Alex Kale is an Assistant Professor in Computer Science and the Data Science Institute at the University of Chicago. Previously, he earned his PhD at the University of Washington where he worked with Jessica Hullman. Alex leads the Data Cognition Lab, focused on creating data visualization and analysis software that explicitly represents the user’s cognitive processes.

Alex creates and evaluates tools for helping people think with data, specializing in data visualization and reasoning with uncertainty. He publishes in top human-computer interaction and data visualization venues such as ACM CHI and IEEE VIS, where his work has won Best Paper and Honorable Mention Awards. Alex’s work addresses gaps in dominant theories and models of what makes visualization effective for inferences and decision making.

Mina Lee is an incoming Assistant Professor of Computer Science and Data Science for the Summer of 2024.

Her research goal is to design and evaluate language models to enhance our productivity and creativity and understand how these models change the way we write. She has built various writing assistants, including an autocomplete system, a contextual thesaurus system, and a creative story-writing system. In addition, she has developed a new framework to evaluate language models based on their ability to interact with humans and augment human capabilities. She was named one of MIT Technology Review’s Korean Innovators under 35 in 2022, and her work has been published in top-tier venues in natural language processing (e.g., ACL and NAACL), machine learning (e.g., NeurIPS), and human-computer interaction (e.g., CHI). Her recent work on human-AI collaborative writing received an Honorable Mention Award at CHI 2022 and was featured in various media outlets, including The Economist. Mina received her PhD from Stanford University in 2023.

Bo is an Associate Professor in the Computer Science Department and Data Science Institute at UChicago.

Bo’s research addresses trustworthy machine learning from both theoretical and practical aspects and aims to enable reliable machine learning algorithms and systems in the real world, such as safe autonomous vehicles and federated (distributed) learning. She focuses on three interconnected aspects: robustness, privacy, generalization, and their underlying connections.

Bo received her Ph.D. in Computer Science from Vanderbilt University in 2016. She was a Postdoctoral Researcher at UC Berkeley 2017-2018 (working with Prof. Dawn Song) and joined the faculty at UIUC in 2018.

She been recognized by a long list of notable awards and fellowships for young faculty. She is a Sloan Fellow, MIT Technology Review TR-35 innovator, and recipient of the IJCAI Computers and Thought Award, NSF CAREER, Intel Rising Star Faculty award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from Amazon/Facebook/Google, and best paper awards at multiple top machine learning and security conferences. Her research has been featured by major publications and media outlets such as Nature, Wired, New York Times, Fortune, and is on display at the Science Museum in London.

Chenhao Tan is an assistant professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

Website

Blase Ur is an assistant professor of computer science at the University of Chicago. He founded the UChicago SUPERgroup, an interdisciplinary research collective whose research spans computer security, privacy, ethical AI, and human-computer interaction (HCI). The SUPERgroup is especially interested in developing data-driven methods and tools that support users’ security and privacy decisions, as well as their interactions with complex computer systems. The group’s work has been supported by nine National Science Foundation grants, as well as grants from Mozilla Research, Meta, Google, and the Data Transparency Lab.

Blase (whose name is pronounced “blaze”; he/him pronouns) has received the Quantrell Award for Undergraduate Teaching (2021), NSF CAREER Award (2021), SIGCHI Outstanding Dissertation Award (2018), and a Fulbright Scholarship (2010). His work has received four best/distinguished paper awards, as well as five honorable mentions for best paper. He earned a PhD in 2016 from Carnegie Mellon University’s Societal Computing program, where he was advised by Lorrie Cranor, and an AB in computer science from Harvard University in 2007. He is also a musician, photographer, (theatre) designer, and avid bicyclist.

Molly Offer-Westort works on quantitative methodology for social science research, with a focus on causal inference and experimental design. Offer-Westort’s PhD is joint in Political Science and Statistics & Data Science, conferred by Yale University in 2019; Offer-Westort also holds a Masters in Statistics, also from Yale, and a Masters in Public Affairs, from the Princeton School of Public and International Affairs.

At Yale, Offer-Westort worked in collaborative research labs with Peter Aronow, Gregory Huber, and Forrest Crawford. In the summer of 2018, she interned with Facebook’s Core Data Science team.

Haifeng Xu is an assistant professor in the Department of Computer Science and the Data Science Institute at UChicago. He directs the Strategic IntelliGence for Machine Agents (SIGMA) research lab which focuses on designing algorithms/systems that can effectively elicit, process and exploit information, particularly in strategic environments. Haifeng has published more than 55 publications at leading venues on computational economics, machine learning and theoretical computer science, such as EC, ICML, NeurIPS, STOC and SODA. His research has been recognized by multiple awards, including the Google Faculty Research Award, ACM SIGecom Dissertation Award (honorable mention), IFAAMAS Victor Lesser Distinguished Dissertation Award (runner-up), Google PhD fellowship, and multiple best paper awards.

The following research themes are the recent focus of our research lab. Please refer to our lab’s website for more details.

• The economics of data/information, including selling, acquiring, and exploiting information
• Machine learning in multi-agent setups under information asymmetry, incentive conflicts, and deception
• Resource allocation in adversarial domains, with applications to security and privacy protection