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Member, Harvard Science Task Force Committee, Spring ’15- 2017. Member, Advisory board of Institute for Applied Computational Science, HarvardUniversity, 2015-present Co-chair, FAS/SEAS Committee on Allston and the School of Engineering and AppliedSciences, Fall’14 - Summer’20 Co-chair, Provost’s Task Force on Transportation for Allston Campus, Spring ’13-2017. Member, SEAS Computational Science and Engineering Program Committee, Spring ’13-’21 Member, Computer Science Committee on Undergraduate Studies, Fall ’03 - present Member, Harvard Academic Deans Council, 2014-2017 Co-lead, Proposal for new FAS Data Science Masters, 2015-17 Speaker, Harvard College Class of 1951 on their upcoming 65th Reunion, May 2016. Co-chair, FAS/SEAS Future of Libraries in Allston Committee, Spring’15-Spring’16 Moderator of Panel on Engineering Entrepreneurship: Making Robotics Fly, HBS,Hubweek October 2015. Speaker, FAS development, September 2015. Member, SEAS Steering Committee, August 2013-July 2017 Member, FAS Search Advisory Committee to Select the Dean of the School of Engineeringand Applied Sciences, Fall ’14 - Spring ’15. Member, SEAS Library Advisory Committee, Spring ’13. Speaker, FAS New York Campaign Steering Committee Meeting, December 2013. Member, SEAS Allston Summer ’13 Committee, Summer 2013. Member, FAS Sabbatical Policy Committee, Fall ’10. Speaker, FAS New York Major Gifts Committee, November ’10. Co-Chair, SEAS Strategic Committee on Applied Mathematics and Computation, Fall ’09Spring’10 Co-chair Information, Technology and Management program review committee, Spring 2007. Co-Chair, SEAS Committee on the Transition from ITM to STM, Spring ’07. Member, FAS Screening Committee, Fall ’05- Spring ’07. Member, FAS Herchel Smith Selection Committee, Spring ’06. Member, DEAS Junior Faculty Committee on the Future of DEAS, Spring ’06. Member, Computer Science Faculty Search Committee, multiple years6

Member, Applied Mathematics Committee on Undergraduate Studies, Fall ’01- Spring ’02,Spring ’13. Member, DEAS Electronic Commerce Search Committee, Fall ’04- Spring ’05. Member, Subcommittee on the Degree of Doctor of Philosophy in Information, Technologyand Management, Fall ’02- 2009. Organizer, Computer Science Colloquium Series, Fall ’02- Spring ’08. Member, SEAS Graduate Admissions Committee, Fall ’01- Spring ’05; SEAS Admissionsand Scholarship Committee, Fall ’07- Spring ’08, Fall ’10- Spring ’11.Funding DARPA, Mechanism Design for Resource Coordination in Dynamic, Multi-Actor Worlds,’19-’22, 1,988,701 Gift funding, Applied cryptography and society, 2,500,000, 2019- present IARPA, Hybrid Forecast Competition (HRL subcontract), ’17-’18, 712,000 Tata comm., Deep learning for econometrics, ’18-’22, 1,169,943 National Center for Women and Information Technology, Girls Who Code, ’17-’18, 3,000 FAS Dean’s Competitive Fund for Promising Scholarship, The Design of CooperativeSociety-Driven Systems, 11/2016 - 5/2017, 20,000 Future of Life Institute Fund, Mechanism Design for Multiple AIs, 8/2015-7/2018, 200,000 Google Award, Incentive-aligned Information Elicitation, 2015 - 2017, 294,377 Co-PI, NIH Statistical and Quantitative Training in Big Data Health Science, ’16-’21, 1.4m NSF AF-1301976 Algorithmic Crowdsourcing Systems, ’13-’18, 999,977 Indo-US Joint Center on Advanced Research in Machine Learning, Game theory andOptimization, Indo-US Science and Technology Forum, ’12-’15, 133,000 NSF CCF-1101570 Heuristic Mechanism Design, ’11-’14, 360,000 Yahoo! Faculty Research Grant, ’09-’10, 25,000 Network Science CTA Grant (BBN/Army Research), ’10-’13, 374,000 Microsoft Research Award for Work on Computational Environment Design, June 2009 15,000 NSF CCF-0915016 Incentive-Compatible Machine Learning, ’09-’12, 500,000 Yahoo! Faculty Research Grant, ’07- ’08, 25,000 Microsoft Research Award, ’08- ’09, 117,000 Department of Defense FA 8721-05-C-0003 (subcontract with CMU) ’09-’10, 75,0007

