Metablake

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Machine Learning & Data Science

About Me

Engineering leader passionate about machine learning, data science, and product development. Currently a Senior Director of Engineering at Reddit. Previously at Instagram, Facebook, Twitter, Foursquare, Sense Networks, and Columbia University.

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

Quora | Google Scholar | Twitter | LinkedIn

Publications

• 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)
• 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)
• 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)
• B Shaw, B Huang, T Jebara. Learning a Distance Metric from a Network, Neural Information Processing Systems, NIPS, December 2011. (Supplemental, Poster, BibTex, Code)
• B Shaw, T Jebara. Structure Preserving Embedding,  International Conference on Machine Learning, ICML, June 2009. Best Paper Award Winner. (Poster, Slides, BibTex, Talk: videolectures.net, Talk: MP4)
• B Shaw, T Jebara. Minimum Volume Embedding, Artificial Intelligence and Statistics, AISTATS, March 2007. (Poster, BibTex, Code)

Workshop

• M Sklar, B Shaw, A Hogue. Recommending Interesting Events in Real-time with Foursquare Check-ins, ACM Conference on Recommender Systems RecSys 2012.
• 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.
• B Shaw, T Jebara. Visualizing Social Networks with Structure Preserving Embedding, Interdisciplinary Workshop on Information and Decision in Social Networks 2011. (Poster)
• B Huang, B Shaw, Tony Jebara. Network Prediction with Degree Distributional Metric Learning, Interdisciplinary Workshop on Information and Decision in Social Networks 2011. (Poster)
• B Shaw, T Jebara. Dimensionality Reduction, Clustering, and PlaceRank Applied to Spatiotemporal Flow Data, New York Academy of Science - Machine Learning Symposium 2009. (Poster)
• B Shaw, T Jebara. Visualizing Graphs with Structure Preserving Embedding, Analyzing Graphs: Theory and Applications. NIPS Workshop. December 2008.
• B Shaw, T Jebara. Graph Embedding with Global Structure Preserving Constraints, New York Academy of Science - Machine Learning Symposium, October 2008. (Poster)
• B Shaw, T Jebara. Minimum Volume Embedding (NYAS), New York Academy of Science - Machine Learning Symposium 2007.
• T Jebara, B Shaw, A Howard. Optimizing Eigengaps and Spectral Functions using Iterated SDP, Learning Workshop 2007.
• T Jebara, B Shaw, V Schogolev. B-matching for Embedding, Snowbird Machine Learning Conference, April 2006.

Talks

• Data for Understanding Cities, Buildings Equal Data Conference Keynote, May 2015
• Data driven Products at Foursquare, Data driven NYC Meetup, November 2013. (Blog post)
• Designing Machine Learning Algorithms for Hadoop, ML Meetup, August 2013. (Meetup)
• What Can We Learn From Billions of Foursquare Check-ins?, Strata, October 2012 (Abstract)
• Big Data and the Big Apple: Understanding New York City using Millions of Check-ins, DataGotham, September 2012 (Blog post)
• Machine Learning with Large Networks of People and Places, ML Meetup, March 2012. (Blog post, Slides)
• Structure Preserving Embedding, International Conference on Machine Learning, ICML, June 2009.

Selected Press

• Why Data Science Matters to Foursquare, The Guardian, January 2014.
• The Brilliant Hack That Brought Foursquare Back From the Dead, Wired, Dec 2013.
• How Foursquare Made Those Insane Data Visualizations, Fast Co, October 2013
• Data Stories: Interview with Data Scientist Blake Shaw of Foursquare, Gnip Blog, November 2012.
• Need a Cab? New Analysis Shows Where to Find One, New York Times, April 2010.
• Yo, Taxi! CabSense finds rides – and a way to crunch big data, Venture Beat, April 2010.

Infographics and Blog Posts at Foursquare

• Foursquare check-ins show the pulse of cities
• Foursquare’s new notifications and the future of contextual mobile experiences
• 500 Million Checkins -- The Last Three Months on Foursquare
• A Hackday Project: What neighborhood is the ‘East Village’ of San Francisco?

Apps

• CabSense - The Smartest Way to Hail a Cab in NY

Teaching

• Programming Languages (Matlab), w3101 section 1 - Spring 2008, Course Website

Patents

• 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