My research leverages machine learning and causal inference methods for business and policy decisions, taking into account the unique feature of networked systems and human behavior. I am currently a Staff Research Scientist with the Networks & Behavioral Modeling group within the Central Applied Science (formerly Core Data Science) team at Meta and a Non-Resident Fellow with the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford Law School. Previously, I received my Bachelor of Science (B.S.) in Mathematics from Ohio University in 2012 where I was also a Barry M. Goldwater Scholar, completed a research fellowship with Alison Morantz and Dan Ho at Stanford Law School in 2012-2014, and received my Master of Arts (A.M.) in Statistics from Harvard University in 2015. I received my Ph.D. (January 2020) in Computational Social Science in the Management Science & Engineering Department at Stanford University (advised by Johan Ugander) where my graduate work was supported in part by a National Defense Science and Engineering Graduate Fellowship. I was formerly a Member of Technical Staff in the Data Science and Cyber Analytics Department at Sandia National Laboratories and was a 2016 SPOT Award recipient based on my research.
Working Papers
Yuan Yuan and Kristen M. Altenburger (2023). Characterizing Interference Heterogeneity and Improving Estimation for Experiments in Networks.
Major revision at Manufacturing & Service Operations Management [paper]
Gordon Burtch, Poppy Zhang, Kristen M. Altenburger, and Shawndra Hill (2023). Comments on Social Media Ads.
(paper available upon request)
Publications
Yiguang Zhang, Kristen M. Altenburger, Poppy Zhang, Tsutomu Okano, and Shawndra Hill (2024). Node Attribute Prediction on Multilayer Networks with Weighted and Directed Edges. Forthcoming: International AAAI Conference on Web and Social Media [paper][code]
Kristen M. Altenburger, Robert Kraut, Shirley Hayati, Jane Yu, Kaiyan Peng, and Yi-Chia Wang (2024). Consequences of Conflicts in Online Conversations. Forthcoming: International AAAI Conference on Web and Social Media
Peter Henderson, Ben Chugg, Brandon Anderson, Kristen M. Altenburger, Alex Turk, John Guyton, Jacob Goldin, and Daniel E. Ho (2023). Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for the Internal Revenue Service Audit Selection. AAAI Conference on Artificial Intelligence. [paper]
Sharon Levy, Yi-Chia Wang, Robert Kraut, Jane Yu, and Kristen M. Altenburger (2022). Understanding Conflicts in Online Conversations. TheWebConf. [paper]
Shen Yan, Kristen M. Altenburger, Yi-Chia Wang, and Justin Cheng (2022). What Does Perception Bias on Social Networks Tell Us about Friend Count Satisfaction? TheWebConf. [paper][code]
Yuan Yuan, Kristen M. Altenburger, and Farshad Kooti (2021). Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests. TheWebConf. [paper][code]
Kristen M. Altenburger and Johan Ugander (2021). Which Node Attribute Prediction Task Are We Solving? Within-Network, Across-Network, or Across-Layer Tasks. International AAAI Conference on Web and Social Media. (Outstanding Problem-Solution Paper Award) [paper][code]
Kristen M. Altenburger and Daniel E. Ho (2019). Is Yelp Actually Cleaning Up the Restaurant Industry? A Re-Analysis on the Relative Usefulness of Consumer Reviews. TheWebConf. (Best Poster, Honorable Mention) [paper][code]
Alex Chin, Yatong Chen, Kristen M. Altenburger and Johan Ugander (2019). Decoupled Smoothing on Graphs. TheWebConf. [paper][code]
Kristen M. Altenburger and Daniel E. Ho (2018). When Algorithms Import Private Bias into Public Enforcement:
The Promise and Limitations of Statistical De-Biasing Solutions. Journal of Institutional and Theoretical Economics. [paper][supplementary information][code][JITE page]
Kristen M. Altenburger, Rajlakshmi De, Kaylyn Frazier, Nikolai Avteniev, and Jim Hamilton (2017). Are there Gender Differences in Professional Self-Promotion? An Empirical Case Study of LinkedIn Profiles among Recent MBA Graduates. International AAAI Conference on Web and Social Media. (Winner of the 2015 LinkedIn Economic Graph Challenge) [paper]
Kyler Siegel, Kristen Altenburger, Yu-Sing Hon, Jessey Lin and Chenglong Yu (2015). PuzzleCluster: A Novel Unsupervised Clustering Algorithm for Binning DNA Fragments in Metagenomics. Current Bioinformatics, 10(2), 231-252. [paper]
Service
I have served on the program committee for the following conferences:
AAAI Conference on Artificial Intelligence (AAAI) 2019 [link]
Association for Computational Linguistics (ACL) 2019, 2020 [link]
ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2022 [link]
ACM Conference on Web Science (WebSci) 2019, 2020 [link]
Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019, 2020 [link]
International Conference on Computational Social Science (IC2S2) 2017 [link]
International AAAI Conference on Web and Social Media (ICWSM) 2018, 2019, 2020 (Best Reviewer Award), 2021 [link]
International World Wide Web Conference (TheWebConf) 2018, 2019, 2020, 2021, 2022 [link]
Knowledge Discovery in Databases (KDD): Workshop on Humanitarian Mapping 2020, 2021, 2022 [link]
Invited Seminar Speaker (Business Analytics) at the University of Iowa. Invited Speaker on Social Networks for Product Innovation. (upcoming) April 12, 2024. [link] Foundations of Fairness, Privacy and Causality in Graphs Workshop at UC Santa Cruz. Invited Speaker on Conversations and User-to-User Networks for Product Innovation. October 19-20, 2023. [link] INFORMS (Human Centered Machine Learning Session) in Phoenix, AZ. Invited Speaker on Conversations and User-to-User Networks for Product Innovation. October 17, 2023. [link] IEEE IEMCON (online). Invited Keynote Speaker. October 12-15, 2022. [link] WPI CS Colloquium (online). Invited speaker on Network Science and Human Behavior. October 8, 2021. [link] University of Michigan, Data Science & Computational Social Science Seminar Series (online). Invited speaker on Network Science and Human Behavior. September 23, 2021. [link] Network Structure: The Causes and Effects of Social Phenomena (online). Invited keynote on How Friends of Friends are Useful for Node Attribute Prediction. June 23, 2021. [link] MIT Conference on Digital Experimentation (CODE) (online). Talk on Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests. November 19-20, 2020. [link] "Fresh from the arXiv" talks at the University of Oxford (online). Talk on Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests. November 17, 2020. [link]
Press Coverage
Facebook AI Blog, “Facebook AI’s co-teaching program to increase pathways into AI for diverse candidates”, October 2020 [link]
Food Safety Magazine, “Artificial Intelligence and Food Safety: Hype vs. Reality”, December 2019 [link]
Food Safety News, “Can Silicon Valley save food safety? Maybe, but not with online reviews alone”, January 2019 [link]
Scientific American, “Friends of Friends Can Reveal Hidden Information about a Person”, June 2018 [link]
Rewire, “How Friends of Your Friends Give Away Your Private Info Online”, May 2018 [link]
LinkedIn Engineering Blog, “Economic Graph Research Program: Insights and Updates”, July 2018 [link]
LinkedIn Engineering Blog, “Measuring Gender Diversity with Data from LinkedIn”, June 2015 [link]