Department of Defense FA 8721-05-C-0003 (subcontract with CMU)’08-’09, 75,000 Alfred P. Sloan Research Fellowship, ’05- ’07, 45,000 NSF DMS-0631636 Model-Based Unsupervised Learning for Robust Indentification ofPreferences and Behavior in Network Economies, ’06- ’09, 300,000 NSF IIS-0534620, Distributed Implementation: Collaborative Decision Making inMulti-Agent Systems, ’05- ’07, 168,000. NSF Career Award IIS-0238147, Mechanism Design for Resource Bounded Agents:Indirect-Revelation and Strategic Approximations, ’03- ’08, 599,000.REU Award (Summer ’03, ’05) 24,000. Federal Aviation Administration Award DTF A0101C00031, Slot Auctions for US Airports,’04- ’05, 120,000. IBM Faculty Partnership Award, Decentralized Allocation and Autonomic Computing,’03- ’04, 40,000 IBM Faculty Partnership Award, Multi-attribute Auction Design, ’02- ’03, 40,000 NASA Ames Research Award, Collective Intelligence, ’02- ’03, 40,000TeachingCS 136: Economics and ComputationFall ’11, ’12, Spring ’13-’16, Fall ’17, ’18, ‘19, ’21 New undergraduate course Enrollment: 10, 43, 26, 49, 53, 53, 54, 66, 95, 109 CUE overall course ratings (5.0 scale): 4.67, 3.87, 4.4, 4.4, 4.6, 4.5, 4.5, 4.6, 4.1 CUE overall instructor ratings (5.0 scale): 4.78, 4.41, 4.6, 4.8, 4.8, 4.7, 4.7, 4.8, 4.7 Also offered as E-CSCI 186 in some years (enrollment 9, 6, 3, 5)CS 91r: Topics in Economics and ComputationSpring 2022 Small course, offered as a continuation of CS 136Fall 18-presentData-driven marketing Harvard Business Analytics Program, co-taught quarterly with Sunil Gupta, Ayelet Israeli,and Eva AscarzaCS 290: PhD Grad Cohort Research Seminar New course, taught jointly with John Girash and Yaniv Yacoby Enrollment: 27 CUE overall course ratings (5.0 scale): 4.6 (fall), 4.9 (spring) CUE overall instructor ratings (5.0 scale): 4.9 (fall), 4.8 (spring)8Fall 2021, Spring 2022

Spring 2018 and Spring 2022CS 282r: Topics in Machine Learning Helped to administer course offered by Google researchersJanuary 2019Artificial intelligence Two sessions, Short intensive program, HBS (January 2019) Two sessions, Executive education, HBS (June 2019)Spring ’17, ’21CS 181: Machine Learning Undergraduate course, co-taught with Sasha Rush (’17) and Finale Doshi-Velez (’21) Enrollment: 217 CUE overall course ratings (5.0 scale): 3.6 CUE overall instructor ratings (5.0 scale): 4.1CS 182: Intell. Machines: Perception, Learning and Uncertainty Spring ’10, ’11 Undergraduate course Enrollment: 42, 45 CUE overall course ratings (5.0 scale): 4.33, 4.33 CUE overall instructor ratings (5.0 scale): 4.56, 4.56 Also offered as E-CSCI 181 (enrollment 14, 10)CIS 700: Computational Mechanism DesignFall ’08 Graduate course at EPFL Enrollment: 10Spring ’09, Fall ’10CS 285: Multi-Agent Systems Graduate course Enrollment: 20 CUE overall course ratings (5.0 scale): 4.2 CUE overall instructor ratings (5.0 scale): 4.6CS 182: Intelligent Machines: Reasoning, Actions and Plans Fall ’02-’05, Fall ’07 Undergraduate course Enrollments: 44, 36, 32, 25, 26 CUE overall course ratings (5.0 scale): 4.0, 4.1, 3.8, 4.1, 4.2 CUE overall instructor ratings (5.0 scale): 4.2, 4.4, 4.3, 4.0, 4.4CS 286r: Topics at the Interface between CS and EconomicsSpring ’02-’07, Fall ’09 New graduate course, rotating topics Computational Mechanism DesignSpring ’02, ’05, ’079

