Bio / CV

Jake Porway has a love of all things related to pattern recognition, data visualization, and that happy union when a huge amount of information slams into something smart enough to do something with it. He revels in the divide between academic expertise and industrial know-how, just as excited to pioneer the next machine learning algorithm as he is to optimize the code for it. Jake is also an abiding fan of art and socially responsible living, loves learning new languages and musical instruments, and learned a lot about life from living with a very fat cat.

Jake joined the New York Times Research & Development group in December 2010, where he is currently exploring the relationships between social media and the process of creating and sharing news, developing interfaces for interaction with new devices, and otherwise redefining media as we know it in his role as Data Scientist. He holds a B.S. in Computer Science and a Ph.D. in Statistics from UCLA, where his research focused on learning hierarchical and contextual grammar models for patterns in images, including recognizing object categories as well as modeling aerial images. Jake was previously a Research Scientist at UtopiaCompression, where he lead and worked on a load of fun and exciting machine learning and computer vision projects, including active and incremental learning, anomaly detection, high-dimensional data visualization and abstraction, and image-to-text conversion. Through internships and collaborations, Jake has also had the privilege of working at Google and Bell Laboratories, as well as contracting projects with the Office of Naval Research, DARPA, and NASA’s Jet Propulsion Laboratory.

Jake is currently living in New York. You can find out more about his work in the projects section, on his blog, or in his CV below.


CV (last updated 7/10/10)


J. Porway, S.C. Zhu, “C4: Exploring Multiple Solutions in Graphical Models by Cluster Sampling”, Pattern Analysis and Machine Intelligence, 2011.

J. Porway, K. Wang, S.C. Zhu, “A Hierarchical and Contextual Model for Aerial Image Understanding”, International Journal of Computer Vision, 2010.

J. Porway, B. Yao, S.C. Zhu, “Learning Compositional Models for Object Categories From Small Sample Sets”, Object Categorization: Computer and Human Vision Perspectives, Cambridge Press, 2009.

L. Lin, T.F. Wu, J. Porway, “A Stochastic Graph Grammar for Compositional Object Representation and Recognition”, Pattern Recognition, 2008.

J. Porway, K. Wang, B. Yao, S.C. Zhu, “A Hierarchical and Contextual Model for Aerial Image Understanding”, Computer Vision and Pattern Recognition, 2008.

L. Lin, S. Peng, J. Porway, S.C. Zhu, Y. Wang, “An Empirical Study of Object Category Recognition: Sequential Testing with Generalized Samples”, International Conference on Computer Vision, 2007.

S. Peng, L. Lin, J. Porway, N. Sang, S.C. Zhu, “Object Category Recognition Using Generative Template Boosting”, Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007.