Johan Ugander

Associate Professor
Statistics & Data Science
Faculty of Arts and Sciences
Yale University

I use statistical and computational methods to study social networks, human behavior, and their interplay. My work is partly empirical, working to advance our understanding of important social systems and processes, and partly methodological, developing tools to do so more efficiently and effectively. I frequently leverage the unique measurement opportunities created by the internet and digitization over the last two decades, making it possible to study social and individual behavior in previously unprecedented ways.

I joined the Yale faculty in July 2025. At Yale I am affiliated with the Institute for Foundations of Data Science (FDS). Prior to Yale, I was on the faculty at Stanford for ten years (2015 to 2025), as an Assistant Professor and (from June 2022) tenured Associated Professor, in the Department of Management Science & Engineering (MS&E).

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.

[third person bio] [serious photo]

Contact me: johan.ugander {at} yale.edu
Visiting address: Kline Tower, Room 1325, 219 Prospect Street, New Haven, CT 06511
See also: bsky, twitter, medium, research blog

Upcoming talks

• Fall 2025: Harvard Statistics (Oct 6)

News

• Fall 2025: Moved to Yale!
• Spring 2025: Talks at Columbia Statistics (Feb 3), MIT IDSS (Mar 3), Northeastern NetSI (Apr 4), MSR NYC (Jun 10), KTH (Jun 12).
• Fall 2024: Talks at Vermont Statistics (Oct 8), CODE@MIT (Oct 19-20), CornellTech (Oct 28), CMU MLD (Nov 19), Cornell CAM (Dec 6).
• Spring 2024: Teaching a PhD semiar, MS&E334: Topics in Social Data, with Ramesh Johari.
• Winter 2024: Teaching MS&E135: Networks at the undergraduate level.
• Fall 2023: Teaching MS&E231: Social Algorithms at the masters level.
• Fall 2023: Talks at National Academies of Science, Collective Intelligence/HCOMP, UC Berkeley Haas.

Ph.D. students and post-docs

• Ziv Epstein, Post-doc, co-advised w/ Bernstein

Former students/postdocs: Izabel Aguiar (PhD ICME, 2025), Amel Awadelkarim (PhD ICME, 2024), Serina Chang (PhD CS, 2024, co-advised w/ Leskovec), Samir Khan (PhD Statistics, 2024), Zhaonan Qu (Post-doc 2023-2024, co-advised w/ Imbens), Martin Saveski (Post-doc 2020-23), Kevin Han (PhD Statistics, 2023, co-advised w/ Imbens), Jenny Hong (PhD MS&E, 2023, co-advised w/ Manning), Jan Overgoor (PhD MS&E, 2021), Arjun Seshadri (PhD EE, 2021), Imanol Arrieta Ibarra (PhD MS&E, 2020), Kristen Altenburger (PhD MS&E, 2020), Stephen Ragain (PhD MS&E, 2019), Alex Chin (PhD Statistics, 2019).

Publications

See also my Google Scholar profile.

Pre-prints:

• K Tomlinson, J Ugander, J Kleinberg
  Exclusion Zones of Instant Runoff Voting
  arXiv:2502.16719, last updated March 2025.
  
  
• I Aguiar, P Chodrow, J Ugander
  The illusion of households as entities in social networks
  arXiv:2502.14764, last updated May 2025.
  
  
• I Slaughter, A Peytavin, J Ugander, M Saveski
  Community Notes Moderate Engagement With and Diffusion of False Information Online
  arXiv:2502.13322, last updated February 2025.
  
  
• M Eichhorn, S Khan, J Ugander, C Lee Yu
  Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
  arXiv:2405.07979, last updated July 2024.
  
  

Publications:

• Z Qu, A Galichon, W Gao, J Ugander
  On Sinkhorn's Algorithm and Choice Modeling
  To appear, Operations Research, 2025+.
  [code]
  
  
• D Liu, A Seshadri, T Eliassi-Rad, J Ugander
  Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings
  To appear, Proc 31st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2025.
  [code]
  
  
• K Tomlinson, T Namjoshi, J Ugander, J Kleinberg
  Replicating Electoral Success
  Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025.
  [code]
  
  
• J Ugander, Z Epstein
  The Art of Randomness: Sampling and Chance in the Age of Algorithmic Reproduction
  Harvard Data Science Review, 2024.
  
