I’m a PhD student in Public Policy and Machine Learning at Carnegie Mellon University. I’m motivated by problems that affect cities, specifically crime, transportation, and education policy. My research focuses on how machine learning can improve policy-relevant research on these types of problems by unlocking information captured in unstructured data (e.g., text, video, mobility data). The goal is to do better social science, so we can make cities safer and more equitable.

For more info, see my CV. You can also reach me at lcrowl [at] cmu.edu.

Research Interests

Policy

  • Crime
  • Transportation
  • Education

Machine Learning

  • Model-based hypothesis generation
  • Interpretability
  • Causal inference
  • Natural language processing

Bio

Chicago

Minnesota
→ Bachelors in math/statistics at Carleton College

New York
→ New York Yankees
→ UChicago Crime Lab New York and Center for Applied AI

Pittsburgh
→ Ph.D. in machine learning and public policy at Carnegie Mellon

Many of my interests (and much of my personality) stem from growing up in Chicago, a place I am annoyingly in love with. I think Chicago’s combo of beautiful neighborhoods, incredible food & culture, rich history, and wonderful people, highlights why living in cities is so rewarding. It also struggles with problems that impact many U.S. cities, including gun violence, car dependence, housing affordability, and access to quality public education. As is so often true, both the benefits and harms are unequally distributed. My experiences growing up in Hyde Park, a neighborhood where this contrast is particularly stark, shaped my interest in how cities work and desire to tackle the problems they face.

For undergrad, I left to study math & statistics at Carleton College in Minnesota. While there, I tried to balance out my math workload with classes that people find more interesting to talk about at dinner. Some highlights include Film Noir, Philosophy of Sex, and Skepticism, God, & Ethical Dilemmas. I also did lots of sports things, including playing varsity baseball, hosting a 3 AM sports-talk radio show, and serving as the color commentator for Knights basketball games.

After college, I moved to New York to work for the Yankees as a quantitative associate in baseball operations. I got to watch a lot of games and learned how to think about sports from very smart people, but the real benefit of the job was that it encouraged Yankee fans to open up to me about all their grievances and conspiracy theories.

After my brief foray into sports analytics, I returned to my interest in cities, working as a data scientist for the University of Chicago’s Crime Lab New York and Center for Applied AI. During that time, I worked primarily on projects for Jens Ludwig and Sendhil Mullainathan, mostly using machine learning to tackle policy problems and understand human decision-making. I really can’t say enough good things about them as people and researchers. Most of my good ideas still come from asking myself what they would do.

I am now a third-year PhD student in the joint machine learning and public policy program at Carnegie Mellon in Pittsburgh. With plenty of help from my advisors, Dan Nagin, Eli Ben-Michael, and Rayid Ghani, I’m currently working on developing ML methods to help social scientists. If you have any interest in that, please reach out.

Way more than you needed, but thanks for reading!