Spotlight on Dr. Caroline Beam (PhD IEOR ‘99)

Carrie Beam Head Shot

1. What inspired you to choose industrial engineering and operations research and attend UC Berkeley?

IEOR: I realized it was love at 9:02 on a grey February morning in New Jersey. It’s the kind of morning that makes most undergraduates sleep, and instead, I found myself bolt upright and wide awake in the front row of a classroom, learning that it is possible to set up and solve a transportation network problem as a linear program. I took it as a sign. (Full confession: I have slept through plenty of other classes, just not optimization ones.)

UC Berkeley: It was the early 90’s. I had taken a corporate consulting job straight out of undergrad, in which our purpose was to put computers in banks (new ATMs were all the rage, and many folks found it very exciting that money was, for the very first time, literally coming out of walls.) But I was really missing my linear programming and was getting increasingly desperate to be around really smart, super geeky people again. I had spent the previous winter in itchy wool suits in Minneapolis and the previous summer working in those same itchy wool suits in North Carolina. I didn’t want a BMW or a ski condo. I wanted that network formulation on the chalkboard. I had been to San Francisco as part of a Society of Women Engineers program a few years back, and developed a lifelong love of tie-dye. When Berkeley admitted me, it was a very simple decision to liberate the wool suits and surround myself with the brain trust.

2. Were there any memorable professors, classes, or experiences that significantly shaped your journey during your time at Berkeley?

Dorit Hochbaum and Shmuel Oren!

Professor Hochbaum invited me to be her teaching assistant for a Quantitative Methods class she was teaching to evening MBA students. The students were older than I was, very smooth and business savvy, quite demanding, and relative to engineers, quantitatively weak. For normal IEOR PhD students, used to helping engineering undergrads, this sort of “Wall Street” student base is a very intimidating situation, but I had no sense of danger as I relatively recently escaped from the ATM consulting job. I loved every minute of being her TA. I will always be grateful to her for introducing me to that type of teaching opportunity. Professor Oren gave me the thumbs up to study auctions on the Internet back in the late 1990’s, before we were really sure the Internet would be a thing. I had some data with respect to auction pricing and thought it would make a good dissertation, but it took Professor Oren’s thumbs up to give me the confidence to lean into that work.

3. What has your professional journey looked like since graduating? Can you share some highlights or milestones?

When I walked across the stage in 1999 to receive the PhD, I was the only one of my cohort who had a Stevie Wonder dissertation (“signed, sealed, and delivered”) and also the only one who was seeking neither a tenure-track nor an industry job.

So what did I do? I took a somewhat wide-ranging path – I’ve always split my professional attentions between university work and consulting. There was a period in which I needed considerable time for some family caregiving, and having the two different types of work gave me the flexibility I needed to make it all happen.

I taught in the MBA program at St. Mary’s College in Moraga for a while, and then I worked for many years remotely as a Teaching Associate Professor at the University of Arkansas, in their Master of Science in Operations Management program. In 2022 I came to work as a
Director in the Master of Science in Business Analytics program here in the San Francisco location of at UC Davis.

I have also always kept my consulting practice active. I had worked in management consulting for a few years before coming back for the PhD, and had continued to do some consulting while a grad student, so when I graduated of course it was the most obvious thing that I should yet again continue to do some consulting. It’s been over 25 years now and I am still doing consulting!

4. How did your education at Berkeley IEOR prepare you for your career?

At Berkeley, high performance was the norm. If I didn’t know a certain type of math, or if I couldn’t program in a language needed to do a certain type of analysis, or if I needed a dataset, it was expected that I would take the initiative and make it happen. That bias towards action, and being in an environment in which everybody else was also seriously committed to excellence, was extremely helpful.

I also learned persistence in the Ph.D. program. Not every research paper gets accepted for publication, not every lecture goes over well, and (especially in Berkeley) not every housing situation or parking ticket works out in the student’s favor. But persistence in the face of setbacks was a key skill.

Those two skills – proactive initiative and persistence – have served me very well careerwise. Since graduation, there have been many situations in which I didn’t have the data, skills, or connections I needed to do something the first time through, and my Berkeley days taught me how to get it done.

5. Looking back, what challenges did you face during your career, and how did you overcome them?

For many years, my biggest challenge was time: I had intensive family caregiving responsibilities. Like many others with family commitments, I had to become extremely efficient, which included doing my most important work every day before anybody else woke up, implementing technology such as automatically graded homeworks and Gradescope in my quantitative classes to reduce the time needed for a class, and investing in new skills. I learned Python in 2014, entirely before breakfast, in six very exciting weeks before I had to teach a class on the same.

Lately, my challenge has been moving from an individual contributor mindset to an organizational mindset. At Davis, my role includes sourcing capstone Practicum projects from industry, working to align course offerings with the demands of the workforce, helping students, and contributing to strategy. All of these tasks are team efforts with tremendous payoff if we can get them right. But it’s a “we,” not an “I.” The idea is to shift from doing it all myself to a mindset of helping the team get even bigger things done. I am a connector, a persuader, and a communicator. It’s so much harder than it looks on TV.

6. How has your perspective on the field of Industrial Engineering and Operations Research evolved over the years, and how do you see the discipline impacting industries in the future?

In IEOR, we were doing analytics before analytics was cool. Machine learning has strong components of optimization, and artificial intelligence is a lot easier to understand if one has good math skills.

Over the years, I would say the democratization of technology and information has made optimization more accessible. There was a time when only the leading edge companies could afford an optimized inventory schedule. Now, you can run that same inventory optimization code on the cloud from your phone in the back seat of a car on the way to a consulting presentation (assuming you don’t get too carsick.) It has reduced barriers to entry.

The INFORMS slogan is “Smarter Decisions for a Better World.” In the future, I see it being much easier, cheaper, and friendlier to get those smarter decisions, for everybody, in all industries – not just tech.

7. What advice would you give to current students or recent graduates entering your field?

Invest in a few softer skills: project management and business development/sales. Good project management skills, which include stakeholder management, work scheduling and planning, and critical path and backlog management, will make everything from a kitchen remodel to a master’s degree to tenure much less chaotic. You will simply get more done with less.

Business development skills, especially in industry, are extremely rare and they are exponentially valuable if you can also add and code.

If you did want to invest in one harder skill, I would say take the equivalent of the first four semesters of undergraduate computer science major programming classes. Even though ChatGPT will write code for you these days, knowing data structures and algorithms, and how to think like a computer, is very useful for an applied mathematician. It’s a skill investment that will save lots of time down the road.

8. Is there anything else you’d like to share with the Berkeley community?

Go Bears!