PhD Student Management Science and Engineering Stanford University
kaltenb [at] stanford.edu
I am a third year PhD candidate in the Management Science & Engineering Department at Stanford University advised by Johan Ugander and am a member of the Social Algorithms Lab. My research interests include social network analysis, machine learning, and causal inference.
I received my BS in Mathematics from Ohio University in 2012 where I was also a Barry M. Goldwater Scholar, completed a research fellowship at Stanford Law School in 2012-2014, and received my AM in Statistics from Harvard University in 2015. I am also a 2016 recipient of the National Defense Science & Engineering Graduate Fellowship. I spent Summers 2015 and 2016 in the Data Science and Cyber Analytics Department at Sandia National Laboratories and am a 2016 Spot Award recipient based on my summer research.
This Summer 2017, I am excited to join Netflix.
Network structure and gender prediction. Joint work with Johan Ugander.
Community Detection in Networks. Joint work with Sandia National Laboratories.
Kristen M. Altenburger and Johan Ugander. Bias and variance in the social structure of gender (under review, 2017). [paper]
Kristen M. Altenburger, W. Philip Kegelmeyer, Ali Pinar, and Jeremy D. Wendt. A Community and Node Attribute-Corrected Stochastic Blockmodel (working paper, 2017).
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 (Poster presentation in Montreal, Canada). [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, 231-252. [paper]
I am serving on the program committee for the “Web and Society” research paper track WWW 2018 [link] and have served on the program committee for the ACL workshop on NLP+CSS 2017 [link] and IC2S2 2017 [link], and have served as a reviewer for Big Data & Society [link].
workshops + conferences + talks
Brown Institute Media Innovation Showcase at Columbia University. Presenting on Re(ef)source - Crowdsourced Coral Reef Imaging, a Recipient of a Magic Grant from the Brown Institute for Media Innovation (2016-2017). upcoming: September 2017.
NLP + CSS Workshop at ACL in Vancouver, Canada. Accepted joint submission. upcoming: August 2017. [link]
Joint Statistical Meetings in Baltimore, Maryland. Accepted joint submission. July 2017. [link]
SIAM Workshop on Network Science in Pittsburgh, Pennsylvania. Accepted joint submission. July 2017. [link]
The 11th International AAAI Conference on Web and Social Media (ICWSM-17) in Montreal, Canada. May 15-18, 2017. [link]
''The Role of Social in Social Theory, Social Media, and Social Engineering'': Class Lecture for MS&E190 at Stanford University. May 4, 2017.
Doctoral Consortium on Natural Language Processing and Computational Social Science in Austin, TX. Accepted submission with travel support. November 5, 2016. [link]
Dean’s Seminar Talk at Sandia National Labs in Livermore, CA. August 9, 2016.
SIAM Workshop on Network Science in Boston. Talk on Ruffled Feathers. July 15, 2016. [link]
International Conference on Computational Social Science at Kellogg School of Management, Northwestern. Talk in the Social Networks Session on Ruffled Feathers. June 24, 2016. [link]
West Coast Experiments Conference at Stanford GSB. Attended. May 21, 2016. [link]
Personal and Partisan Incumbency Effects in the Electoral Regression Discontinuity Design: A Principal- Stratification Approach. Presented poster at the Women in Machine Learning Workshop. Joint work with Andrew Hall. December 7, 2015. [link]
Second Annual Media Innovation Base Camp at the Brown Institute, Columbia University. Exploring the intersection of storytellling + technology. November 6-8, 2015. [link]
Women in Data Science Conference at Stanford University. Attended. November 2, 2015. [link]
Applications of Machine Learning to Economics and the Social Sciences by Sendhil Mullainathan. Mini-course at UC Berkeley's Institute for Research on Labor and Employment. August 27-29, 2015.