PhD Student Management Science and Engineering Stanford University
kaltenb [at] stanford.edu
I am a third year PhD candidate in Computational Social Science 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 was previously a Member of Technical Staff in the Data Science and Cyber Analytics Department at Sandia National Laboratories and am 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.
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).
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. [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 have served on the program committee for the following conferences:
WWW 2018, “Web and Society” research paper track [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). September 28, 2017. [link]
NLP + CSS Workshop at ACL in Vancouver, Canada. Poster on The Role of Network Structure for Gender Prediction. August 3, 2017. [link]
Joint Statistical Meetings in Baltimore, Maryland. Accepted joint submission on A Community and Node Attribute-Corrected Stochastic Blockmodel. July 2017. [link]
SIAM Workshop on Network Science in Pittsburgh, Pennsylvania. Accepted joint submission on Adversarial Analysis of Community Detection. July 2017. [link]
The 11th International AAAI Conference on Web and Social Media (ICWSM-17) in Montreal, Canada on Are there Gender Differences in Professional Self-Promotion? 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.