My name is Tomo Lazovich. I am an Assistant Professor of the Practice in AI Governance and Policy at the Data Science Institute. I teach courses in the undergraduate computer science department and the Online Data Science Master’s Program.

What brought you to Brown DSI?

I have sort of a winding path to where I am now. My undergraduate and graduate training was in physics, but I always had an interest in both physics and computer science. When I went to graduate school, I chose to focus on particle physics, a field that is very computationally intensive. 

I worked on an experiment at the Large Hadron Collider, where we dealt with extremely large datasets. That experience is really where I first developed my skills in data science and machine learning, applying them to scientific analysis of experimental data. 

After finishing my PhD, I decided to take those skills into industry. As a Professor of the Practice, my path is a bit different from the traditional academic route. I spent several years working in machine learning in industry, and over time, I transitioned into what is now called responsible AI or AI governance. That progression ultimately led me to where I am today. 

What does your research focus on now? 

My research focuses on the societal impacts of algorithmic decision-making in the real world. In particular, I think about how people who are harmed by these systems can find remedies, whether technical or legal. 

A big part of my work is connecting the technical side, how these systems are built and how harms might be prevented, with the legal and policy side, which focuses on how governments can regulate the ways technology companies develop and deploy them. 

I am currently working on a project that I am very excited about with a master’s student, Harry Yang, and hopefully some undergraduates this summer through the UTRA program. We are studying how algorithmic systems are used in public benefits programs across the United States, including programs like SNAP, Medicaid, and unemployment insurance, which serve many low-income communities. 

These programs increasingly rely on automated systems to determine eligibility or detect fraud, but there is very little documentation around which states are using these tools and how they are being applied. We are launching a large public records campaign to gather information such as contracts and documentation on these systems. 

The goal is to build a comprehensive database that helps make these systems more visible and understandable, ultimately giving people better insight into how decisions are being made about their access to benefits. 

You’ve just started a research lab at DSI. What will your lab focus on? 

It is called the PANDORA Lab, which stands for Policy Advocacy and Novel Designs for the Oversight and Regulation of Algorithms. It is a bit of a mouthful, but it captures what we are trying to do. 

The first project of the PANDORA Lab is the public records campaign on benefits programs that I mentioned earlier. Right now, it is still a small team, just me and a few students, but that project is where we are hitting the ground running. Currently, I am working with master’s student, Harry Yang, and I am reviewing applications for the UTRA program. I expect to bring on four students for the summer, and I have received close to forty applications. It has been great to see so much interest. Students have been reaching out during office hours to talk about the lab, and there are a lot of strong applications. 

It is exciting to think about the range of projects we can tackle and the energy the students bring. I am really looking forward to seeing how the team comes together over the summer.

You’ve also been very involved with the Center for Technological Responsibility, Reimagination, and Redesign (CNTR); what have you been working on with CNTR?

This is actually a great time to talk about that because we just launched a new project called the AI Legislation Lab. I serve as the Policy Director for the initiative, which focuses on analyzing AI-related legislation across the United States, primarily at the state level, along with some federal activity. 

The project involves a cohort of undergraduate and graduate students who review proposed bills using a structured questionnaire that we developed. Each student is assigned legislation from different states and works through the framework to analyze the content of each bill. 

That information is then compiled into a public-facing library, where users can look up specific pieces of legislation and see a clear, structured summary. For example, you can find a bill introduced in a state legislature and quickly understand what areas it covers and what kinds of impacts it may have. 

The goal is to make sense of a rapidly evolving landscape. AI-related laws are emerging across the country, often addressing very different issues depending on the state. This project helps bring clarity and accessibility to that complexity, and I am really excited about where it is going. 

In addition to your research, you’re developing multiple courses for DSI’s undergraduate and Master’s programs; how has that experience been?

For DSI, I am teaching a course that is cross-listed with the computer science department, CSCI 1491, Fairness and Automated Decision Making. It has been especially meaningful to work with students and help them understand the real-world implications of the systems they are building. In computer science, many courses focus primarily on math or algorithms. There are fewer opportunities to step back and consider the broader impact of these technologies. It has been very rewarding to hear students’ questions and to see things click as they begin to realize that their work goes beyond code running on a computer. These systems have real consequences for people. Being part of that learning process has been incredibly fulfilling, and I have been grateful for the opportunity to teach this course. 

For the Online Data Science Master’s program, I am teaching DSI 2030, the Applied Learning Experience. It is the capstone project class that students take in their final semester, where they put together a substantial project synthesizing what they have learned across their courses. I am excited to bring my industry experience into the class and structure it so that students gain a sense of how a real-world socio-technical project works. The course is still in development, but the goal is to make it open-ended and relevant to students’ career goals. 

The philosophy behind the course is that students should produce work that is genuinely useful for their portfolio and career aspirations. The master’s program is mostly made up of working professionals looking to pivot or advance their careers. I don’t want the project to be just an assignment; they should create something meaningful that they can showcase to potential employers. The course is less about introducing new technical content and more about guiding students through the process of thinking through a project at the intersection of society, data, and policy. I am drawing on my industry experience to help students navigate that space, and I am designing it to be flexible enough for students with both technical and policy or product backgrounds. Right now, I am about a quarter of the way through development, and that philosophy is guiding everything I build.  

You are also finishing your law degree right now; how has your study of law made an impact on your research and teaching at DSI?

I am especially excited about finishing my law degree. As part of my effort to bridge the technical and legal sides of this work, I have been attending law school at night while working full-time. I will be completing the program this April, after four years, which feels like a major milestone. I am very much looking forward to finishing and bringing that training into my research and teaching. 

I’ll be teaching a new course in the fall that I’ve proposed: DATA 1250: Artificial Intelligence Law and Policy.  It will explore how courts, regulators, and policymakers are responding to the complex questions posed by artificial intelligence and machine learning technology. It will cover several areas of law and policy, including liability theories for individual harms, privacy, discrimination, copyright, and new regulations emerging both in the U.S. and abroad. I’m hoping this will be of interest to pre-law students or other students with a policy background, as well as those from more technical backgrounds who want to learn about law and policy. It will emulate courses and discussions that students might encounter in law school classes themselves.

What excites you about being part of the Data Science Institute?

What excites me most about the DSI is how it brings together people from so many different disciplines. It is a unique space where you can see the wide range of ways data is being used across the university. 

You might attend a talk from someone in anthropology or see projects focused on reconstructing ancient documents, while at the same time, researchers are working on cutting-edge questions in public health and healthcare. It is rare to have all of that in one place. 

That kind of environment makes it so much easier to share ideas and stay connected to what is happening across Brown more broadly. It creates opportunities for collaboration that would be much harder to find otherwise. It is an exciting community to be part of. 

What do you do for fun outside of work?

I am a big gaming enthusiast, both in video games and tabletop games. I especially enjoy board games and trading card games, so you can often find me at a local game shop playing Magic: The Gathering or something similar with friends. 

Right now, my favorite game is Star Wars Unlimited, a trading card game that I have been really enjoying. I am also a big Star Wars fan, so it is a perfect combination for me.