Enrollments: 29, 24, 14 CUE overall course ratings (5.0 scale): 4.4, 4.7, 4.6 CUE overall instructor ratings (5.0 scale): 4.8, 4.8, 5.0 Electronic Market Design Enrollment: 32 CUE overall course rating (5.0 scale): 4.5 CUE overall instructor rating (5.0 scale): 4.7Spring ’03 Iterative Combinatorial Exchanges Enrollment: 24 CUE overall course rating (5.0 scale): 4.1 CUE overall instructor rating (5.0 scale): 4.8Spring ’04 Multi-Agent Learning and Implementation Enrollment: 24 CUE overall course rating (5.0 scale): 4.7 CUE overall instructor rating (5.0 scale): 4.8Spring ’06 Assignment, Matching and Dynamics Enrollment: 26 CUE overall course rating (5.0 scale): 4.7 CUE overall instructor rating (5.0 scale): 4.9Fall ’09AM 121: Intro to Optimization: Models and MethodsSpring ’08, Fall ’14, ’16 New undergraduate course Enrollment: 37, 75, 61 CUE overall course rating (5.0 scale): 4.2, 4.1 CUE overall instructor rating (5.0 scale): 4.5, 4.3FS 26n: Electronic Transactions: Economic and Comput. ThinkingFall ’06 Freshman seminar Enrollment: 10 CUE overall course rating (5.0 scale): 4.6 CUE overall instructor rating (5.0 scale): 5.0OPIM 101: Intro. to the Computer as a Decision Analysis ToolSpring ’01 Co-lecturer, The Wharton School, University of Pennsylvania. Required freshman course for all business concentrators Enrollment 360 (4 sections)Guest Lectures (Teaching) Deep learning for economic design, Simplicity and Complexity in Economics, StanfordUniversity, April 202210

Differentiable Economics, Keynote speaker, City University of Hong Kong Summer School(by Zoom), July 2021 Deep learning for economic design, Simplicity and Complexity in Economics, StanfordUniversity, April 2020 Deep learning for economic design, EC 2099: Market Design, Harvard University,November’17, November’18. Economic Reasoning and Artificial Intelligence, CS 108: Intelligent Systems: Design andEthical Challenges, Harvard University, November’16. Dark pools and trust without transparency, EC 2099: Market Design, Harvard University,November ’15. Combinatorial Exchanges, ECON 1465 Market Design, Brown University, October ’10 Mechanism Design for the Assignment Problem, AM 50 Introduction to AppliedMathematics, Harvard University, March ’09. Adaptive Online Mechanism Design for Sequential Environments, CS 590A ResearchSeminar in Artificial Intelligence, University of Washington, May ’06. ICE: An Iterative Combinatorial Exchange, EC 2056 Market Design, Harvard University,April ’06. Mechanism Design for Dynamic Environments, EC 2149 Computational Economics,Harvard University, Nov ’05. Mechanism Design for Dynamic Environments, CS 15-892 Foundations of ElectronicMarketplaces, Carnegie Mellon University, Nov ’05. Distributed Artificial Intelligence: Self-Interested Agents, CS 50 Introduction to ComputerScience, Harvard University, Dec ’03. Auction Design with Costly Preference Elicitation, EC 2056 Market Design, HarvardUniversity, March ’03. Distributed Artificial Intelligence: Self-Interested Agents, CS 50 Introduction to ComputerScience, Harvard University, Dec ’02.Distinguished Lecturer Series[1] Deep Learning for Economic Discovery. BEMACS lecture, Bocconi University, October 2021.[2] Machine learning for mechanism design. Distinguished Israel Pollak Lecture, The Technion,Haifa Israel, April 2018.[3] Robust Methods to Elicit Informative Feedback. Center for Info. Technology PolicyDistinguished Lecturer, Princeton University, Princeton NJ, May 2017.[4] Incentive Engineering: Getting to the right inputs. William Mong Distinguished Lecture,Engineering faculty, University of Hong Kong, July 2016.11

