Interview with Dr. Maggie Makar

by Jung Min Lee (PhD student, Michigan AI)

Dr. Maggie Makar is the Computer Science department’s first Presidential Postdoctoral Fellow/assistant professor. Prior to joining the department in 2021, she was a PhD student at CSAIL MIT, advised by Prof. John Guttag. Dr. Makar’s research interests focus on the intersection of machine learning and causal inference, and how these tools can be applied in healthcare settings. 

The content of this interview has been edited for coherence and readability. 

JM: Maggie, it’s great to see you again. Can you briefly introduce yourself for our readers?

Maggie: Sure, I’m a Presidential Postdoctoral Fellow and a non-tenure track assistant professor here at CSE. Before coming to Michigan, I did my PhD at MIT in CSAIL where I was advised by Prof. John Guttag. Before MIT, I spent a little over a year at Brigham and Women’s hospital studying questions such as the end of life care with Ziad Obermeyer, and before that I was an undergraduate in Math and Economics at the Univ. of Massachusetts Amherst. And before that, I was in Egypt where I was born and raised, and while some people in Egypt will now tell me that my Arabic has deteriorated, I still hold that I am profoundly Egyptian at my core. 

JM: I’m curious about the Presidential Postdoctoral Fellow Program (PPFP), especially since you are the first PPFP fellow in the CSE department. Could you explain a bit more about what that looks like and why you chose to pursue that path? 

Maggie: One of the reasons I ultimately chose to come to Michigan is because of this unique position. As a postdoctoral fellow, I’m somewhere between a postdoc and a full assistant professor. I don’t have any teaching requirements right now, so I can focus my time on research. The position is designed to put you on a fast track to begin your faculty career. Once this appointment is over, I get evaluated on whether or not I will be joining as a tenure track assistant professor. 

I think this position is very helpful because it gives me a bit of space to develop a research agenda, and to think deeply about the type of mentor I want to be and the kind of collaborations I want to build. I like to think of it as a ramp that’s designed to help you start flying. 

JM: Do you plan on teaching once you become a full faculty member? 

Maggie: Absolutely, and that’s something I’m excited about as well. Teaching is a great way to really learn about a topic. There were many times during my PhD when my advisor would ask me “Why is X true? Explain it to me.” And if I couldn’t explain it to him, that told me that I didn’t fully understand the subject. So teaching can really help you understand something better. I also sense some interest from different people in the department for a causal inference course, which is exciting. 

JM: That sounds like a great opportunity. In terms of research, what topics have you been focusing on? 

Maggie: Broadly, the thread of research that I’ve been most excited about in recent years is how we can use ideas from causality to build better machine learning models, and how in turn we can use ideas from statistics and machine learning to make causal inference more efficient. I typically use these tools in the context of healthcare by applying them to specific problems such as infectious diseases. 

I’m also starting to get into causal discovery. It’s a specific type of causal inference where you are not just asking whether or not intervention A has a causal effect on outcome B, but whether you can reconstruct the underlying causal mechanisms based on some observed data. 

Another idea I’m interested in is using causality in machine learning to create more generalizable and robust models. I think it’s important because datasets, especially in healthcare, can reflect systemic biases that are correlations and not actual medical information. If we don’t take into account the causal knowledge of medicine, we might end up with machine learning models that encode spurious correlations or perpetuate biases that have no rooting in medical soundness. 

JM: Do you have any advice for graduate students who are trying to decide between academia and industry careers? What were the factors that made you choose a career path in academia? 

Maggie: That’s a great question. I had offers from both industry and academia so I had to make this choice as well. I think computer science is unique in that you can do great things in either career direction. What ended up tipping the scales for me were two things. First, academia is an exciting career because students tend to bring a fresh perspective that might be different from how I have been thinking about a specific problem. So I like the fact that in academia, there will always be a fresh perspective and that just leads to more exciting research. 

The other reason is that I’m interested in both applications of AI to healthcare and regulation of machine learning algorithms, such as defining what it means to audit them and check if they conform to causal mechanisms. And these are both areas that are harder to tackle in industry. With healthcare, there’s understandably a lot of red tape around medical data that makes it difficult for companies to work with them. There are also some red tape and restrictions around topics such as regulation that might conflict with the companies’ interests, so there is some limitation to academic freedom there. 

That being said, I am still a consulting researcher at Microsoft, so I also enjoy doing industry type research on this side, along with my academic research and my ability to collaborate with students. 

JM: What are some things graduate students can do to prepare for a career after grad school? Were there any specific steps you had to take to prepare for an academic position?

Maggie: Honestly, I believe that what you need to do in order to land a job that you like is roughly very similar independently to whether you want to go into industry or academia. At the end of the day, what companies like Microsoft and Google are looking for is a good researcher. My advisor used to say, “Don’t over optimize. Do what you like doing, and if it gets you where you want to go, then it’s a double win. If it doesn’t go where you want to go, you still have the advantage of having done something you enjoy and having created a dent in an area you wanted to create a dent in.”  

So my advice is to not do things just because it might look good on your resume, or to make the type of calculations like “that looks good if I want to do X and not if I want to do Y”. The reality is that it’s impossible to guess to that level of precision on what you need to do in order to land a very specific position. What you can do is to focus on what you think is worth answering and do a really good job at it. If that gets you the job that you’ve always dreamt of, that’s great. And even if it doesn’t, you still would have done something that’s meaningful to you. 

JM: That’s great advice! I think that way of thinking can also take away some of the pressure students find themselves facing when they become a bit too goal-oriented. It’s also a great segue into our next question. Are there any general advice you have for graduate students? 

Maggie: Yes, I have one piece of advice that I tell everybody. I think one of the things that makes the difference between a successful PhD and an unenjoyable PhD is how you approach failure. I’m definitely guilty of this too –  there are times when I’ve gone into the lab and became anxious or upset that my experiment didn’t give me the result I wanted. The reason this isn’t a helpful attitude is because often, really good papers come out from these failures. If you instead approach this as “That’s really interesting! Let me see why that happened.” you may realize there is an opportunity for a new approach or a new solution. In my experience, good papers are born out of failed experiments most of the time. Instead of just meeting this failure with disappointment and frustration, it is more helpful to let failures spark your curiosity. 

JM: What’s your goal for the next 5 to 10 years, and where do you see yourself?

Maggie: By 5 years, I hope to have built collaborations with students and have them working on good research projects that they are excited about. My hope is that these projects will also have a technical impact and an impact on society, whether it’s through healthcare or positive ethical feedback in AI. 

I hope to be recognized in the causal inference field as someone who’s done good work, and work that moves the field in meaningful ways. And I would hope to be recognized as a good citizen in the department, one who can be trusted by the community in a meaningful way and a person that people feel comfortable reaching out to.

In 10 years – this has been my weird dream – is to be the expert witness in some kind of congressional committee for algorithmic regulation in AI. It’s a fun daydream I’ve had for some time. I’d also like to have something in the hospital system that we’ve implemented, whether it’s an alert system or a diagnostic tool, that can improve hospital operations and patient outcomes. 

More details about Dr. Maggie Makar’s work:


About the author:

Jung Min Lee is currently a PhD student affiliated with the Michigan AI Lab – CSE, EECS Univ. of Michigan.