2023 PhD graduate Evi Micha
Supervisor: Nisarg Shah
Tell us about your graduate research.
During my PhD, I worked on topics related to computational social choice and algorithmic fairness. The field of computational social choice lies at the intersection of Economics and Computer Science and studies algorithms for aggregating individual preferences into collective decisions. By leveraging concepts from social choice theory, computational social choice seeks to understand how to aggregate individual preferences and opinions, addressing challenges in areas such as voting systems, resource allocation, and group decision-making. My recent work has also focused on the enhancement of fairness in AI systems using ideas that derive from Economics. Both algorithmic fairness and computational social choice contribute to the broader goal of creating ethical and effective systems in the ever-expanding landscape of technology and society.
What got you interested in this particular area of computer science?
Since I was an undergrad, I got excited about the field of computational social choice and the design of algorithms that aggregate people’s preferences. The main reason that I got particularly interested in this field of computer science is its impact that it can have on various real life applications.
Big picture — How do you see your research improving the life of the average person?
My research focuses on exploring ways to make fair decisions in various scenarios, including recommendation systems, the allocation of scarce resources, and the design of peer-review procedures. In a recent paper, we examined how to efficiently allocate the scarce resource of pooled testing for COVID-19 during a lockdown, aiming to ensure a safe return to in-person activities. We demonstrated that simple solutions, which are logistically easy to implement in practice, suffice. Additionally, we designed an algorithm that identifies an almost optimal allocation. Our algorithm was tested in a research institute in Mexico.
Tell us about an experience from your PhD program that stands out to you.
During my PhD program, I was fortunate to be advised by Professor Nisarg Shah, who is an amazing researcher and a very supportive mentor. One of the most important lessons I learned from him is to be passionate about my research. I was a graduate fellow at the Vector Institute and at the Schwartz Reisman Institute. Through both fellowships, I had the opportunity to interact with people from various backgrounds and discuss interdisciplinary research questions, including fairness and ethics in AI. The University of Toronto truly promotes interdisciplinary research, and I feel fortunate to have been part of this unique culture.
What’s one piece of advice you would share with incoming computer science graduate students?
A piece of advice that I would give to incoming computer science graduate students is to not give up when things get hard and to take good breaks. I was always able to solve most of my research problems when I revisited them with fresh eyes.
What’s next for you?
Currently, I am a postdoctoral fellow at Harvard University. In August 2024, I will begin my role as an Assistant Professor in the Department of Computer Science at the University of Southern California.
Building upon the knowledge acquired during my PhD, I continue to explore algorithmic fairness in AI systems. My particular interest lies in investigating popular fairness notions from computational social choice literature that can effectively apply to diverse domains such as machine learning. In an era where algorithms significantly impact our lives, I believe it is more crucial than ever to design algorithms that are fair, utilize limited resources efficiently, and contribute to social good. This is the dedication of my research.
This Q&A has been edited for clarity and length.