Google Scholar

* - Authors contributed equally.
' - Work I supervised.

Social Science through the Computational Lens

Elisa Celis, Peter Krafft, Nisheeth Vishnoi. (2017). A Distributed Learning Dynamics in Social Groups. ACM Symposium on Principles of Distributed Computing (PODC).


Peter Krafft, Kaitlyn Zhou, Isabelle Edwards, Kate Starbird, Emma Spiro. (2017). Centralized, Parallel, and Distributed Information Processing during Collective Sensemaking. ACM CHI Conference on Human Factors in Computing Systems (CHI).


Peter Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, and Alex Pentland. (2016). Human collective intelligence as distributed Bayesian inference. arXiv:1608.01987.


Experiments in Collective Intelligence

Elisa Celis, Peter Krafft, and Nathan Kobe. (2016). Sequential Voting Promotes Collective Discovery in Social Recommendation Systems. The Tenth International AAAI Conference on Web and Social Media (ICWSM).

Paper, Code/Data

Peter Krafft, Robert Hawkins, Alex Pentland, Noah Goodman, and Joshua Tenenbaum. (2015). Emergent Collective Sensing in Human Groups. Annual Conference of the Cognitive Science Society (CogSci). [Computational Modeling Prize for Best Paper on Applied Cognition]


Computational Social Science Methodology

Peter Krafft, Michael Macy, Sandy Pentland. (2017). Bots as Virtual Confederates: Design and Ethics. The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).


Machine Learning for Computational Social Science

Ali Nahm, Sandy Pentland, Peter Krafft'. (2016). Inferring Population Preferences via Mixtures of Spatial Voting Models. The 8th International Conference on Social Informatics (SocInfo).

Paper, Code/Data

Peter Krafft, Juston Moore, Bruce Desmarais, and Hanna Wallach. (2012). Topic-Partitioned Multinetwork Embeddings. Neural Information Processing Systems (NIPS).

Paper, Code, Data

Multiagent Models

Peter Krafft, Chris Baker, Alex Pentland, and Joshua Tenenbaum. (2016). Modeling Human Ad Hoc Coordination. The Thirtieth AAAI Conference on Artificial Intelligence (AAAI).

Paper, Code/Data

Older Work

Chang Wang, Peter Krafft, and Sridhar Mahadevan. (2011). Manifold Alignment. Appearing in Manifold Learning: Theory and Applications. Taylor and Francis CRC Press.


Evan Ray, Peter Krafft, Patty Freedson, and John Staudenmayer. (2011). Novel Analytic Methods to Estimate Physical Activity from Accelerometer Data: An Opensource Web-based Tool. International Conference on Ambulatory Monitoring of Physical Activity and Movement. [Runner-up, Best Student Presented Poster].

Abstract, Poster

Peter Krafft and Sridhar Mahadevan. (2010). Feature-Preserving Embeddings for Topic Transfer. NIPS Workshop on Transfer Learning Via Rich Generative Models.

Extended Abstract

Peter Krafft. (2010). Applying Deep Belief Networks to the Game of Go. Undergraduate Thesis.


Peter Krafft and Michael Lavine. (2009). Modeling Light in Harvard Forest. The 15th Annual Massachusetts Statewide Undergraduate Research Conference.

Abstract, Presentation