Professor and National Center Chair
Department of Computer and Information Science at the University of Pennsylvania
Founding Director, Warren Center for Network and Data Sciences
Founder and former Director, Penn program in Networked and Social Systems Engineering
Secondary Appointments in Economics, Statistics and Data Science (Wharton) and Operations, Information and Decisions (Wharton)

Mailing Address: 509 Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104-6389
Phone: 215.898.7888
3401 Walnut office: 404B
Email: mkearns@cis.upenn.edu
Amazon Email: kearmic@amazon.com
Twitter: @mkearnsupenn

Admin Support, Warren Center and NETS Program Manager and Social Media Outreach:
Lily Hoot
Phone: 215.573.0861
Email: lhoot@seas.upenn.edu


My research interests include topics in machine learning, artificial intelligence, algorithmic game theory and microeconomics, computational social science, and quantitative finance and algorithmic trading. I often examine problems in these areas using methods and models from theoretical computer science and related disciplines. While much of my work is mathematical in nature, I also often participate in empirical and experimental projects, including applications of machine learning to problems in algorithmic trading and quantitative finance, and human-subject experiments on strategic and economic interaction in social networks.


For (in)convenience, most of this site is organized as a single flat html file. The links below let you navigate directly to the various subsections.

Publications   Research Group Members   Teaching and Tutorial Material   Professional Bio   Educational Background   Editorial and Professional Service   Press


Aaron Roth and I have written a general-audience book about the science of designing algorithms that embed social values like privacy and fairness; here is the publisher's description:

Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.

Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

Here are links to some media related to the book:

  • A review in Nature.
  • A review/summary in strategy+business.
  • A policy briefing for the Brookings Institution on technology regulatory implications.
  • A talk video filmed at the Carnegie Council for Ethics in International Affairs in NYC, along with a full transcript and slides.
  • A Penn podcast in which we discuss some of the book's topics.
  • A piece on the book on NPR Marketplace Morning Report.
  • Interview on WHYY's Radio Times (at 32 minute mark) November 2019.
  • An excerpt from the book in Penn Today.
  • An opinion piece adapted from book themes in Scientific American.


    Teaching Spring 2024: CIS 4230/5230, Ethical Algorithm Design.
    Teaching Fall 2023: CIS 6250, Theory of Machine Learning.
    Warren Center for Network and Data Sciences
    Networked and Social Systems Engineering (NETS) Program
    Penn undergraduate course Networked Life (NETS 112), Fall 2019 and a condensed video version.
    Penn-Lehman Automated Trading Project (inactive)
    Tribute Day for Les Valiant, May 2009

    Videos of some miscellaneous talks:

  • "The Ethical Algorithm" (University of Washington, 2019 Taskar Memorial Lecture, general audience)
  • "The Ethical Algorithm" (Centre for Ethics, University of Toronto 2019, general audience)
  • "Machine Learning and Social Norms" (Santa Fe Institute 2017, general audience)
  • Panel discussion on privacy policy and regulation (AT&T Forum 2017, general audience)
  • "Fair Algorithms for Machine Learning" (ACM EC 2017, technical)
  • "Games, Networks and People" (NIPS 2014, technical)
  • "The Complexity of Economics" (Santa Fe Institute 2017; general audience)
  • "Algorithmic Trading: The Machine Learning Approach", (Quantcon 2015, technical)



    Since 2002 I have been a professor in the Computer and Information Science Department at the University of Pennsylvania, where I hold the National Center Chair. I have secondary appointments in the department of Economics, and in the departments of Statistics and Data Science and Operations, Information and Decisions (OID) in the Wharton School. I am the Founding Director of the Warren Center for Network and Data Sciences, where my Co-Director is Rakesh Vohra. I am the faculty founder and former director of Penn Engineering's Networked and Social Systems Engineering (NETS) Program, whose current directors are Andreas Haeberlen and Aaron Roth. I am a faculty affiliate in Penn's Applied Math and Computational Science graduate program. Until July 2006 I was the co-director of Penn's interdisciplinary Institute for Research in Cognitive Science.

