Nick Feamster

About

Nick Feamster is a Neubauer Professor of Computer Science at the University of Chicago. His research focuses on areas within computer science that include security and privacy, data science, and the ethical implications of artificial intelligence. He is recognized for his impactful research and contributions to understanding and unmasking AI music, as well as addressing issues related to dark patterns in digital interfaces. As a distinguished faculty member, Feamster has received notable awards such as the 2026 Quantrell Teaching Award. His work often explores the intersection of technology, society, and policy, emphasizing the importance of ethical considerations in the development and deployment of computing systems. His academic and research pursuits aim to advance the understanding of complex issues in computer science, contributing to both theoretical foundations and practical applications.

Research topics

• Computer Science
• Internet privacy
• World Wide Web
• Computer Security
• Sociology
• Political Science
• Telecommunications
• Embedded system
• Data science
• Human–computer interaction

Selected publications

• IoT Inspector

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2020 · 113 citations

  Senior authorCorresponding

  The proliferation of smart home devices has created new opportunities for empirical research in ubiquitous computing, ranging from security and privacy to personal health. Yet, data from smart home deployments are hard to come by, and existing empirical studies of smart home devices typically involve only a small number of devices in lab settings. To contribute to data-driven smart home research, we crowdsource the largest known dataset of labeled network traffic from smart home devices from wit…

  DOI

• A Longitudinal Study of the Prevalence of WiFi Bottlenecks in Home Access Networks

  2024-11-01 · 4 citations

  article

  Although home wireless networks (WiFi) are increasingly becoming performance bottlenecks, there are no research studies based on long-running field deployments that document this phenomenon. Given both public and private investment in broadband Internet infrastructure, a rigorous study of this phenomenon---and accompanying public data, based on open-source methods, is critical. To this end, this study pioneers a system and measurement technique to directly assess WiFi and access network performa…

  PublisherDOI

• Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA)

  2025-04-24 · 3 citations

  articleOpen access

  To protect consumer privacy, the California Consumer Privacy Act (CCPA) mandates that businesses provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy over the opt-out process allows them to impose hurdles on consumers aiming to opt out, including by employing dark patterns. Motivated by the enactment of the California Privacy Rights Act (CPRA), which strengthens the CCPA and explicitly forbids ce…

  PublisherDOI

• CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs

  arXiv (Cornell University) · 2024-11-21 · 2 citations

  preprintOpen accessSenior author

  Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A models have emerged as a promising alternative, allowing users to query partitions of configurations through prompts and receive answers based on learned patterns, thanks to transformer models pre-trained on vast datasets that provide generic configuration context for interpreting router configurations. Yet, current…

  PublisherOA PDFDOI

• Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a s…

  PublisherDOI

Recent grants

• FIA: Collaborative Research: Architecting for Innovation

  NSF · $200k · 2010–2014

• NeTS: Medium: Collaborative Research: Optimizing Network Support for Cloud Services: From Short-Term Measurements to Long-Term Planning

  NSF · $400k · 2015–2017

• RAPID: Measuring the Effects of the COVID-19 Pandemic on Broadband Access Networks to Inform Robust Network Design

  NSF · $187k · 2020–2023

Frequent coauthors

• Renata Teixeira

  Netflix (United States)

  61 shared

• Paul Schmitt

  University of Hawaiʻi at Mānoa

  59 shared

• Francesco Bronzino

  52 shared

• Jennifer Rexford

  47 shared

• Srikanth Sundaresan

  Menlo School

  36 shared

• Vytautas Valancius

  Google (United States)

  35 shared

• Hari Balakrishnan

  IIT@MIT

  32 shared

• Marshini Chetty

  University of Chicago

  31 shared

Labs

• Research Labs & GroupsPI

Education

• Ph.D., Computer Science

  MIT

  2005

• Other, Electrical Engineering and Computer Science

  MIT

  2001

• Other, Electrical Engineering and Computer Science

  MIT

  2000

Awards & honors

• ACM Fellow
• USENIX Community Contribution Award, USENIX/ACM Symposium on…
• USENIX “Test of Time” Best Paper Award
• Internet Research Task Force Applied Networking Research Pri…
• ACM SIGCOMM Community Award

Similar researchers at University of Chicago

• Ian Fosterh-150
• Michael Franklinh-97
• David Cashh-75