  
• A Awadelkarim, J Ugander
  Statistical Models of Top-k Partial Orders
  Proc. 30th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2024.
  [code]
  
  
• S Chang, F Koehler, Z Qu, J Leskovec, J Ugander
  Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
  International Conference on Machine Learning (ICML), 2024.
  [code]
  
  
• S Khan, M Saveski, J Ugander
  Off-policy evaluation beyond overlap: partial identification through smoothness
  International Conference on Machine Learning (ICML), 2024.
  [code]
  
  
• I Aguiar, D Taylor, J Ugander
  A tensor factorization model of multilayer network interdependence
  Journal of Machine Learning Research (JMLR), 2024.
  [code] [twitter thread] [arxiv]
  
  
• S Khan, J Ugander
  Doubly-robust and heteroscedasticity-aware sample trimming for causal inference
  Biometrika, 2024.
  [code] [twitter thread] [arxiv]
  
  
• I Aguiar, J Ugander
  The latent cognitive structures of social networks
  Network Science, 2024.
  [code] [arXiv]
  
  
• K Tomlinson, J Ugander, J Kleinberg
  The Moderating Effect of Instant Runoff Voting
  Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024.
  [code]
  
  
• M Saveski, S Jecmen, N Shah, J Ugander
  Counterfactual Evaluation of Peer-Review Assignment Policies
  Advances in Neural Information Processing Systems (NeurIPS), 2023.
  [code] [NeurIPS video]
  
  
• M Bernstein, A Christin, J Hancock, T Hashimoto, C Jia, M Lam, N Meister, N Persily, T Piccardi, M Saveski, J Tsai, J Ugander, C Xu
  Embedding Societal Values into Social Media Algorithms
  Journal of Online Trust and Safety, 2(1), 2023.
  
  
• K Han, J Ugander
  Model-Based Regression Adjustment with Model-Free Covariates for Network Interference
  Journal of Causal Inference, 2023.
  [arXiv]
  
  
• A Awadelkarim, A Seshadri, I Ashlagi, I Lo, J Ugander
  Rank-heterogeneous Preference Models for School Choice
  Proc. 29th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2023.
  [code] [KDD video] [twitter thread]
  
  
• K Tomlinson, J Ugander, J Kleinberg
  Ballot length in instant runoff voting
  Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
  [code] [twitter thread]
  
  
• S Chang, D Vrabac, J Leskovec, J Ugander
  Estimating geographic spillover effects of COVID-19 policies from large-scale mobility networks
  Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
  [code] [twitter thread]
  
  
• J Ugander, H Yin
  Randomized Graph Cluster Randomization
  Journal of Causal Inference, 2023.
  [code] [arXiv] [talk video, MIT IDE] [twitter thread]
  
  
• S Khan, J Ugander
  Adaptive normalization for IPW estimation
  Journal of Causal Inference, 2023.
  [code] [arXiv] [talk slides, UChicago] [twitter thread]
  
  
• C Musco, I Ramesh, J Ugander, RT Witter
  How to Quantify Polarization in Models of Opinion Dynamics
  KDD Workshop on Mining and Learning with Graphs (MLG), 2022.
  
  
• S Chang, J Ugander
  To Recommend or Not? A Model-Based Comparison of Item-Matching Processes
  Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2022.
  