[5] Strategic Behavior in Coordination Platforms. Distinguished Lecture, EECS department,Vanderbilt University, March 2015.[6] Computational Environment Design for Online Communities. Invited distinguished speaker,Research center for Symbiotic computing, Nagoya Inst. of Technology, December 2014.[7] Mechanism Design as a Classification Problem. Distinguished Speaker Series, AlgorithmicEconomics Seminar, Computer Science Department, Carnegie Mellon University, PittsburghPA, November 2012.[8] Computational Environment Design for Online Communities. Distinguished Lecturer Series,Lady Margaret Lecture, Christ’s College, University of Cambridge, Cambridge, England, May2012.[9] Incentive Mechanism Engineering in the Internet Age. Distinguished Lecturer Series,Computer Science and Automation, Indian Institute of Sciences, Bangalore, India, November2010.[10] Incentive Mechanism Engineering in the Internet Age. Distinguished Lecturer Series,Triangle Computer Science, Duke University, Durham, North Carolina, September 2010.[11] Incentive Mechanism Engineering in the Internet Age. Distinguished Lecture Series,University of British Columbia, Canada, March 2010.Invited Talks and Panel Participation at Conferences[1] New Challenges, New Tools and New Objectives for Market Design. Panel, EuropeanEconomic Association and the Econometric Society, Cologne, Germany, August 2018.[2] Deep learning for market design. Plenary speaker, Annual Meeting of the GermanEconomic Association, Freiburg, Germany, September 2018.[3] Deep learning for market design. Plenary speaker, Kick-off Symposium of the AI researchcenter, Nagoya Institute of Technology, Nagoya Japan, May 2018.[4] Data science challenges. Young Presidents’ Organization, New York City, July 2018.[5] Spatial-Temporal Pricing (and Coordination). Uber Marketplace Optimization Data ScienceSymposium, San Francisco CA, March 2017.[6] On AI, Markets and Machine Learning. Plenary speaker, Sixteenth InternationalConference on Antonomous Agents and Multiagent Sytems (AAMAS’17), Sao Paolo Brazil,May 2017.[7] Life in 2030: How AI Will Transform Work, Life, and Play. Plenary speaker, AmericanAssociation for the Advancement of Science session on Artificial Intelligence, People, andSociety, organized by the Royal Society, February 2017.[8] How to elicit information when it is not possible to verify the answer. Plenary speaker,Collective Intelligence 2016, New York, June 2016.12

[9] Mechanism Design through Statistical Machine Learning: Part II (Social choice andmatching). Plenary speaker, 41th conference on The mathematics of operations research,Lunteren, The Netherlands, January 2016.[10] Mechanism Design through Statistical Machine Learning: Part I (Auctions). Plenaryspeaker, 41th conference on The mathematics of operations research, Lunteren, TheNetherlands, May 2016.[11] The Tyranny of Algorithms? Panel, MIT Conference on Digital Experimentation,Cambridge MA, October 2016.[12] Panelist: Preparing for the Future of Artificial Intelligence. John F. Kennedy Jr. Forum,Kennedy School of Government, Cambridge MA,, November 2016.[13] The design of incentive mechanisms through statistical machine learning. Plenary speaker,Optimization Days 2016 conference, HEC Montreal, Canada, May 2016.[14] Trust without Disclosure: Dark Pools and Secrecy-Preserving Proofs. Plenary speaker, 3rdConference on Auctions, Market Mechanisms and Their Applications (AMMA), Chicago,August 2015.[15] Payment rules through disciminant-based classifiers. Indo-US Symposium on New Directionsin ML, Game Theory and Optimization, Bangalore, India, January 2014.[16] Flexible Parametric Ranking models. Indo-US Symposium on New Directions in ML, GameTheory and Optimization, Bangalore, India, January 2014.[17] Peer Prediction. Plenary speaker, Microsoft Research, Machine Learning Summit, Paris,France, April 2013.[18] Engineering Coordinated Behavior Across Socio-Economic Systems. Plenary speaker, 94thAnnual Conference of Information Processing Society of Japan, Nagoya, Japan, March 2012.[19] Learning Payment rules through Discriminant-Based Classifiers. Technion-MicrosoftElectronic Commerce Day, The Technion, Haifa, Israel, May 2012.[20] Designing Corruption Proof Procurement Auctions. Conference on Combating Corruption inPublic Procurement, Rome, Italy, July 2012.[21] Approximate Incentive Compatibility in Combinatorial Exchanges. 9th Annual InternationalIndustrial Organization Conference, Boston, MA, April 2011.[22] Payment Rules for Combinatorial Auctions via Structural Support Vector Machines.Plenary speaker, 4th Annual New York Computer Science and Economics Day (NYCE’11), New York NY, September 2011.[23] Promoting Sustainability: Exploring the Role of Expensive, Indirect, and Hidden Markets.2nd International Conference on Computational Sustainability (CompSust10) Cambridge,MA, June 2010.[24] The Interplay of Machine Learning and Mechanism Design. Plenary speaker, NeuralInformation Processing Systems Foundation (NIPS ’10), Vancouver, B.C., Canada,December 2010.13