    Since June 2020, I am an Amazon Scholar, focusing on fairness and privacy in machine learning and related topics within Amazon Web Services.

    I have worked extensively in quantitative and algorithmic trading on Wall Street (including at Lehman Brothers, Bank of America, SAC Capital and Morgan Stanley; see further details below). I often serve as an advisor to technology companies and venture capital firms, and sometimes invest in early-stage technology startups. I occasionally serve as an expert witness or consultant on technology-related legal and regulatory cases.

    I am an elected Member/Fellow of the National Academy of Sciences, the American Academy of Arts and Sciences, the Association for Computing Machinery, the Association for the Advancement of Artificial Intelligence, and the Society for the Advancement of Economic Theory.

    The Past:

    I spent the decade 1991-2001 in machine learning and AI research at AT&T Bell Labs. (Here is an epic photo of Labs staff from 1995, there are many famous scientists peppered throughout.) During my last four years there, I was the head of the AI department, which conducted a broad range of systems and foundational AI work; I also served briefly as the head of the Secure Systems Research department. The AI department boasted terrific colleagues and friends that included Charles Isbell (now at Georgia Tech), Diane Litman (now at University of Pittsburgh), Michael Littman (later at Rutgers, now at Brown), David McAllester (now at TTI-Chicago), Satinder Singh (now at University of Michigan), Peter Stone (now at University of Texas), and Rich Sutton (now at University of Alberta). Prior to my time as its head, the AI department was shaped by the efforts of a number of notable figures, including Ron Brachman (who originally founded the department; now at Cornell Tech), Henry Kautz (who led the department before heading to the University of Washington; now at the University of Rochester), and Bart Selman (now at Cornell). Before leading the AI group, I was a member of the closely related Machine Learning department at the labs, which was headed by Fernando Pereira (later at Penn, now at Google), and included Michael Collins (later at MIT and Columbia, now at Google), Sanjoy Dasgupta (now at UCSD), Yoav Freund (now at UCSD), Rob Schapire (later at Princeton, now at Microsoft Research), William Cohen (now at CMU), and Yoram Singer (later at Hebrew University and Google, now at Princeton). Other friends and colleagues from Labs days include Sebastian Seung (later at MIT, now at Princeton), Lawrence Saul (later at Penn, now at UCSD), Yann LeCun (now at Facebook and NYU), Roberto Pieraccini (now at Jibo), Esther Levin (now at Point72), Lyn Walker (now at UC Santa Cruz), Corinna Cortes (now at Google), and Vladimir Vapnik (now at Facebook).

    I spent 2001 as CTO of the European venture capital firm Syntek Capital, and joined the Penn faculty in January 2002.

    From June 2018 to June 2020, I led applied research in the AI Center of Excellence at Morgan Stanley, along with Yuriy Nevmyvaka (with whom I have also collaborated on a number of papers on algorithmic trading ).

    From June 2016 to March of 2018, I was the Chief Scientist of MANA Partners, a trading, technology and asset management firm based in NYC. From early 2014 to June 2016, I led a quantitative portfolio management team with Yuriy Nevmyvaka at Engineers Gate. From June 2009 through September 2013, we were PMs in the MultiQuant division of SAC Capital in New York City. From May 2007 through April 2009, we led a quantitative trading team at Bank of America in New York City, working on both proprietary and algorithmic trading strategies within BofA's Electronic Trading Services division. From the Spring of 2002 through May 2007, I was first a consultant to, and later the head of, a quant prop trading team within the Equity Strategies group of Lehman Brothers in New York City.

    I spent most of 2011 on sabbatical in Cambridge, England, where I visited the University of Cambridge Economics Department and was a visiting Fellow at Christ's College. I also spent time visiting Microsoft Research Cambridge.

    I have served as an advisor to the startups Yodle (acquired by web.com), Wealthfront, Activate Networks, RootMetrics (acquired by IHS), Convertro (acquired by AOL), Invite Media (acquired by Google), SiteAdvisor (founded by Chris Dixon; acquired by McAfee), PayNearMe (formerly known as Kwedit), and Riverhead Networks (acquired by Cisco). I was also involved in Dixon's startup Hunch (acquired by eBay), and have been a consultant to Bessemer Venture Partners.

