I am a Research Scientist with Core Data Science at Facebook. I defended my Ph.D. thesis in June 2019 in Computational Social Science in the Management Science & Engineering Department at Stanford University, advised by Johan Ugander (expected graduation December 2019). My research focuses on developing statistical methods for characterizing social structures in networks, with applications to data privacy, and focuses on promoting equitable digital systems that feature complex cultural and political considerations.
I received my BS 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 AM in Statistics from Harvard University in 2015. I was previously 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. During the summer 2017, I joined the Social Science & Algorithms team at Netflix for an internship analyzing word-of-mouth effects and other social signals. During the summer 2018, I did graduate research at Stanford University. I was a 2018 Stanford Data Science Scholar, a Graduate Student Fellow of the Regulation, Evaluation and Governance Lab (RegLab), a member of the Society & Algorithms Lab, and my graduate work was supported in part by a National Defense Science and Engineering Graduate Fellowship.
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. WWW.
(Best Poster, Honorable Mention) [paper][code]
Alex Chin, Yatong Chen, Kristen M. Altenburger and Johan Ugander (2019). Decoupled Smoothing on Graphs. WWW. [paper][code]
Kristen M. Altenburger and Johan Ugander (2018). Node Attribute Prediction: An Evaluation of Within- versus Across-Network Tasks. NeurIPS Workshop on Relational Representation Learning. [paper]
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. Proc. 11th AAAI Int’l Conf. on Weblogs and Social Media (pp. 460-463). [paper]
Data access granted due to winning entry in the LinkedIn Economic Graph Challenge, 2015. [link]
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]
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 [link]
ACM Conference on Web Science (WebSci) 2019 [link]
Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019 [link]
International Conference on Computational Social Science (IC2S2) 2017 [link]
International AAAI Conference on Web and Social Media (ICWSM) 2018, 2019 [link]
International World Wide Web Conference (WWW) 2018, 2019, 2020 [link]
Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) 2017, 2019 [link]
and have served as a reviewer for Science Advances and Big Data & Society.
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]
Selected Workshops + Conferences + Talks
9th Yale Law School Annual Doctoral Conference in New Haven, CT. Talk for the Law & Economics, Network Theory, and Policy. November 8-9, 2019. [link] Conference on Digital Experimentation (CODE) in Cambridge, MA. Talk on Rumors on Social Networks. November 1-2, 2019. [link] PhD Oral Defense in Stanford University. Committee: Daniel E. Ho, Ramesh Johari, Philip Kegelmeyer, and Johan Ugander. June 10, 2019.
The Web Conference 2019 in San Francisco, CA. Talk and Poster. May 13-17, 2019. [link] Women in Analytics Conference in Columbus, OH. Talk. March 21-22, 2019. [link] Relational Representation Learning at NeurIPS 2018 Workshop in Montréal, Canada. Work on Node Attribute Prediction: An Evaluation of Within- versus Across-Network Tasks. December 8, 2018. [link] MIT Conference on Digital Experimentation (CODE) in Boston, MA. Talk on Uncomplicate Causal Inference Complications on Social Networks. October 26-27, 2018. [link] Network Science Institute at Northeastern University in Boston, MA. Talk on Ruffled Feathers. Joint work with Johan Ugander. October 25, 2018. Xerox PARC in Palo Alto, CA. Talk on Ruffled Feathers. Joint work with Johan Ugander. September 26, 2018. [link] International Conference on Computational Social Science in Evanston, IL. Talk on The Network Structure of Missing Attributes. Joint work with Johan Ugander. July 13-15, 2018. Travel support awarded. [link] SIAM Workshop on Network Science in Portland, OR. Poster on Uncomplicate Causal Inference Complications on Social Networks. July 12-13, 2018. [link] Atlantic Causal Inference Conference in Pittsburgh, PA. Poster on Uncomplicate Causal Inference Complications on Social Networks. May 22-23, 2018. [link] NLP + CSS Workshop at ACL in Vancouver, Canada. Poster on The Role of Network Structure for Gender Prediction. Joint work with Johan Ugander. August 3, 2017. [link] Joint Statistical Meetings in Baltimore, Maryland. Poster on A Community and Node Attribute-Corrected Stochastic Blockmodel. Joint work with Sandia National Labs. July 2017. [link] Doctoral Consortium on Natural Language Processing and Computational Social Science in Austin, TX. November 5, 2016. Travel support awarded. [link] SIAM Workshop on Network Science in Boston. Talk on Ruffled Feathers. Joint work with Johan Ugander. July 15, 2016. [link] International Conference on Computational Social Science in Evanston, IL. Talk on Ruffled Feathers. Joint work with Johan Ugander. June 24, 2016. [link]