Jennie Rogers

I am an assistant professor of computer science at Northwestern University. Broadly speaking, my research is motivated by empowering people with data. More specifically, I investigate pragmatic privacy-preserving data analytics, federating databases over multiple data models, and new approaches with which individuals can explore and understand their datasets. I am especially interested in private data federations, wherein two or more mutually distrustful data providers compute SQL queries over the union of their records without revealing their sensitive input tuples to others. I received the NSF CAREER Award in 2019. My Erdös number is 3.

Research Projects

VaultDB: A Private Data Federation

For many people seeking actionable insights from data, their first major obstacle is getting access to relevant datasets. Despite the abundance of information we collect on practically every domain of life, much of it is fractured among many private data stores. For example, electronic health records on a given patient are often partitioned among multiple hospitals. Querying fractured datasets is often challenging owing to regulatory requirements and privacy concerns. A private data federation (PDF) queries the union of the private records of multiple autonomous data stores such that no one learns about the data of its peers. VaultDB, our PDF prototype, translates SQL queries into secure multi-party computation protocols and orchestrates their execution among the data providers. These cryptographic protocols incur substantial overhead for PDF queries with runtimes often 1-2 orders of magnitude slower than that of an insecure execution of the same query. We are researching a query optimizer for VaultDB that leverages properties of the relational model to enable PDF queries to run efficiently. Papers: [pdf], [pdf], Code: [github]

BigDAWG: A Federator for Heterogeneous Data Models

BigDAWG explores a new view of data federations to address the growing need for cross-database querying over heterogeneous data stores. This need is fueled by the proliferation of storage engines and data models - such as arrays, graphs, and text engines - each usually having its own query language and storage semantics. We are studying how to integrate and optimize queries that span many disparate data models through a single query interface. Papers: [pdf], [pdf], Code: [github]

Systems for Emerging Data Scientists and Data Enthusiasts

We are investigating novel data models and query frameworks for non-traditional data analysts. We have researched this in two domains: personal data-driven decision making and querying for scientists conducting research. In the latter, our focus is on principles and techniques for making the scientific method a first-class citizen in the database. We designed these frameworks based on interviews with individuals who are presently underserved by relational databases who also have an abundance of data. We have synthesized our initial findings from these studies into two vision papers. Papers: [pdf] [pdf]

Selected Publications

J. Bater, X. He, W. Ehrich, A. Machanavajjhala, and J. Rogers. "Shrinkwrap: Efficient SQL Query Processing in Differentially Private Data Federations," PVLDB, 12(3), 2018. [pdf]

J. Bater, G. Elliott, C. Eggen, S. Goel, A. Kho, and J. Rogers, "SMCQL: Secure Querying for Federated Databases," in PVLDB, 10(6), pages 673-684, 2017. [pdf] [code]

J. Duggan and M. Brodie, "Hephaestus: Data Reuse for Accelerating Scientific Discovery," in Proceedings of CIDR, 2015. [pdf]

J. Duggan, A. Elmore, M. Stonebraker, M. Balazinska, B. Howe, J. Kepner,et al., "The BigDAWG Polystore System," in Sigmod Record, 44(3), 2015. [pdf]

A. Elmore, J. Duggan, M. Stonebraker, M. Balazinska, U. Cetintemel, V. Gadepally, et al., "A Demonstration of the BigDAWG Polystore System," in VLDB, 8(12), 2015. [PDF]

J. Duggan and M. Stonebraker, "Incremental Elasticity for Array Databases," in SIGMOD 2014. [pdf]

J. Duggan, "The Case for Personal Data-Driven Decision Making," in VLDB, 7(11), pages 943-946, 2014. [pdf]

J. Duggan, U. Cetintemel, O. Papaemmanouil, E. Upfal, "Performance Prediction for Concurrent Database Workloads," in SIGMOD, 2011. [pdf]

P. Cudre-Mauroux, H. Kimura, K.-T. Lim, J. Rogers, R. Simakov, E. Soroush, et al., "A Demonstration of SciDB: A Science-Oriented DBMS," in VLDB, pages 1534-1537, 2009. [pdf]

Prospective Students

I am looking for graduate and senior undergraduate students to work on research projects. If you are Northwestern student and are interested in working with me, please drop me a line to set up an appointment. Please include a brief summary of your background and interests. If you are not enrolled at Northwestern, you should apply (undergraduate, graduate) to become one before touching base.


EECS 339: Introduction to Database Systems - Winter 2015, Winter 2016, Spring 2017, Fall 2017, Winter 2018, Fall 2018, and Spring 2019

EECS 395/495-46: Data Science Seminar - Fall 2015, Spring 2018, and Fall 2018

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