    In the past I have served as a member of the Advanced Technology Advisory Council of PJM Interconnection. the Scientific Advisory Board of Opera Solutions, and the Technical Advisory Board of Microsoft Research Cambridge. I am a former member of the Scientific Advisory Board of the Alan Turing Institute, and of the Market Surveillance Advisory Group of FINRA, and a former external faculty member at the Santa Fe Institute.


    I did my undergraduate studies at the University of California at Berkeley in math and computer science, graduating in 1985. I received a Ph.D. in computer science from Harvard University in 1989. The title of my dissertation was The Computational Complexity of Machine Learning (see Publications below for more information), and Les Valiant was my (superb) advisor. Following postdoctoral positions at the Laboratory for Computer Science at M.I.T. (hosted by Ron Rivest ) and at the International Computer Science Institute (ICSI) in Berkeley (hosted by Dick Karp ), in 1991 I joined the research staff of AT&T Bell Labs, and later the Penn faculty (see professional bio above).

    Alongside my formal education, I was strongly influenced by being raised in an academic family, which included my father David R. Kearns (UCSD, Chemistry); his brother, and my uncle Thomas R. Kearns (Amherst College, Philosophy); their father, and my paternal grandfather, Clyde W. Kearns (University of Illinois, Entomology); my mother Alice Chen Kearns, who was an early influence on my writing; and her father, and my maternal grandfather Chen Shou-Yi (Pomona College, Chinese History and Literature).


    In the past I have been program chair or co-chair of ACM FAccT, NIPS, AAAI, COLT, and ACM EC. I have also served on the program committees of NIPS, AAAI, IJCAI, COLT, UAI, ICML, STOC, FOCS, and a variety of other acryonyms. I am a member of the NIPS Foundation, and was formerly on the steering committee for the Snowbird Conference on Learning (RIP).

    I am on the editorial board of the MIT Press series on Adaptive Computation and Machine Learning, and the editorial board of the journals PNAS Nexus and Market Microstructure and Liquidity. I also act as a handling editor for the Proceedings of the National Academy of Sciences.

    In the past I have served on the editorial boards of Games and Economic Behavior, the Journal of the ACM, SIAM Journal on Computing, Machine Learning, the Journal of AI Research, and the Journal of Machine Learning Research.

    I serve as a member and current chair of the ACM A.M. Turing Award Committee.

    I am currently a member of the Emerging Technology Technical Advisory Committee of the U.S. Department of Commerce.

    I am a former member of the Computer Science and Telecommunications Board of the National Academies. From 2002-2008 I was a member, vice chair and chair of DARPA's Information Science and Technology (ISAT) study group.


    Current (alphabetical):

    Postdoc Yahav Bechavod (hosted by Aaron Roth )
    Doctoral student Natalie Collina (jointly advised with Aaron Roth )
    Doctoral student Ira Globus-Harris (jointly advised with Aaron Roth )
    Doctoral student Varun Gupta (jointly advised with Aaron Roth )
    Doctoral student Georgy Noarov (jointly advised with Aaron Roth )
    Doctoral student Mirah Shi (jointly advised with Aaron Roth )
    Doctoral student Sikata Sengupta (jointly advised with Aaron Roth and Duncan Watts )
    Postdoc Jess Sorrell (hosted by Aaron Roth )

    Alumni (reverse chronological):

