Johan Ugander

Assistant Professor, Management Science & Engineering (MS&E)
David Morgenthaler II Faculty Fellow
School of Engineering
Stanford University

My research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale data-rich contexts. I am particularly interested in the challenges of causal inference and experimentation in these complex domains. My work commonly falls at the intersections of machine learning, probability theory, statistics, optimization, graph theory, and algorithm design.

Within MS&E I am a member of the Social Algorithms Lab (SOAL). I am also among the faculty co-directors of the RAIN Seminar. At Stanford I am also affiliated with the Institute for Computational & Mathematical Engineering (ICME) and the Center for Computational Social Science.

I obtained my Ph.D. in Applied Mathematics from Cornell University in 2014, advised by Jon Kleinberg. I also hold degrees from the University of Cambridge and Lund University; before that I attended Deep Springs College. From 2010-14 I held an affiliation with the Facebook Data Science team. In 2014-15 I spent one year as a post-doctoral researcher at Microsoft Research, hosted by Eric Horvitz. I joined the Stanford faculty in September 2015.
[third person bio] [serious photo]

Contact me: jugander {at} stanford.edu
Visiting address: Huang Engineering Center 357, 475 Via Ortega, Stanford, CA 94305-4121
See also: twitter, medium, research blog

News

• July 12-13, 2018: Co-chairing the SIAM Workshop on Network Science, Portland, OR.
• June 2018: New paper at EC (with Imanol Arrieta-Ibarra): A Personalized BDM Mechanism for Efficient Market Intervention Experiments.
• June 2018: Scientific American story about recent work, Friends of Friends Can Reveal Hidden Information about a Person.
• Spring 2018: Teaching MS&E135: Networks at the undergraduate level and MS&E234: Data Privacy and Ethics at the masters level.
• March 26, 2018: Talk at CMU, Heinz College.
• March 2018: New paper in Nature Human Behaviour (with Kristen Altenburger): Monophily in social networks introduces similarity among friends-of-friends.
• February 6, 2018: Talk at Northwestern, SONIC group.
• January 11-12, 2018: Talk at JMM, San Diego, AMS Special Session on Network Science.
• Fall 2017: Teaching MS&E334: Topics in Social Data aimed at Ph.D. students.
• November 20-21, 2017: Talk at UMich, Complex Systems.
• October 2017: Visiting Boston for CODE@MIT.
• June 2017: New paper at EC (with Jon Kleinberg and Sendhil Mullainathan): Comparison-Based Choices.
• May 2017: Invited talk at GraphEx 2017 in Boston.
• May 2017: Invited keynote at the Observational Studies through Social Media (OSSM) 2017 Workshop in Montreal.
• April 2017: Awarded NSF CRII grant, information here. Thanks NSF!
• February 2017: Daughter Matilda born!
• January 2017: New paper in PNAS (with Isabel Kloumann and Jon Kleinberg): Block models and personalized PageRank.
• December 2016: New paper at NIPS (with Stephen Ragain): Pairwise Choice Markov Chains.

Ph.D. students

• Kristen Altenburger, MS&E
• Imanol Arrieta-Ibarra, MS&E
• Alex Chin, Statistics
• Stephen Ragain, MS&E

Publications

See also my Google Scholar profile.

• I Arrieta-Ibarra, J Ugander
  A Personalized BDM Mechanism for Efficient Market Intervention Experiments
  Proc. 19th ACM Conf. on Economics and Computation (EC), 2018.
  [talk slides, EC by Imanol Arrieta-Ibarra]
  
  
• KM Altenburger, J Ugander
  Monophily in social networks introduces similarity among friends-of-friends
  Nature Human Behaviour 2:284–290, 2018.
  [Supplementary Information] [NHB page] [arXiv pre-print] [code]
  
  
• B Fosdick, D Larremore, J Nishimura, J Ugander
  Configuring Random Graph Models with Fixed Degree Sequences
  SIAM Review 60(2):315–355, 2018.
  [talk slides, NetSci17 by Dan Larremore] [arXiv pre-print] [code]
  
  
• J Kleinberg, S Mullainathan, J Ugander
  Comparison-Based Choices
  Proc. 18th ACM Conf. on Economics and Computation (EC), 2017.
  [talk slides, EC] [talk video, EC]
  
