Interview with Professor Eric Mazumdar

Interviewer: Hannah X. Chen

Could you start by sharing a brief overview of your research and its applications?

I work at the intersection of machine learning and economics, and the motivation is really thinking about how we use machine learning algorithms in the real world, and what happens when we actually use them. In the real world, algorithms have to interact not only with people, but potentially also with other algorithms, and they have to deal with situations that we’ve historically seen machine learning algorithms struggle with. If you think back to applications of machine learning in the real world, there’s all these famous failures. There’s self-driving cars that crash because they can’t tell the sky from a truck, or that won’t cross an intersection because they can’t figure out the intent of other agents that might be present. There’s also all types of problems that happen in e-commerce markets, in social networks, and so on. And you can really think of these issues as arising because learning algorithms are coming into contact with people and other algorithms.

A big part of my research is just trying to understand what can go wrong when we use algorithms in environments containing real people who might have their own objectives. We’re also thinking about how we would design algorithms specifically for these environments so that they can have provable guarantees or at least economically meaningful measures of performance. My main application areas are in transport systems (ride-sharing platforms are a major example of this), e-commerce platforms, social networks, the delivery of healthcare — all of which have this kind of flavor of algorithms meeting people and interacting with other algorithms.

What do you see as the short-term or long-term directions of your work and the field as a whole? What is the end goal you would eventually like to reach?

The kind of pipe dream is this idea that we can develop algorithms that actually have a beneficial impact in society. And what I mean by that is having algorithms that don’t make things worse when they’re actually deployed in the environment — instead, they actively improve things. Maybe if it’s in transport systems, it improves congestion in cities. Maybe if it’s on e-commerce platforms, it lowers prices and makes the market more equitable. But doing this while keeping in mind issues of fairness and biases in data is really hard. But I think it’s a really important problem, because people aren’t going to stop using algorithms in the real world, and we really need to start figuring out how we do this properly, fairly, and ethically. That’s a big part of what my research is trying to do.

Is there anything you’re really excited about in your work right now, such as a current project or new idea?

Theoretically, we were able to show that one of the most important aspects when you’re using learning in the real world is how fast you’re learning. Often, we think that learning fast is always better, but when we started applying these types of ideas in games — where you’re learning against an opponent who’s also learning — we found it might not be better. And actually, the speed at which you learn changes the equilibria you might find, but different equilibria have different properties. Some might be more socially optimal, some might be better for individuals, and so on. So all of a sudden, we now have this way of achieving different solutions by changing how fast we learn.

Something I’m really excited about is that this is a phenomenon we’re seeing in real data from pricing on e-commerce platforms, for example. We see that people change how fast they update the prices relative to their competitors to get an advantage. Sometimes people update their prices very very slowly, and then they’re actually better off and are able to get more of the market. In other cases, they update much more quickly and can be better off as well. So there’s this interesting degree of freedom, which I don’t think has been explored much. I’m very interested in exploring the dynamics of learning algorithms in these game theoretic settings, which I think will be a really exciting area in the next couple of years. 

What are the biggest challenges that come with performing this research? What are the things you have to do to get your work moving forward?

A lot of my work is more on the theory side, mostly trying to develop algorithms that satisfy some theoretical guarantees and seeing that they work in practice. There’s two main challenges: on the data side, a lot of companies and entities that use algorithms in the real world don’t want to share their data because they don’t want to show that they’re doing something that isn’t working, or they’re scared of revealing some sort of inherent bias in their data. On the theoretical side, you have to bring in ideas from a lot of different fields to try and understand these problems. You need ideas from game theory and economics to deal with these issues of multiple agents all interacting, but then you need ideas from control and dynamical systems to think about the dynamics that are arising in these situations. And you need to bring in ideas from computer science to deal with the algorithmic parts. It’s really a research agenda that’s at this intersection of three different domains, so there are natural challenges regarding how they interact and whether you can make these things play nicely. It’s a really cross-disciplinary research agenda, and that comes with challenges, but also a lot of opportunities.