    Former doctoral student Alexander Tolbert, now on the Emory University faculty
    Former Masters student Declan Harrison, now an officer in the U.S. Navy
    Former doctoral student Emily Diana, now on the research faculty at TTI Chicago, then joining CMU faculty
    Former doctoral student Saeed Sharifi-Malvajerdi, now on the research faculty at TTI Chicago
    Former doctoral student Chris Jung, now a postdoc at Stanford
    Former Warren Center postdoc Travis Dick, now at Google Research NYC
    Former Warren Center postdoc Juba Ziani, now on the Georgia Tech faculty
    Former doctoral student Hadi Elzayn , now a research scientist at Meta/Facebook
    Former doctoral student Seth Neel, now on the Harvard Business School faculty
    Former doctoral student Shahin Jabbari, now on the Drexel faculty
    Former Warren Center postdoc Jieming Mao, now at Google Research NYC
    Former Warren Center postdoc Bo Waggoner, now on the University of Colorado faculty
    Former Warren Center postdoc Jamie Morgenstern, now on the University of Washington faculty
    Former doctoral student Steven Wu, now on the CMU faculty
    Former doctoral student Hoda Heidari, now on the CMU faculty
    Former doctoral student Ryan Rogers, now at LinkedIn
    Former Warren Center postdoc Grigory Yaroslavtsev, now on the George Mason University faculty
    Former graduate student Lili Dworkin now at Recidiviz
    Former doctoral student Kareem Amin, now at Google Research NYC
    Former research scientist Stephen Judd
    Former doctoral student Mickey Brautbar, now at Shipt
    Former postdoc Jake Abernethy, now on the Georgia Tech faculty
    Former postdoc Karthik Sridharan, now on the Cornell faculty
    Former postdoc Kris Iyer, now on the Cornell faculty
    Former MD/PhD student Renuka Nayak, now on the UCSF faculty
    Former doctoral student Tanmoy Chakraborty, now at Facebook
    Former postdoc Umar Syed, now at Google Research NYC
    Former doctoral student Jinsong Tan, now at Square
    Former postdoc Eugene Vorobeychik, now on the Washington University faculty
    Former postdoc Giro Cavallo, now at Yahoo! NYC
    Former doctoral student Jenn Wortman Vaughan, now at Microsoft Research NYC
    Former postdoc Eyal Even-Dar, now at Final Israel
    Former doctoral student Sid Suri, now at Microsoft Research NYC
    Former postdoc Sham Kakade, now on the Harvard faculty
    Former postdoc Ryan Porter
    Former postdoc Luis Ortiz, now on the University of Michigan-Dearborn CS faculty
    Former postdoc John Langford, now at Microsoft Research NYC


    Teaching Spring 2024: CIS 4230/5230, Ethical Algorithm Design.
    Teaching Fall 2023: CIS 6250, Theory of Machine Learning.
    Web page for the undergraduate course Networked Life (NETS 112), Fall 2019 and a condensed online video version.
    (See also the Fall 2018,   Fall 2017,   Fall 2016,   Fall 2015,   Fall 2014,   Fall 2013,   Fall 2012,   Fall 2011 (hosted at Lore),   Spring 2010,   Spring 2009,   Spring 2008,   Spring 2007,   Spring 2006,   Spring 2005, and Spring 2004 offerings.)

    Web page for MKSE 150: Market and Social Systems on the Internet, Spring 2013, taught jointly with Aaron Roth.

    Web page for the graduate seminar No Regrets in Learning and Game Theory, Spring 2013, run jointly with Aaron Roth.
    Here are the slides for my STOC 2012 tutorial on Algorithmic Trading and Computational Finance
    Web page for CIS 625, Spring 2018: Computational Learning Theory. Here is the Spring 2016 version, an earlier version with Grigory Yaroslavtsev, an earlier version with Jake Abernethy, and an earlier version with Koby Crammer.
    Web page for the graduate seminar course Social Networks and Algorithmic Game Theory, Fall 2009
    Web page for CIS 620, Fall 2007: Seminar on Foundations of Cryptography.
    Web page for CIS 620, Fall 2006: Seminar on Sponsored Search.
    Web page for the graduate seminar CIS 700/04: Advanced Topics in Machine Learning (Fall 2004).
    Web page for CIS 700/04: Advanced Topics in Machine Learning (Fall 2003).
    Web page for a course on Computational Game Theory (Spring 2003). This was a joint course between CIS and Wharton (listed as CIS 620 and Wharton OPIM 952).
    Course web page for CIS 620: Advanced Topics in AI (Spring 2002)
    Course web page for CIS 620: Advanced Topics in AI (Spring 1997)
    Web page for NIPS 2002 Tutorial on Computational Game Theory.
    ACL 1999 Tutorial Slides [PDF]
    Course Outline and Material for 1999 Bellairs Institute Workshop
    Theoretical Issues in Probabilistic Artificial Intelligence (FOCS 98 Tutorial) [PDF]
    A Short Course in Computational Learning Theory: ICML '97 and AAAI '97 Tutorials [PDF]


    Below are some press/media articles about my research/work, or in which I am quoted, or which I authored. (Some links are behind paywalls or are unfortunately now dead.)

