Preceptors in Data Science

Jonier is a preceptor at the Data Science Institute of UChicago, working jointly with DSI and the City Colleges of Chicago. Jonier obtained his Bachelor’s in Physics from the Federal University of Rio Grande do Sul and his PhD in Mathematics from Northeastern University. Like his academic background, Jonier’s teaching practices are informed by interdisciplinarity and oriented towards problem-solving. Prior to joining the DSI, Jonier worked at Harvard University as a preceptor in the Department of Mathematics, where he helped implement active learning practices in the classroom for introductory math courses. In his spare time, Jonier likes to dabble in the visual arts and singing.

Jennifer Blanc is a Data Science Preceptor at the University of Chicago Data Science Institute and the City Colleges of Chicago. She earned her PhD in Human Genetics from the University of Chicago in 2024. As a statistical and population geneticist, her research focuses on developing novel approaches that leverage genetic data to better understand complex trait variation across diverse human populations.

As an educator, Jennifer is passionate about data science education and outreach. She enjoys teaching coding and statistical analysis using real-world examples that empower students to see the power of data science in their own lives.

Outside of work, Jennifer enjoys playing volleyball and spending time with her adorable tuxedo cat, Rosie (named after Rosalind Franklin, whose work was central to discovering the structure of DNA!).

Emily Bolger is a Data Science Preceptor at the University of Chicago Data Science Institute and the City Colleges of Chicago. She earned her PhD in Computational Mathematics, Science and Engineering (CMSE) at Michigan State University (MSU), with a focus in Computing Education. Her research interests include using Natural Language Processing techniques to identify trends in education literature. She received a Certificate in College Teaching from MSU and has worked closely with colleagues to develop curricula for introductory courses on data ethics and algorithmic bias. She believes everyone can find a place in STEM and thus, focuses her efforts in making science more accessible to students and to the general public. In her free time, she enjoys reading, crocheting, and dancing.

John Hood is a SkAI Data Science Preceptor, where he will be an instructor at both the University of Chicago and the City Colleges of Chicago. In addition, he will continue research at the NSF-Simons SKAI Institute for Astronomy and AI. He earned his PhD in Astrophysics from Vanderbilt University, where he focused on multi-wavelength observation of active galactic nuclei (AGN) and experimental cosmology. He is currently Co-PI of a NASA-RIA grant upgrading the equipment of a campus observatory (mount, camera, filter wheel, dome) to make it research-capable for undergraduate students and to carry out multiple projects including AGN and near earth object (NEO) monitoring.

He believes that one of the best parts about being a scientist is having the ability to do community outreach and in person science communication. Between volunteering for astronomy conversations at Adler Planetarium to visiting schools to talk with students, he loves any opportunity he gets to talk to people about exciting topics in astronomy and data science! In his free time he enjoys many hobbies from playing video games to collecting cards/comics and building gunpla.

Lindsay House is a SkAI Data Science Preceptor, where she will be an instructor at both the University of Chicago and the City Colleges of Chicago. In addition, she will continue research at the NSF-Simons SKAI Institute for Astronomy and AI. She earned her PhD in Astrophysics from the University of Texas at Austin, where she focused on dark energy cosmology. She is the project lead for the NASA Zooniverse project Dark Energy Explorers, combining participatory science with machine learning to educate the public while researching some of the universe’s mysteries.

Continuing to weave education into her research, she was an NSF intern at Adler Planetarium and Zooniverse, where she focused on science communication. She has worked at many other informal education institutions, including the Smithsonian National Air and Space Museum, Fiske Planetarium, and Morehead Planetarium. Outside of work, Lindsay is always trying a new hobby, whether it’s working out or crafting. You name it, she has probably tried it!

Bailey Howell is a Data Science Preceptor at the University of Chicago Data Science Institute and the City Colleges of Chicago. She received her Ph.D. in Biological Sciences from Virginia Tech. Her research broadly focuses on applying data science methods to study species’ ecology and evolution. Her dissertation work involved integrating information from diverse data sources, including images, geospatial data, and publicly available trait databases, into predictive modeling frameworks to study invasive species. At Virginia Tech, she was heavily involved in mentorship and outreach. She is committed to continuing this dedication to teaching, mentorship, and outreach at UChicago DSI and City Colleges, empowering all students to realize their full potential.

Kriti is a preceptor at the Data Science Institute (DSI) of the University of Chicago. She is working jointly with DSI and the City Colleges of Chicago. Prior to joining DSI, Kriti obtained her PhD in Mathematics from The Ohio State University (OSU) and Masters in Mathematics from Indian Institute of Science (IISc). She is passionate about teaching and has taught a diverse range of Mathematics courses at both OSU and IISc as a teaching assistant. She was honored with the highest teaching award at OSU, the Graduate Associate Teaching Award in 2023 and the Phil Hueneke distinguishing graduate associate teaching award in 2022. She is excited to continue to grow as an educator in her role as a preceptor and contribute to data science education. In her spare time, she enjoys running, badminton and is always open to chat!

Kelly Smalenberger is a Preceptor of Data Science at the Data Science Institute. Her focus is on data science education and creating effective learning spaces to meet a wide variety of student needs at the University of Chicago and its partnering institutions. Kelly received a Ph.D. in Applied Mathematics from the University of North Carolina at Charlotte and a certificate in Data Science and Machine Learning from MIT’s Schwarzman College of Computing. Kelly enjoys spending time with her young family, being outdoors, and running.