What has it been like working in both the CMS and economics departments at Caltech?

I only recently started, but it’s been a very satisfying experience because from what I’ve seen, economics and the computing sciences are very tightly integrated at Caltech. The economists are familiar with a lot of the tools and ideas from computer science and engineering, and the people in engineering also have some connections with economics, so it’s been really interesting to work at this interaction and play both sides. In my view, Caltech is extremely well positioned in this new research area that’s emerging at the intersection of machine learning and economics. We have a very strong mathematical microeconomics core, along with very strong experimental economists that understand decision making, whose ideas we can then embed into our algorithms. On the other hand, it has a very strong computer science and mathematical sciences department. So it’s been a really fun, unique opportunity, and part of that is only possible because of the small size of Caltech, which makes it easy to work between divisions and departments.

What drew you to this area to begin with? How did you get started in either machine learning or economics?

The way I got into computer science, machine learning, and economics is a bit weird. I basically started by getting my hands on data. We were looking at data about how people park their cars in Seattle, and there’s this idea that a large part of the congestion in Seattle is caused by people just looking for parking. So the question was whether we could come up with a model for this and see if this is true. Something very natural that emerged from this problem is to model parking as a game between people: those trying to drive on the city streets and those looking for parking spots. We started doing that, and that got me thinking a little bit more about how we can even use learning algorithms in game theoretic settings. From there, I kind of moved more and more towards machine learning and game theory, and then economics more broadly. So it was not an immediate process — I didn’t go into grad school knowing that this was what I wanted to do, but it was a very natural thing because this research area allowed me to draw on ideas from game theory, which I thought was fascinating, but also apply tools from machine learning, dynamical systems, and control, all in the context of interesting, real-world problems.

What were your mentors like as you were working in multiple fields? What has your experience been as a mentor to students of your own?

What may be surprising is that in my undergrad, I was actually doing computational biology. I had a set of really good mentors who allowed me to explore my interests in that field and help me discover that the thing that excited me most in computational biology was actually mathematical modeling and coming up with dynamic models of how, for example, drugs propagate through biological systems. When I got to Berkeley for grad school, I again had mentors who could really get me thinking about big open questions and the emerging themes and questions that were arising as machine learning and algorithms are deployed in the real world. So I’ve been extremely lucky throughout.

Here at Caltech, there’s two main parts of mentorship for me. The first is giving people the freedom to be creative and come up with their own ideas while giving them guidance through high-level research goals and things like this. The second thing is mental health. I’ve had a lot of close friends struggle with mental health in grad school and undergrad, so for me, a big part of mentorship is making sure the person feels very supported on the day-to-day and has something to fall back on. A lot of these environments, grad school and even undergrad at such an academically rigorous place, can be very high-pressure, so you need to have strong support networks for students.

What are some of your favorite parts about being a researcher and professor?

The best part of doing research is being able to talk to people and all of the random conversations you have in hallways or in front of white boards. It’s also just interacting with students and people with such different backgrounds and interests. That’s kind of the standard answer, but it’s really the most interesting part for me, especially at Caltech where you can meet so many people who do so many different things. You can go from a conversation about fluid mechanics to game theory and auctions in the same hour. It’s all very exciting.

Do you have any advice for students who want to get into research, especially within your field? 

As I mentioned, I was in bioengineering when I started undergrad, and I ended up in EECS (electrical engineering and computer science); now, I’ve moved towards economics. I think being multidisciplinary and having varied interests is a good thing. Interesting things happen at the intersection of fields, so don’t be scared of trying to make connections between fields. 

Getting involved at Caltech, I think, is really a question of persistence. If professors aren’t answering (and I’m definitely guilty of this, too), it’s not because they don’t want to — it’s just that they get too many emails. We want to work with students at Caltech since you’re all so good, so it’s very important to not be afraid to just send the emails and knock on doors. I was definitely worried when I sent my first few emails looking for positions, but in the end it’s just biting the bullet and doing it.

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