    Articles in Semafor and Penn Engineering blog on this model disgorgement paper, May 2024.
    Philadelphia FOX29 TV piece on Penn Engineering's new AI major, February 2024.
    Podcast on responsible AI in the generative era, on This Week in Machine Learning with Sam Charrington, December 2023.
    Ever-so-brief sound bite about ChatGPT on NPR's All Thing Considered, November 2023.
    InformationWeek article on ChatGPT and the Great App-ocalypse, November 2023.
    Amazon post on clean rooms differential privacy product launch, November 2023.
    Amazon Science blog post on Responsible AI in the wild: Lessons learned at AWS, with Aaron Roth, November 2023.
    Article about Apple and generative AI in AI Business, October 2023.
    Article about LLM prompt research in AI Business, September 2023.
    "Bridging Philly" podcast and radio program on generative AI with Cary Coglianese and host Raquel Williams, July 2023.
    Penn Engineering podcast and video on "The Growth and Impact of Generative AI", May 2023.
    Amazon Science blog post on Responsible AI in the generative era, May 2023.
    Penn Engineering blog post on the vulnerability of US Census data to reconstruction attack, February 2023.
    "The Take" podcast episode on the human cost of ChatGPT, February 2023.
    Philadelphia Inquirer article on face scanning at PHL, January 2023.
    Die Zeit Article on ChatGPT, January 2023.
    This Week in Machine Learning podcast with Sam Charrington, January 2023.
    Articles on AWS AI/ML launch of service cards in Reuters, Tech Times, and Venture Beat, December 2022.
    "Eye on AI" podcast, August 2022.
    Science News article on AI and ethics, February 2022.
    Interview with Clubic related to AWS ML Summit (en Francais), June 2021.
    Actuia article related to AWS ML Summit (en Francais), May 2021.
    Press release on election to National Academy of Sciences and an article in Penn Today, April 2021.
    "Who Should Stop Unethical AI?", The New Yorker   [PDF version] February 2021, and a follow-up article in Psychology Today, April 2021.
    Penn Gazette interview on "The Ethical Algorithm", November 2020.
    Series of articles on bias in AI in Quartz, March 2020.
    NPR Marketplace on algorithmic trading and coronavirus fears, March 2020.
    Ipse Dixit podcast on "The Ethical Algorithm", March 2020.
    WHYY's The Pulse piece on "Can Algorithms Help Judges Make Fair Decisions?", February 2020.
    Tech Nation interview with Moira Gunn on "The Ethical Algorithm", January 2020.
    Interview with Aaron Roth about "The Ethical Algorithm" in SINC (Spanish), January 2020.
    Philadelphia Inquirer article about face scanning at PHL, January 2020.
    Fintech Beat podcast with Chris Brummer on "The Ethical Algorithm", January 2020.
    Review of "The Ethical Algorithm" in Nature, January 2020.
    Discussion of "The Ethical Algorithm" at Keystone Strategy NYC, aired on CSPAN's Book TV, December 2019.
    Discussion of "The Ethical Algorithm" on Beyond50 Radio, December 2019.
    "The Ethical Algorithm" on Talks at Google, December 2019.
    Steptoe CyberLaw podcast on "The Ethical Algorithm", December 2019.
    Podcast on "The Ethical Algorithm" for Carnegie Council, December 2019.
    Podcast on "The Ethical Algorithm" on Knowledge@Wharton, December 2019.
    Podcast of Seattle Town Hall talk on "The Ethical Algorithm", moderated by Eric Horvitz, November 2019.
    Interview about "The Ethical Algorithm" on WHYY's Radio Times (at 32 minute mark), November 2019.
    Opinion piece adapted from themes in "The Ethical Algorithm" in Scientific American, November 2019.
    Excerpt from "The Ethical Algorithm" in Penn Today, November 2019.
    NPR Marketplace Morning Report interview on "The Ethical Algorithm", October 2019.
    Very brief informational article on deepfakes in Christian Science Monitor, October 2019.
    Knowledge@Wharton article on the market for consumer data and related privacy concerns, October 2019.
    A couple of articles in Penn Today on AI, ML and "The Ethical Algorithm" and a related podcast , September 2019.
    NPR Marketplace interview on presidential tweets, market volatility and algorithms (roughly the 2 minute mark), August 2019.
    Knowledge@Wharton article on data privacy, anonymity, and re-identification, August 2019.
    WSJ article on Wall Street and academia, May 2019.
    Bloomberg article on machine learning at Morgan Stanley, April 2019.
    Fast Company article by Kartik Hosanagar on an algorithmic bill of rights, March 2019.
    Bloomberg article about shutdown of the legendary Prediction Company, September 2018.
    Bloomberg article about joining Morgan Stanley, June 2018.
    NYT article on the EU's GDPR, May 2018.
    Penn News article on fairness gerrymandering, February 2018.
