. Today, all PhD students take a common course in their first quarter introducing them to research across the entire department. Previously, a PhD student might never even meet other students outside their disciplinary area. This class gives opportunities for PhD students to meet, create a social environment, and get exposed to research questions and methodologies across the entire department.

It creates a common culture and helps their research because they learn what resources are in the department. If you need expertise in Area X, there might be a faculty member down the hall. That might lead to new types of interaction that are good for the department. The course has had some knock-on effect of increasing integration within the faculty, maybe because PhD students work so closely with faculty.

Why are students, faculty, and industry so interested in MS&E?

MS&E has a culture in which people have the freedom to pursue an enormous array of research questions. We have people working on algorithms that can be used by technology companies. We have people working on healthcare policy questions, healthcare operations, supply chain problems, government policy, questions related to defense policy, education choice questions, technology strategy, entrepreneurship, and work design issues.

Interest in what MS&E does has gotten stronger over the years. It's reflected particularly at the master's level by selectivity, with the proportion of applicants we admit falling. It shows how many students find MS&E exciting and want to be part of it. And we've had faculty searches in which the number of applications has been more than 500.

What we do is heavily appreciated. Our faculty has deep connections in the technology sector to industry researchers. The students we graduate are very attractive to these companies. With companies, just as with universities, who they hire tells you a lot about what they value. That's a strong vote from Silicon Valley and the technology sector.

What surprised you about leading MS&E?

I was happily surprised by the level of goodwill across the department to create the best possible environment for teaching and research, regardless of an individual's own interests and preferences. MS&E had 30 faculty and 100 PhD students, 200 master's students, and 70 or 80 undergraduates. And with a staff of 15 to 20, it’s a big operation. You can imagine lots of centrifugal forces that make an entity that big difficult to lead.

It really was the case, and is to this day, that faculty come together because they realize it's in the interest of everybody to have the best possible department. People may have differing ideas on how to get there, but the fact is that everybody sees a strong department as being most important, more important than any personal interest.

That doesn't mean we don't talk about controversial and difficult things, but making sure that while we can disagree, we recognize that everyone brings a common value system of wanting to do what's best for the department.

What advice do you have for other leaders in academia?

You are there to serve your fellow faculty and students. Try to identify healthy ways to move forward. This type of approach requires a lot of listening and understanding that differences of opinion are honest and are being made with best of intentions. Synthesize these things into a cohesive vision for the future. Understand you're just one person in this environment.

What makes you optimistic about MS&E?

This is a very exciting department with great faculty and students and programs. We've hired faculty members who are doing a wonderful job of positioning MS&E for the future. I'm excited to see where they will take us. This department has enormous opportunities and I'm excited about what it will do and what it will contribute to the world.