  
• D Eckles, B Karrer, J Ugander
  Design and analysis of experiments in networks: Reducing bias from interference
  Journal of Causal Inference 5(1):1-23, 2017.
  [arXiv pre-print]
  
  
• I Kloumann, J Ugander, J Kleinberg
  Block models and personalized PageRank
  Proceedings of the National Academy of Sciences (PNAS) 114(1):33-38, 2017.
  [talk slides, Google Research] [arXiv pre-print]
  
  
• S Ragain, J Ugander
  Pairwise Choice Markov Chains
  Advances in Neural Information Processing Systems (NIPS) 29, 2016.
  [talk slides, CODE@MIT17] [Code and data]
  
  
• J Ugander, R Drapeau, C Guestrin
  The Wisdom of Multiple Guesses
  Proc. 16th ACM Conf. on Economics and Computation (EC), 2015.
  [talk slides, EC] [Code and data]
  
  
• AZ Jacobs, SF Way, J Ugander, A Clauset
  Assembling thefacebook: Using Heterogeneity to Understand Online Social Network Assembly
  Proc. 7th ACM Int'l Conf. on Web Science (WebSci), 2015.
  [talk slides, ICCSS16 by Abigail Jacobs] [Supplementary data]
  
  
• J Ugander, B Karrer, L Backstrom, J Kleinberg
  Graph Cluster Randomization: Network Exposure to Multiple Universes
  Proc. 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2013.
  [talk video, KDD]
  
  
• J Nishimura, J Ugander
  Restreaming Graph Partitioning: Simple Versatile Algorithms for Advanced Balancing
  Proc. 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2013.
  [Cython implementation by Justin Vincent]
  
  
• J Ugander, L Backstrom, J Kleinberg
  Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections
  Proc. 22nd Int'l World Wide Web Conf. (WWW), 2013.
  [talk slides, WWW] [Summary and R code]
  
  
• DM Romero, C Tan, and J Ugander
  On the Interplay Between Social and Topical Structure
  Proc. 7th AAAI Int'l Conf. on Weblogs and Social Media (ICWSM), 2013.
  [talk slides, ICWSM by Chenhao Tan]
  
  
• J Ugander, L Backstrom
  Balanced Label Propagation for Partitioning Massive Graphs
  Proc. 6th ACM Int'l Conf. on Web Search and Data Mining (WSDM), 2013.
  (Best Student Paper Award)
  [talk slides, WSDM]
  
  
• J Ugander, L Backstrom, C Marlow, J Kleinberg
  Structural Diversity in Social Contagion
  Proceedings of the National Academy of Sciences (PNAS), 109(16) 5962-5966, 17 April 2012.
  [talk slides, NetSci]
  
  
• J Ugander, B Karrer, L Backstrom, C Marlow
  The Anatomy of the Facebook Social Graph.
  arXiv, 2011.
  
  
• L Backstrom, P Boldi, M Rosa, J Ugander, S Vigna
  Four Degrees of Separation
  Proc. 4th ACM Int'l Conf. on Web Science (WebSci), 2012.
  (Best Paper Award)
  [HyperANF metadata and degree distributions]
  
  
• M Larsson, J Ugander
  A Concave Regularization Technique for Sparse Mixture Models
  Advances in Neural Information Processing Systems (NIPS) 24, 2011.
  [NIPS poster]
  
  
• J Ugander, MJ Dunlop, RM Murray
  Analysis of a Digital Clock for Molecular Computing
  Proc. 2007 American Control Conference (ACC), New York, July 2007. p. 1595-1599.

Theses

• Computational Perspectives on Large-Scale Social Networks
  Ph.D. Thesis, Center for Applied Mathematics, Cornell University, June 2014.
  
  
• Efficiency and Fragility in Loss Networks
  Part III Essay, University of Cambridge, May 2009.
  
  
• Delay-dependent Stability of Genetic Regulatory Networks
  M.Sc. Thesis, Department of Automatic Control, Lund University, July 2008.

Teaching

• MS&E 135: Networks (Spring 2018, Winter 2017, Spring 2016)
  This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".
  