    NPR Marketplace interview on algorithmic trading and market volatility, February 2018.
    "Data Skeptic" podcast with Kyle Polich on machine learning, computational complexity, game theory, trading, fairness etc. November 2017.
    WSJ article on financial markets counterterrorism. October 2017.
    Regulatory Review article on fairness in machine learning. October 2017.
    Axios article on "intimiate" data and machine learning, September 2017.
    Interview on Fairness in Machine Learning. Aired on Sirius XM Channel 111, Business Radio Powered by The Wharton School, August 2017.  
    Pasatiempo Magazine (Santa Fe New Mexican) article about SFI lecture on machine learning and social norms,   April 2017.
    CBS Sunday Morning segment on "Luck",   September 2016.
    Bloomberg news article on machine learning and macroeconomic policy, and a related radio segment on Bloomberg Surveillance,   June 2016.
    Some coverage of the article Private Algorithms for the Protected in Social Network Search in Quartz,   Pacific Standard,   Motherboard,   Naked Scientists,   Groks Science,   PBS Newshour,   and upenn.edu,   Jan-June 2016.
    MIT Technology Review article on Cloverpop, September 2014.
    Bloomberg News article on HFT and hybrid quant funds, March 2014
    Discussions of PAC and SQ learning and their relevance to evolution in Les Valiant's book "Probably Approximately Correct", June 2013
    NPR text and audio on Coursera, online education, and Penn, October 2012
    Australian radio program "Future Tense" on "The Algorithm", March 2012
    Chapter on biased voting experiments in Garth Sundem's book "Brain Trust", 2012.
    ScienceNews article on Princeton fish consensus experiments, December 2011.
    A profile of and an interview with Les Valiant upon his receiving the 2010 Turing Award, CACM June 2011.
    Profile and lecture overview, Christ's College Pieces, Lent Term 2011.
    Fiscal Times article on machine learning and technology in trading, March 2011,
    Wired Magazine article on algorithmic trading, January 2011, and some more extensive remarks and one-year follow-up on the author's blog.
    Science News article on light speed propagation delays in trading, October 2010
    Economist article on flash crash autopsy, October 2010
    WSJ online post on HFT research, September 2010
    Discussion of behavioral social network experiments in Peter Miller's "The Smart Swarm" (Chapter 3, page 139 forward)
    Atlantic article on HFT "crop circles", August 2010
    Nature News article on "distributed thinking", August 2010
    Wall Street Journal article on machine learning in quant trading, July 2010 and a related interview on CNBC
    New Scientist article on "Why Facebook friends are worth keeping", July 2010; here is a free reproduction
    Philadelphia Business Journal article on the MKSE program and Networked Life, October 2009
    Discussion of behavioral social network experiments in Christakis and Fowler's "Connected" (page 165 foward)
    Philadelphia Inquirer article on networked voting experiments, March 2009
    Science Daily article on networked voting experiments, February 2009
    The Trade magazine article natural language processing for algorithmic trading, September 2007
    Bloomberg Markets magazine article on AI on Wall Street, June 2007
    SIAM News article on behavioral graph coloring, November 2006
    Philadelphia Inquirer article on network science and NSA link analysis, May 2006
    Chicago Tribune article on privacy in blogs and social networks, November 2005
    Chronicle of Higher Education article on Facebook and social networks, May 2004
    Star-Ledger article on the demise of AT&T Labs, March 2004
    Business Week Online article on technology in NASDAQ and NYSE, September 2003
    Philadelphia Inquirer article on ISTAR, interdependent security, and games on networks, January 2003
    Washington Post article on web-based chatterbots, September 2002
    New Scientist article on the Cobot spoken dialogue system, August 2002
    Tornado Insider article on DDoS attacks, January 2002 [Cover]
    Tornado Insider article on biometric security, January 2002
    Audio of COMNET panel "Staving Off Denial-of-Service Attacks and Detecting Malicious Code"
    Tornado Insider article on natural language technology, September 2001
    Tornado Insider article on robotics, July 2001
    Il Sole 24 Ore profile, June 2001 [English Translation]
    Corriere Della Sera profile, May 2001 [English Translation]
    Associated Press article on software robots, February 2001
    New York Times article on TAC, August 2000
    New York Times on Cobot, February 2000
    TIME Digital Magazine (now Time On) on Cobot, May 2000
    Washington Post article on Cobot, December 2000
    New York Times article on boosting, August 1999



