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Educational and economic opportunity, as well as health outcomes, depend on the availability of affordable, high-speed Internet access. The COVID-19 pandemic—and in particular society’s increasing reliance on reliable high-speed broadband Internet access during the crisis—has accelerated and magnified these existing disparities. As the essential tasks of living, such as learning, job seeking, and accessing health care, move online, Internet access is increasingly becoming an issue of educational equality and economic opportunity, one that disproportionately hurts low-income families and minorities.

The lack of precise broadband deployment data complicates this issue and attempts to remedy the “digital divide.” Current FCC broadband maps rely on census block data that overestimates the number of households with Internet access and lacks fine-grained detail. With a grant from, our team will innovate new, powerful data science approaches that gather and aggregate multiple data sources to produce accurate broadband maps. We will also create an Internet measurement and performance toolkit that will allow policymakers, administrators, and the public to locate and understand the gaps in Internet coverage and target critical resource investments among communities most in need.

Interdisciplinary researchers at the University of Chicago, including the Crown Family School of Social Work, Policy, and Practice and the Office of Civic Engagement, will work with Chicago Public Schools and civic non-profit organization Kids First and urban solutions accelerator City Tech Collaborative to develop technical tools and data that meet the needs of stakeholders and community members, reducing inequities in high-speed Internet access.


Nick Feamster is Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Research for the Data Science Institute. He researches computer networking and networked systems, with a particular interest in Internet censorship, privacy, and the Internet of Things. His work on experimental networked systems and security aims to make networks easier to manage, more secure, and more available.


Nicole Marwell is Associate Professor in the University of Chicago Crown Family School of Social Work, Policy, and Practice. She is also a faculty affiliate of the Department of Sociology, a faculty fellow at the Center for Spatial Data Science, and a member of the Faculty Advisory Council of the Mansueto Institute for Urban Innovation. Her research examines urban governance, with a focus on the diverse intersections between nonprofit organizations, government bureaucracies, and politics.

Bio: Arjun studies the security of machine learning systems, with a focus on adversarial and distributed learning. His work has exposed new vulnerabilities in learning algorithms, along with the development of a theoretical framework to analyze them. He was a finalist for the 2020 Bede Liu Best Dissertation Award, and won the 2019 Yan Huo *94 Graduate Fellowship and 2018 SEAS Award for Excellence at Princeton University. He received the 2018 Siemens FutureMakers Fellowship in Machine Learning, and was a finalist for the 2017 Bell Labs Prize. He is currently a postdoctoral scholar at UChicago with Ben Zhao and Nick Feamster.

Talk Title: The Role of Data Geometry in Adversarial Machine Learning

Talk Abstract: Understanding the robustness of machine learning systems has become a problem of critical interest due to their increasing deployment in safety critical systems. Of particular interest are adversarial examples, which are maliciously pertrubed test-time examples designed to induce misclassification. Most research on adversarial examples has focused on developing better attacks and ad hoc defenses, resulting in an attacker-defender arms race.

In this talk, we will step away from this paradigm and show how fundamental bounds on learning in the presence of adversarial examples can be obtained by viewing the problem through an information-theoretic lens. For fixed but arbitrary distributions, we demonstrate lower bounds on both the 0-1 and cross-entropy losses for robust learning. We compare these bounds to the performance of state-of-the-art robust classifiers and analyze the impact of different layers on robustness.

Kyle Macmillan is a PhD Student in Computer Science at the University of Chicago where he is advised by Professor Nick Feamster. He is a member of the NOISE Lab and is broadly interested in internet measurement, networks, and their applications in law and policy.

He received my BSE in Electrical Engineering from Princeton University in 2020. His undergraduate thesis was advised by Professor Prateek Mittal.

Tarun Mangla joined DSI as a postdoctoral scholar in summer 2020, and was previously a PhD student in the School of Computer Science at the Georgia Institute of Technology, co-advised by Mostafa Ammar and Ellen Zegura. His research interests span video streaming, network measurements, and cellular networks. He completed his bachelors in Computer Science and Engineering from Indian Institute of Technology, Delhi (2014) and MS in Computer Science from Georgia Tech (2018). He is a recipient of the Best Paper Award at IFIP TMA, 2018.

Nzinga-Ain Barberousse was homeschooled until college and remains engaged in the Chicagoland homeschooling community. She earned a Bachelor’s degree in Neuroscience from UIC and a Master’s degree in Clinical Psychology – Counseling Practice from Roosevelt University, where she graduated magna cum laude. Nzinga has experience in projects and program management, including work as a LPC-eligible Adolescent Therapist (focusing on trauma, mood, and substance use), experience as a Legal Project Manager, and time as the Piano Program Director for the Hyde Park Suzuki Institute.

Nzinga is a passionate animal lover, particularly of reptiles and rodents, and has special interest in neuropsychology, especially in the areas of Autism and ADHD. In her free time, she teaches private piano lessons, enjoys reading, embroidery, and baking, as well as trying new restaurants, learning new languages, and traveling to new places. Nzinga values collaboration and open communication and is a team player, always looking for new opportunities to learn and grow, both professionally and personally.

Jesse London is a software engineer at the Data Science Institute, where he contributes to open source initiatives.

Guilherme Martins works for DSI with Software and Data Engineering to ensure the real-time flow of the data that fuels mission-critical data analysis and processes. He’s currently assigned to the Internet Equity Project, where he manages the contributions and deployment of the main open-source package. He has an industrial background from companies like Alcatel-Lucent, LG Electronics, and HP and seeks to stay on top of the application of AI/ML, High-Performance Computing and modern data analysis pipelines to digital learning and social impact projects.

Marc is the technical project manager for the Internet Equity Initiative (IEI). He is responsible for working with our researchers and engineers to build cutting-edge, open-source network measurement tools to serve decision-makers in the broadband policy space. In addition to directing technical development for the IEI, Marc also oversees deployments of measurement devices to collect longitudinal, household-specific network performance data to support the IEI’s research objectives. Before coming to DSI, Marc graduated from the UChicago CAPP program in 2021. Prior to that, he was a project manager and analyst at a DC-based economic consulting company. Marc is a Colorado native and enjoys reading, skiing, and basically anything involving the great outdoors.