  
• J Juul, J Ugander
  Comparing information diffusion mechanisms by matching on cascade size
  Proceedings of the National Academy of Sciences (PNAS), 118(46), 2021.
  [code] [Stanford News] [twitter thread]
  
  
• K Tomlinson, J Ugander, A Benson
  Choice Set Confounding in Discrete Choice
  Proc. 27th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2021.
  [code]
  
  
• K Altenburger, J Ugander
  Which Node Attribute Prediction Task Are We Solving? Within-Network, Across-Network, or Across-Layer Tasks
  Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2021.
  (Outstanding Problem-Solution Paper Award)
  [code]
  
  
• W Cai, J Ugander
  Experience-Driven Peer Effects: Evidence from a Large Natural Experiment
  Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2021.
  [code]
  
  
• A Chin, D Eckles, J Ugander
  Evaluating stochastic seeding strategies in networks
  Management Science, 2021.
  [arxiv pre-print] [code] [twitter thread]
  
  
• A Seshadri, S Ragain, J Ugander
  Learning Rich Rankings
  Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.
  [code] [twitter thread]
  
  
• J Overgoor, G Supaniratisai, J Ugander
  Scaling Choice Models of Relational Social Data
  Proc. 26th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2020.
  [talk slides, SIAMNS by Jan Overgoor] [code] [twitter thread]
  
  
• A Awadelkarim, J Ugander
  Prioritized Restreaming Algorithms for Balanced Graph Partitioning
  Proc. 26th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2020.
  [talk slides, SIAMNS by Amel Awadelkarim] [code] [twitter thread]
  
  
• J Su, K Kamath, A Sharma, J Ugander, S Goel
  An Experimental Study of Structural Diversity in Social Networks
  Proceedings of International AAAI Conference on Web and Social Media (ICWSM), 2020.
  (Best Paper Award)
  [talk slides, CODE@MIT17 by Jessica Su] [arXiv pre-print] [twitter thread]
  
  
• H Yin, A Benson, J Ugander
  Measuring Directed Triadic Closure with Closure Coefficients
  Network Science, 2020.
  [code] [arXiv pre-print]
  
  
• A Seshadri, J Ugander
  Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice
  Extended abstract, ACM Conference on Economics and Computation (EC), 2019.
  [talk video, EC] [talk slides, EC by Arjun Seshadri] [twitter thread]
  
  
• A Seshadri, A Peysakhovich, J Ugander
  Discovering Context Effects from Raw Choice Data
  International Conference on Machine Learning (ICML), 2019.
  [talk slides, ICML by Arjun Seshardi] [code] [twitter thread]
  
  
• J Overgoor, A Benson, J Ugander
  Choosing To Grow a Graph: Modeling Network Formation as Discrete Choice
  Proceedings of the World Wide Web Conference (WWW), 2019.
  [talk slides, NetSci19 by Austin Benson] [code] [twitter thread]
  
  
• A Chin, Y Chen, KM Altenburger, J Ugander
  Decoupled smoothing on graphs
  Proceedings of the World Wide Web Conference (WWW), 2019.
  [talk slides, WWW by Yatong Chen] [code]
  
  
• R Makhijani, J Ugander
  Parametric Models for Intransitivity in Pairwise Rankings
  Proceedings of the World Wide Web Conference (WWW), 2019.
  
  
• 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] [code]
  
  
• 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] [talk slides, CODE@MIT14]
  
  
• 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 (NeurIPS) 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] [talk video by Jon Kleinberg]
  
  
• 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; 2025 Test of Time Award)
  [HyperANF metadata and degree distributions]
  
  
• M Larsson, J Ugander
  A Concave Regularization Technique for Sparse Mixture Models
  Advances in Neural Information Processing Systems (NeurIPS) 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.

Dormant:

• S Ragain, J Ugander
  Choosing to Rank
  arXiv:1809.05139, last updated January 2019.
  (Parts were incorporated into Seshadri, Ragain, and Ugander, NuerIPS 2020)
  
  
• S Ragain, A Peysakhovich, J Ugander
  Improving pairwise comparison models using Empirical Bayes shrinkage
  arXiv:1807.09236, last updated July 2018.