  
• MS&E 334: Topics in Social Data (Fall 2017)
  Previous versions: Fall 2016, Fall 2015
  This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. There will be a particular focus on recent developments in discrete choice theory and preference learning. Connections will be made to graph-theoretic investigations common in the study of social networks. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students with an interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.

Activities

I have organized or co-chaired the following workshops:

• 2018: SIAM Workshop on Network Science
• 2016: IPAM Cultural Analytics Workshop on Cultural Patterns
• 2016: NIPS Workshop on Networks in the Social and Information Sciences
• 2015: JMM Special Session on Network Science
• 2014: NIPS Workshop on Networks: From Graphs to Rich Data

I am serving/have served on the Program Committee of the following conferences/workshops:

• 2019: ACM WSDM
• 2018: ACM EC, ACM WSDM, WWW, SIAM NS (chair)
• 2017: ACM WSDM, WWW (senior PC), NIPS (reviewer)
• 2016: ACM EC, ICCSS, ACM KDD, SIAM NS, AAAI IJCAI, AAAI ICWSM (senior PC), WWW, SIAM SDM
• 2015: ACM EC, ICCSS, ACM KDD, WWW, SIAM SDM
• 2014: SocInfo, ACM CIKM, AAAI ICWSM, WWW
• 2013: WebSci

I have also served as a reviewer for the following journals:

• Proceedings of the National Academy of Sciences; Science; SIAM Review; Network Science (CUP); Journal of Complex Networks (Oxford); Journal of the American Statistical Association; Annals of Applied Statistics (IMS); Management Science (INFORMS); Transactions on Network Science & Engineering (IEEE); Social Network Analysis and Mining (Springer); Scientific Reports (NPG); PLOS One; Physical Review X.

Selected press coverage

• Scientific American, June 2018: Friends of Friends Can Reveal Hidden Information about a Person
• BBC Radio 4: Digital Human, May 2016: Lost and Found
• MIT Technology Review (blog), April 2015: Network Archaeologists Discover Two Types of Social Network Growth
• Wall Street Journal (blog), June 2014: Studying Your Users: Facebook's Greatest Hits
• Facebook Engineering Blog, April 2014: Large-scale graph partitioning with Apache Giraph
• Wired (blog), April 2013: Exploring the Space of Human Interaction
• SmartPlanet, October 2012: Q&A: Why you have fewer friends than your friends on Facebook
• NY Times Opinionator, September 2012: Friends You Can Count On
• Nature, August 2012: Computational Social Science: Making the Links
• American Mathematical Society, July 2012: SIAM Annual Meeting 2012 Highlights
• Science Now, April 2012: How Facebook "Contagion" Spreads
• New Scientist, April 2012: Variety, Not Viral Spread, is Key to Facebook Growth
• The Economist, April 2012: Social Contagion: Conflicting Ideas
• The Economist Daily Chart, March 2012: The Sun Never Sets
• The Telegraph, March 2012: Facebook: British Empire Still Shapes Friendship Patterns
• NPR (on-air interview), November 2011: 4.74 Degrees of Separation
• Wired, Novemeber 2011: Facebook Study: It's a Small(er) World After All
• TechCrunch, Novemeber 2011: 4.74 - Facebook Wins By Getting Us Closer Than Six Degrees
• NY Times, November 2011: Between You and Me? 4.74 Degrees

Bookmarklets

• scholarfy: a bookmarklet I wrote to transfer search queries to Google Scholar.
• JSTORpdf: a bookmarklet I wrote to access PDFs faster on JSTOR.
• googURL: a bookmarklet I wrote to circumvent some paywalls using Google.

Climbing

My wife and I spend a lot of our free time climbing. Sometimes we write trip reports.

First ascents:

• West Maiden, Southwest Face And Other Climbs, Arrigetch Peaks, Brooks Range, Alaska, American Alpine Journal, 2016.

Other trip reports:

• Ingalls Peak Traverse, Cascade Range, Washington, October 2014.
• Backbone Ridge, Dragontail Peak, Cascade Range, Washington, June 2014.
• Rio de Janeiro, Brazil, May 2013.

I also enjoy trail running. Very occasionally I'll run competitively.

Misc

• National Weather Service probabilistic snow forecasts, also known as "Where is it dumping powder?"