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Apply for 2023 Summer Lab!

The Data Science Institute Summer Lab program is an immersive 10-week paid summer research program at the University of Chicago. In the program, high school and undergraduate students are paired with a data science mentor in various domains, including: computer science, data science, social science, climate and energy policy, public policy, materials science, and biomedical research. Through this pairing the research assistant will engage with and hone their skills in research methodologies, practices, and teamwork. Eligible high school and undergraduate students, are paired with projects in the Social Impact Track (more details below). We encourage participation from a broad range of students, and require no prior research experience to apply.

DSI Summer Lab Team

Kyle Chard is a Research Assistant Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

Maria V. Fernandez is Research Program Manager for the Data Science Institute. Before joining the DSI, Maria was a Project Manager and Metadata Specialist at the UChicago Library. She earned a Masters of Science in Information Studies and a Masters of Arts in Latin American Studies from the University of Texas at Austin, and she has a long history working in higher education on data and project management in a variety of fields and contexts. Within data science, Maria is interested in the application of the CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) Principles for Indigenous data governance in open data, data sharing, and machine learning.