In the fall semester of 2022, Cornell Engineering welcomed eleven new faculty members. While their fields of study run the gamut from gauging financial risk to understanding and controlling quantum... Read more about Cornell Engineering welcomed eleven new faculty in the fall 2022 semester
Ian Lundberg: Applying computational methods to the study of economic inequality
- New Faculty Year: 2022
Assistant Professor, Information Science
Academic focus: I develop and apply computational methods to study economic inequality. As a sociologist by training, I love collaborating not only with other social scientists but also with computer scientists and data scientists. The best research ideas often come from teams with diverse backgrounds, which is why I am so excited to be part of Cornell Information Science.
Research summary: Computational power has been multiplying for decades, and new machine learning tools might change the way we do science. Yet algorithms alone rarely produce new findings---human researchers play an essential role as well. My research aims to help social scientists ask questions about economic inequality in new ways, using precise language that can point toward algorithmic tools for estimation. My research also aims to help those who develop algorithms to translate those algorithms back to well-defined research questions, thus motivating the quantity an algorithm produces. I often find that effective use of computational tools comes from clarity about the research question, which often requires the language of causal inference. Substantively, my research explores factors that shape socioeconomic opportunities over lifetimes and across generations.
What inspired you to pursue a career in this field? During college, I came to realize how remarkably unequal economic outcomes in America are. I worked with a terrific undergraduate adviser (Alexandra Killewald, Harvard) who brought me into the research process and showed me how my love of numbers could be directed toward population-based summaries of inequality. She instilled in me a commitment to getting the answer right by using flexible statistical models. During graduate school, I began to understand how that commitment could be taken further by adopting machine learning methods for estimation. My PhD adviser (Brandon Stewart, Princeton) encouraged me as my reading branched out to include not only sociology and economics but also statistics and computer science. Together, we came to understand methodology in a new way. I am excited to pursue this career because there are many more open problems and much more to understand.
What are you most looking forward to as a Cornell Engineering faculty member? I am most looking forward to research collaborations with students and colleagues. I have learned the most and arrived at the best ideas by discussing problems with others. Cornell is full of people with innovative ideas and diverse perspectives. I'm looking forward to meeting those people---perhaps even you who are reading this article!
What do you like to do when you’re not working? I love hiking, running, and anything outdoors. This winter I hope to become better at cross country skiing!