  • The Ethical Algorithm: The Science of Socially Aware Algorithm Design. The Ethical Algorithm is jointly authored with Aaron Roth, and is a general-audience book about the science of designing algorithms that embed social values like privacy and fairness.

  • An Introduction to Computational Learning Theory. Jointly authored with Umesh Vazirani of U.C. Berkeley, this MIT Press publication is intended to be an intuitive but precise treatment of some interesting and fundamental topics in computational learning theory. The level is appropriate for graduate students and researchers in machine learning, artificial intelligence, neural networks, and theoretical computer science. The link above is to the MIT Press page that provides a brief description of the book and ordering information. If you have the book and have any questions or comments, please click here to send me mail.
  • The Computational Complexity of Machine Learning. This revision of my doctoral dissertation was published by the MIT Press as part of the ACM Doctoral Dissertation Award Series. As it is now out of print, I am making it available for downloading below.


    What follows is a listing of (almost) all of my research papers in (approximately) reverse chronological order. For papers with both a conference and journal version, the paper is usually placed by its first (conference) date. Also, as per the honorable tradition of the theoretical computer science community, on almost all of the papers below that are primarily mathematical in content, authors are listed alphabetically.

    Acronyms for conferences and journals include: AAAI: Annual National Conference on Artificial Intelligence; AIES: AAAI/ACM Conference on Artificial Intelligence, Ethics and Society; AISTATS: International Conference on Artificial Intelligence and Statistics; ALT: Algorithmic Learning Theory; COLT: Annual Conference on Computational Learning Theory; EC: ACM Conference on Economics and Computation; FAccT: ACM Conference on Fairness, Accountability and Transparency (formerly FAT* and FATML); FOCS: IEEE Foundations of Computer Science; HCOMP: AAAI Conference on Human Computation and Crowdsourcing; ICCV: International Conference on Computer Vision; ICML: International Conference on Machine Learning; IJCAI: International Joint Conference on Artificial Intelligence; ITCS: Innovations in Theoretical Computer Science; NIPS/NeurIPS: Neural Information Processing Systems; PNAS: Proceedings of the National Academy of Sciences; SaTML: IEEE Conference on Secure and Trustworthy Machine Learning; SODA: ACM Symposium on Discrete Algorithms; STOC: ACM Symposium on the Theory of Computation; UAI: Annual Conference on Uncertainty in Artificial Intelligence; WINE: Workshop on Internet and Network Economics.

    In addition to the list below, you can also look at my page on Google Scholar, and this DBLP query seems to do a pretty good job of finding those publications that appeared in mainstream CS venues (though not others), and can be useful for generating bibtex citations.

    Last Modified: May 24, 2024.