[25] Incentive Engineering in the Internet Age. Plenary speaker, The Twenty-Fourth AAAIConference on Artificial Intelligence (AAAI ’10), Atlanta, GA, July 2010.[26] When Analysis Fails: Heuristic Mechanism Design via Self-Correcting Procedures. Plenaryspeaker, 35th International Conference on Current Trends in Theory and Practice ofComputer Science, (SOFSEM ’09), Špindlerův, Mlýn, Czech Republic, January 2009.[27] Panel: AAAI Study on Long-Term AI Futures. In 21st Int. Joint Conference on ArtificialIntelligence IJCAI’09, Pasadena Conference Center, Pasadena, CA, July 2009.[28] Self-Correcting Sampling-Based Dynamic Multi-Unit Auctions. Conference on EconomicDesign 2009, Maastricht, The Netherlands, June 2009.[29] Dynamic mechanisms for Distributed Coordination: Models and Methods. Semi-plenaryspeaker, The Third World Congress of the Game Theory Society (GAMES 2008), ChicagoIL, July 2008.[30] Computational Ironing to Achieve Monotonicity in Dynamic Mechanisms. PlenarySpeaker, The 18th International Conference on Game Theory, Stonybrook NY, July 2007.[31] Computational Mechanism Design: An AI Agenda. Plenary Speaker, The 17thBelgian-Dutch Conference on Artificial Intelligence, Brussels, Belgium, October 2005.[32] Panel: Spectrum Auctions with Package Bidding. In 31st Annual Research Conference onCommunication, Information, and Internet Policy (TPRC’03), George Mason UniversityLaw School, Arlington, VA, September 2003.[33] Computational Mechanism Design: Taming the Strategic Dragon Without Invoking theComplexity Monster. Plenary Speaker, The 2nd International Joint Conference onAutonomous Agents and Multiagent Systems (AAMAS’03), Melbourne, Australia, July 2003.[34] Incremental Revelation in Computational Mechanisms. American Association for theAdvancement of Science Annual Meeting, Devner CO, February 2003.[35] Towards Iterative Combinatorial Exchanges. 3rd FCC Conference on CombinatorialAuctions, Aspen Institute’s Wye River Conference Center, Queenstown MD, November 2003.[36] Panel: Feasible Auctions and Exchanges for FCC Spectrum Licenses. In 3rd FCCConference on Combinatorial Auctions, Aspen Institute’s Wye River Conference Center,Queenstown, MD, November 2003.[37] Panel: Agents and Electronic Commerce. In 2nd International Joint Conference onAutonomous Agents and Multiagent Systems (AAMAS’03), Melbourne, Australia, July 2003.[38] Computational Mechanism Design in the Supply Chain. Plenary Speaker, InternationalConference on Supply-Chain Management and Electronic Commerce, Beijing, China, August2002.[39] Panel: What is the Best Feasible Mechanism for Auctioning FCC Spectrum Licenses? In2nd FCC Conference on Combinatorial Auctions, Aspen Institute’s Wye River ConferenceCenter, Queenstown, MD, November 2001.[40] Combinatorial Exchanges. 2nd FCC Conference on Combinatorial Auctions, AspenInstitute’s Wye River Conference Center, Queenstown MD, October 2001.14

Invited Talks and Panel Participation at Workshops[1] Studying Algorithmic Economies Through a Reinforcement-Learning Lens. Frankfurt SchoolArtificial Intelligence and Business Analytics Workshop, Frankfurt, Germany, July 2022.[2] Differentiable Economics. Machine Learning for Algorithms Workshop, MIT, (on Zoom),July 2021.[3] Optimal Economic Design through Deep Learning. Microsoft Research New EnglandEconomics Workshop, Cambridge MA, June 2019.[4] Optimal Economic Design through Deep Learning. 14th SIGCOMM-ACMEC Workshop onthe Economics of Networks, Systems and Computation (NetEcon), Phoenix AZ, June 2019.[5] Optimal Economic Design through Deep Learning. WWW Workshop on the intersection ofmachine learning and mechanism design, San Francisco CA, May 2019.[6] Optimal Auction Design through Deep Learning. STOC Workshop on New Frontiers ofAutomated Mechanism Design for Pricing and Auctions, Phoenix AZ, June 2019.[7] Deep Learning for Multi-Facility Location Mechanism Design. ACMEC Workshop onOpinion Aggregation, Dynamics, and Elicitation (WADE), Ithaca, NY, June 2018.[8] Optimal Auctions through Deep Learning. Simons Institute “Economics and ComputationReunion Workshop”, Berkeley CA, April 2017.[9] Provably Trustworthy Dark Pools. Marketplace Innovation Workshop. NYU Stern, NewYork, NY, December 2016.[10] Plenary speaker: How to elicit information when it is not possible to verify the answer.IJCAI Algorithmic Game Theory Workshop, New York, NY, July 2016.[11] Panel: Regulatory Mechan

John A. Paulson School of Engineering and Applied Sciences, Harvard University, 150 Western Avenue, Boston, MA 02134, USA https://parkes.seas.harvard.edu/ August 2022 . Co-Director, Harvard Business Analytics Program, 9/17-present Boston, MA Harvard University Area Dean for Computer Science, 7/13-6/17 Cambridge, MA