Theses:

• Computational Perspectives on Large-Scale Social Networks
  Ph.D. Thesis, Center for Applied Mathematics, Cornell University, August 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 (Winter 2023)
  Previous versions: Winter 2022, Winter 2021, Winter 2020, Winter 2019, 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 231: Introdution to Computational Social Science (Fall 2022)
  With a vast amount of data now collected on our online and offline actions – from what we buy, to where we travel, to who we interact with – we have an unprecedented opportunity to study traditional social systems with novel precision and detail, as well the emerging challenges of studying modern social systems infused with learning algorithms. In this hands-on course, we first develop ideas from computer science and statistics to address problems in sociology, economics, political science. Beyond that, we place a particular emphasis on the study of algorithm-in-the-loop social systems. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas. Prerequisites: introductory course in applied statistics and experience coding in Python.
  
  
• MS&E 234: Data Privacy and Ethics (Winter 2022)
  Previous versions: Winter 2020, Winter 2019, Spring 2018
  This course engages with ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy raises both practical and theoretical considerations. As part of the module on experimentation, students are required to complete the Stanford IRB training for social and behavioral research. The course assumes a strong technical familiarity with the practice of machine learning and data science. Recommended: 221, 226, CS 161, or equivalents.
  
  
• MS&E 334: Topics in Social Data (Fall 2018)
  Previous versions: Fall 2017, 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. One-off lecture notes:

Spectral theory for planar graphs, including the Spielman-Teng partitioning result. (9/29/2011)

Grant/project pages

Since joining the Stanford faculty my research has been generously supported by the National Science Foundation (NSF), the Army Research Office (ARO), Stanford's Cisco Systems (2022-25) and David Morgenthaler II Faculty Fellowships (2015), a Hellman Foundation Faculty Fellowship (2019), the Stanford Thailand Research Consortium, the Stanford Institute for Human-centered AI (HAI), the Stanford King Center on Global Development, the Stanford Program on Democracy and the Internet (PDI), the Koret Foundation, and Facebook. My Ph.D. students have individually received further external fellowship support from the NSF Graduate Research Fellowship and National Defense Science and Engineering Graduate (NDSEG) Fellowship programs.

A list of my federally funded grants:

• NSF CAREER Awarrd (2022-2027): Machine Learning with Behavioral and Social Data
• ARO MURI Award (2020-Present): A Multimodal Approach to Network Information Dynamics (co-PI)
• ARO Young Investigator Award (2019-2021): Models and Algorithms for Higher Order Network Inference (Award #73348-NS-YIP)
• NSF CRII (2017-2019): Algorithms for Causal Inference on Networks (Award #1657104)

Activities

I have organized or co-chaired the following workshops:

• 2018: Fifth Annual SIAM Workshop on Network Science (SIAM NS)
• 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:

• 2023: GraphEx
• 2022: ACM KDD, SIAM NS, GraphEx, IC2S2
• 2021: ACM EC (area chair), NeurIPS (area chair), WWW, GraphEx, IC2S2
• 2020: ACM WSDM, WWW, GraphEx, IC2S2
• 2019: ACM WSDM, WWW, ACM EC (senior PC), ICCSS, SIAM NS, GraphEx
• 2018: ACM WSDM, WWW, ACM EC, SIAM NS (co-chair), Black in AI
• 2017: ACM WSDM, WWW (senior PC), NIPS (reviewer)
• 2016: WWW, ACM EC, ICCSS, ACM KDD, SIAM NS, AAAI IJCAI, AAAI ICWSM (senior PC), SIAM SDM
• 2015: WWW, ACM EC, ICCSS, ACM KDD, SIAM SDM
• 2014: WWW, SocInfo, ACM CIKM, AAAI ICWSM
• 2013: WebSci

I am also an Associate Editor for Science Advances (AAAS) (7/2022 - present), and increasingly find my reviewing time is devoted to journal review work.

Figure 2. The summit of Fairview Dome, Yosemite National Park, July 2012.

Selected press coverage

• Nature Research Communities, August 2019: Spotlight on Early-Career Researchers interview
• 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?" (only active during North American winter).
• OpenSnow Tahoe blog, with detailed discussions of Tahoe snow forecasts.
• ForecastAdvisor.com for Palo Alto, which tracks the accuracy of different weather forecasts.