Sanjay Krishnan

About

Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research group studies corrupted, missing, or otherwise uncertain data in database and information retrieval systems. His current research focuses on systems that can provide certifiable accuracy guarantees in partially complete databases, query accuracy evaluation in corrupted databases, and automatic detection of data leakage. His research areas include Data Science, Databases, and Machine Learning, with a particular emphasis on managing and analyzing data at scale. He is involved in labs and groups such as ChiDATA and the Systems Group, conducting research on large-scale video analysis, efficient data processing systems, and the economics of data. His work aims to advance understanding and development of systems that handle uncertain data, ensuring reliability and security in data management and retrieval processes.

Research topics

• Computer Science
• Data Mining
• Information Retrieval
• Telecommunications
• Algorithm
• World Wide Web
• Database
• Real-time computing
• Data science
• Engineering

Selected publications

• How Large Language Models Will Disrupt Data Management

  Proceedings of the VLDB Endowment · 2023 · 97 citations

  Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to understand, process, and synthesize language. The authors of this paper believe that this advance in technology is significant enough to prompt introspection in the data management community, similar to previous technological disruptions such as the advents of the world wide web, cloud computing, and statistical machine learning. We argue that the disruptive influence that LLMs will have on data management wil…

  PublisherDOI

• Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

  IEEE Open Journal of Vehicular Technology · 2020 · 21 citations

  Senior authorCorresponding

  The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we…

  DOI

• Neighborhood sociome factors and pediatric asthma exacerbations: Protective role of tree crown density and importance of pharmacy access in Chicago's south side

  Pediatric Allergy and Immunology · 2025-07-01 · 4 citations

  articleOpen access

  BACKGROUND: Pediatric asthma exacerbations remain a critical public health concern, particularly in historically underserved urban settings. OBJECTIVE: This study investigates sociome factors-the social context of disease-associated with asthma exacerbations among children living in Chicago's South Side, leveraging clinical and publicly available generalizable census tract-level datasets from agencies including ChiVes, the City of Chicago Data Portal, EPA, Census Bureau, HUD, NOAA, and more. The…

  PublisherOA PDFDOI

• DePLOI: Applying NL2SQL to Synthesize and Audit Database Access Control

  arXiv (Cornell University) · 2024-02-11 · 4 citations

  preprintOpen accessSenior author

  In every enterprise database, administrators must define an access control policy that specifies which users have access to which tables. Access control straddles two worlds: policy (organization-level principles that define who should have access) and process (database-level primitives that actually implement the policy). Assessing and enforcing process compliance with a policy is a manual and ad-hoc task. This paper introduces a new access control model called Intent-Based Access Control for D…

  PublisherOA PDFDOI

• Toward a Life Cycle Assessment for the Carbon Footprint of Data

  2023-07-09 · 4 citations

  articleSenior author

  The growing data economy features a complex ecosystem of organizations, individuals, and devices. With digital data exchange between entities becoming ubiquitous in modern society, there is a need for carbon cost estimates that span the entire life cycle of data. We argue that accounting at the granularity of an application, process, or request can be augmented by a scheme that associates carbon annotations with data. Such a scheme would preserve continuity between interacting entities in the da…

  PublisherDOI

Frequent coauthors

• Michael J. Franklin

  University of Chicago

  61 shared

• Ken Goldberg

  University of California, Berkeley

  52 shared

• Ken Goldberg

  41 shared

• Aaron J. Elmore

  University of Chicago

  38 shared

• Arie van Deursen

  Delft University of Technology

  36 shared

• Peter A. Raymond

  36 shared

• D. Bosscher

  University of Aberdeen

  36 shared

• Marinus van Hulst

  University of Groningen

  36 shared

Labs

• Systems GroupPI

Education

• Ph.D., Computer Science

  University of Chicago

  2005

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  2000

• B.S., Computer Science

  University of Illinois at Urbana-Champaign

  1998

Awards & honors

• CAREER Award for Resource-Efficient Databases (2021)

Similar researchers at University of Chicago

• Ian Fosterh-150
• David Cashh-75