1. A Noulas, B Shaw, R Lambiotte, C Mascolo. Topological Properties and Temporal Dynamics of Place Networks in Urban Environments, International Conference on World Wide Web WWW 2015 (BibTex)

  2. N Grinberg, M Naaman, B Shaw, G Lotan. Extracting Diurnal Patterns of Real World Activity from Social Media, International AAAI Conference on Weblogs and Social Media ICWSM 2013. (BibTex)

  3. B Shaw, J Shea, S Sinha, A Hogue. Learning to Rank for Spatiotemporal Search, Proc. 6th ACM Int'l Conf. on Web Search and Data Mining (WSDM), 2013. (BibTex)

  4. B Shaw, B Huang, T Jebara. Learning a Distance Metric from a Network, Neural Information Processing Systems, NIPS, December 2011. (Supplemental, Poster, BibTex, Code)

  5. B Shaw, T Jebara. Structure Preserving Embedding,  International Conference on Machine Learning, ICML, June 2009. Best Paper Award Winner. (Poster, Slides, BibTex, Talk:, Talk: MP4)

  6. B Shaw, T Jebara. Minimum Volume Embedding, Artificial Intelligence and Statistics, AISTATS, March 2007. (Poster, BibTex, Code)


  1. M Sklar, B Shaw, A Hogue. Recommending Interesting Events in Real-time with Foursquare Check-ins, ACM Conference on Recommender Systems RecSys 2012.

  2. B Huang, B Shaw, T Jebara. Learning a Degree-Augmented Distance Metric from a Network, Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity. NIPS 2011 workshop.

  3. B Shaw, T Jebara. Visualizing Social Networks with Structure Preserving Embedding, Interdisciplinary Workshop on Information and Decision in Social Networks 2011. (Poster)

  4. B Huang, B Shaw, Tony Jebara. Network Prediction with Degree Distributional Metric Learning, Interdisciplinary Workshop on Information and Decision in Social Networks 2011. (Poster)

  5. B Shaw, T Jebara. Dimensionality Reduction, Clustering, and PlaceRank Applied to Spatiotemporal Flow Data, New York Academy of Science - Machine Learning Symposium 2009. (Poster)

  6. B Shaw, T Jebara. Visualizing Graphs with Structure Preserving Embedding, Analyzing Graphs: Theory and Applications. NIPS Workshop. December 2008.

  7. B Shaw, T Jebara. Graph Embedding with Global Structure Preserving Constraints, New York Academy of Science - Machine Learning Symposium, October 2008. (Poster)

  8. B Shaw, T Jebara. Minimum Volume Embedding (NYAS), New York Academy of Science - Machine Learning Symposium 2007.

  9. T Jebara, B Shaw, A Howard. Optimizing Eigengaps and Spectral Functions using Iterated SDP, Learning Workshop 2007.

  10. T Jebara, B Shaw, V Schogolev. B-matching for Embedding, Snowbird Machine Learning Conference, April 2006.


  1. Data for Understanding Cities, Buildings Equal Data Conference Keynote, May 2015

  2. Data driven Products at Foursquare, Data driven NYC Meetup, November 2013. (Blog post)

  3. Designing Machine Learning Algorithms for Hadoop, ML Meetup, August 2013. (Meetup)

  4. What Can We Learn From Billions of Foursquare Check-ins?, Strata, October 2012 (Abstract)

  5. Big Data and the Big Apple: Understanding New York City using Millions of Check-ins, DataGotham, September 2012 (Blog post)

  6. Machine Learning with Large Networks of People and Places, ML Meetup, March 2012. (Blog post, Slides)

  7. Structure Preserving Embedding, International Conference on Machine Learning, ICML, June 2009.

Selected Press

  1. Why Data Science Matters to Foursquare, The Guardian, January 2014.

  2. The Brilliant Hack That Brought Foursquare Back From the Dead, Wired, Dec 2013.

  3. How Foursquare Made Those Insane Data Visualizations, Fast Co, October 2013

  4. Data Stories: Interview with Data Scientist Blake Shaw of Foursquare, Gnip Blog, November 2012.

  5. Need a Cab? New Analysis Shows Where to Find One, New York Times, April 2010.

  6. Yo, Taxi! CabSense finds rides – and a way to crunch big data, Venture Beat, April 2010.

Infographics and Blog Posts at Foursquare

  1. Foursquare check-ins show the pulse of cities

  2. Foursquare’s new notifications and the future of contextual mobile experiences

  3. 500 Million Checkins -- The Last Three Months on Foursquare

  4. A Hackday Project: What neighborhood is the ‘East Village’ of San Francisco?

Projects at Sense Networks

CabSense - The Smartest Way to Hail a Cab in NY

MacroSense - Relevant Recommendation, Personalization and Discovery from Mobile Location Data

CitySense - Live San Francisco Nightlife Activity


Programming Languages (Matlab)

w3101 section 1 - Spring 2008

Course Website


13/707,478 - Network information methods devices and systems

12/134,634 - System and Method of Performing Location Analytics

12/241,266 - Event Identification in Sensor Analytics

2/241,227 - Comparing Spatial-Temporal Trails in Location Analytics

13/660,261 - System and Method for Providing Recommendations with a Location-based Service

13/974,708 - System and method for contextual messaging in a location-based network

Earlier Projects in Computer Science at Columbia

Engineering manager at Facebook applying machine learning algorithms to large spatiotemporal datasets.  Previously head of data science at Foursquare, and research scientist at Columbia University and Sense Networks.

Research Interests: machine learning, spatiotemporal data, networks, visualization, dimensionality reduction, spectral optimizations, data mining, graph algorithms, social media, large datasets.

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