Sorelle Friedler

Assistant Professor of Computer Science

Sorelle Friedler is an Assistant Professor of Computer Science at Haverford College and an Affiliate at the Data & Society Research Institute. Her research interests include the design and analysis of algorithms, computational geometry, data mining and machine learning, and the application of such algorithms to interdisciplinary data.

Sorelle is the Program Committee Co-chair for the new Conference on Fairness, Accountability, and Transparency (FAT*) and is one of the organizers of the Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML). She has received a Fellowship and recent NSF grant for her work on preventing discrimination in machine learning. Her work on this topic has been featured in IEEE Spectrum, Gizmodo, and NBC News and she has been interviewed about algorithmic fairness by the Guardian, Bloomberg, and NPR.

Sorelle is the recipient, along with chemistry professors Josh Schrier and Alex Norquist, of two NSF Grants to apply data mining techniques to materials chemistry data to speed up materials discovery. Their paper on this work was featured on the cover of Nature and was covered by The Wall Street Journal and Scientific American.

Before Haverford, Sorelle was a software engineer at Alphabet (formerly Google), where she worked in the X lab and in search infrastructure. She received a Ph.D. in computer science in 2010 and an M.S. in computer science in 2007, both from the University of Maryland, College Park. She is a 2004 graduate of Swarthmore College.

CV (pdf) Grants Papers Press Teaching

Grants

NSF DMR-1709351 (2017 - 2020): CDS&E: D3SC: The Dark Reaction Project: A machine-learning approach to exploring structural diversity in solid state synthesis. Joshua Schrier, Sorelle Friedler, and Alexander Norquist. $645,288.

NSF IIS-1633387 (2016 - 2019): BIGDATA: Collaborative Research: F: Algorithmic Fairness: A Systemic and Foundational Treatment of Nondiscriminatory Data Mining. Suresh Venkatasubramanian, danah boyd, and Sorelle Friedler. $953,432 (Haverford portion: $172,742).

Knight News Challenge Prototype Fund (2016): Could your data discriminate? Sorelle Friedler, Wilneida Negron, Surya Mattu, Suresh Venkatasubramanian. $35,000.

Data & Society Research Institute Fellow (2015 - 2016): Preventing Discrimination in Machine Learning: from theory to law and policy. $10,000.

NSF DMR-1307801 (2013 - 2016): The Dark Reaction Project: a machine learning approach to materials discovery. Joshua Schrier, Alexander Norquist, and Sorelle Friedler. $299,998.

Papers

Journal Papers

Paul Raccuglia, Katherine C. Elbert, Philip D. F. Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Matthias Zeller, Sorelle A. Friedler, Joshua Schrier, and Alexander J. Norquist. Machine-learning-assisted materials discovery using failed experiments. Nature, 533: 73 - 76, May 5, 2016. [link | project site]

Sorelle A. Friedler and David M. Mount. A Sensor-Based Framework for Kinetic Data Compression. Computational Geometry: Theory and Applications, 48(3): 147 - 168, March 2015. (doi: 10.1016/j.comgeo.2014.09.002) [PDF | link]

Sorelle A. Friedler and David M. Mount. Approximation algorithm for the kinetic robust k-center problem. Computational Geometry: Theory and Applications, 2010. (doi: 10.1016/j.comgeo.2010.01.001). [PDF (preprint) | link]

Sorelle A. Friedler, Yee Lin Tan, Nir J. Peer, and Ben Shneiderman. Enabling teachers to explore grade patterns to identify individual needs and promote fairer student assessment. Computers & Education, 51(4):1467-1485, December 2008. [PDF (preprint) | link] [code and help videos]

Peer-reviewed Conference Proceedings

Philip Adler, Casey Falk, Sorelle A. Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. Auditing Black-box Models for Indirect Influence. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2016. [PDF | code]

F. Betul Atalay, Sorelle A. Friedler, and Dianna Xu. Convex hull for probabilistic points. In Technical Papers of the 29th Conference on Graphics, Patterns and Images (SIBGRAPI '16), 2016. [PDF]

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. [PDF | code]

Sorelle A. Friedler and David M. Mount. Spatio-temporal range searching over compressed kinetic sensor data. In Proc. of the European Symposium on Algorithms (ESA), pages 386-397, 2010. [PDF (preprint) | link] [TR]
     2nd Workshop on Massive Data Algorithmics, 2010 [PDF]
     Fall Workshop on Computational Geometry, 2009 [PDF]

Sorelle A. Friedler and David M. Mount. Compressing kinetic data from sensor networks. In Proc. of the 5th International Workshop on Algorithmic Aspects of Wireless Sensor Networks (AlgoSensors), pages 191 - 202, 2009. [PDF (preprint) | link] [TR]

Workshop Papers and Technical Reports

Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Runaway Feedback Loops in Predictive Policing. Presented as a talk at the Fairness, Accountability, and Transparency in Machine Learning Workshop, Aug. 14, 2017. [link]

Danielle Ensign, Sorelle Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Decision Making with Limited Feedback: Error bounds for Recidivism Prediction and Predictive Policing. Presented as a poster at the Fairness, Accountability, and Transparency in Machine Learning Workshop, Aug. 14, 2017. [PDF]

Richard L. Phillips, Kyu Hyun Chang, and Sorelle A. Friedler. Interpretable Active Learning. Presented at the ICML Workshop on Human Interpretability in Machine Learning, Aug. 10, 2017.

Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im)possibility of fairness. arXiv:1609.07236, Sept. 23, 2016. [link]

Ifeoma Ajunwa, Sorelle Friedler, Carlos E. Scheidegger, and Suresh Venkatasubramanian. Hiring by Algorithm: Predicting and Preventing Disparate Impact. Presented at the Yale Law School Information Society Project conference Unlocking the Black Box: The Promise and Limits of Algorithmic Accountability in the Professions, Apr. 2, 2016. [PDF]

Philip Adler, Casey Falk, Sorelle A. Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, Suresh Venkatasubramanian. Auditing Black-box Models by Obscuring Features. arXiv:1602.07043. [link]

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact. Presented at the Fairness, Accountability, and Transparency in Machine Learning Workshop, Dec. 12, 2014. [link]

F. Betul Atalay, Sorelle A. Friedler, and Dianna Xu. Probabilistic Kinetic Data Structures. Presented at the Fall Workshop on Computational Geometry, Oct. 25, 2013. [PDF | link]

Sorelle A. Friedler and David M. Mount. Realistic compression of kinetic sensor data. Technical Report CS-TR-4959, University of Maryland, College Park, 2010. [PDF | TR]

Thesis

Sorelle A. Friedler. Geometric Algorithms for Objects in Motion. Dissertation committee: Prof. David Mount (chair), Prof. William Gasarch, Prof. Samir Khuller, Prof. Steven Selden, Prof. Amitabh Varshney. Defense date: July 30, 2010. [PDF] [presentation]

Patents

Mohammed Waleed Kadous, Isaac Richard Taylor, Cedric Dupont, Brian Patrick Williams, Sorelle Alaina Friedler. Permissions based on wireless network data. US 20130244684 A1. Publication date: Sep. 19, 2013.

Sorelle Alaina Friedler, Mohammed Waleed Kadous, Andrew Lookingbill. Position indication controls for device locations. US 20130131973 A1 (also WO 2013078125 A1). Publication date: May 23, 2013.

Press

Related to Machine-learning-assisted materials discovery using failed experiments:

Adam Marcus and Ivan Oransky. What scientists could learn from startups. The Week and STAT, May 12, 2016.

Daniela Hernandez. Why Machines Should Learn From Failures. The Wall Street Journal, May 6, 2016.

Jordana Cepelewicz. Lab Failures Turn to Gold in Search for New Materials. Scientific American, May 6, 2016.

Philip Ball. Computer gleans chemical insight from lab notebook failures. Nature News, May 4, 2016.

Related to Certifying and removing disparate impact:

Lauren J. Young. Computer Scientists Find Bias in Algorithms. IEEE Spectrum, August 21, 2015.

Julianne Pepitone. Can Resume-Reviewing Software Be As Biased As Human Hiring Managers? NBC News, August 17, 2015.

Kiona Smith-Strickland. Computer Programs Can Be as Biased as Humans. Gizmodo, August 16, 2015.

Background on Algorithmic Fairness:

Sam Levin. A beauty contest was judged by AI and the robots didn't like dark skin. The Guardian, September 8, 2016.

David Ingold and Spencer Soper. Amazon Doesn't Consider the Race of Its Customers. Should It? Bloomberg, April 21, 2016.

Rose Eveleth. The Inherent Bias of Facial Recognition. Motherboard, March 21, 2016.

Laura Sydell. Can Computer Programs be Racist and Sexist? NPR, March 15, 2016.

Lauren Kirchner. When big data becomes bad data. ProPublica, September 2, 2015.

Hal Hodson. No one in control: The algorithms that run our lives. New Scientist, February 4, 2015.

Regularly Taught Classes

CS 104: Topics in Introductory Programming
CS 105: Introduction to Computer Science
CS 207: Data Science and Visualization
CS 340: Analysis of Algorithms
CS 395: Mobile Development for Social Change
CS 399: Senior Thesis (advising) - see this topics list if you might be interested in working with me (only accessible when signed into haverford.edu)

Past Classes

CS 101: Fluency with Information Technology

Design and Analysis of Computer Algorithms, Summer 2009, University of Maryland, College Park
Organization of Programming Languages, Summer 2007, University of Maryland, College Park
Computer Organization (TA) Spring 2006, University of Maryland, College Park
Introduction to Low-Level Programming Concepts (TA) Fall 2005, University of Maryland, College Park