Our work, Terrapattern, has had a very successful public launch and got featured in many technology news outlets. Terrapattern allows a user to perform arbitrary queries-by-example in satellite imagery. Terrapattern is ideal for discovering, locating and labeling typologies that aren't customarily indicated on maps. These might include ephemeral or temporally-contingent features (such as vehicles or construction sites), or the sorts of banal infrastructure (like fracking wells or smokestacks) that only appear on specialist blueprints, if they appear at all. Terrapattern was created by Golan Levin, David Newbury, Kyle McDonald, Irene Alvarado, Aman Tiwari, and Manzil Zaheer. The project was developed at the Carnegie Mellon University Frank-Ratchye STUDIO for Creative Inquiry with support from the John S. and James L. Knight Foundation.
-  Coldewey, Devin. "Terrapattern is reverse image search for maps, powered by a neural network". TechCrunch, 5/25/2016
-  Mandelbaum, Ryan F. "This website lets you find the hidden similarities in big cities". Popular Science, 5/25/2016
-  Meyer, Robinson. "The Thrill of Terrapattern, a New Way to Search Satellite Imagery". The Atlantic, 5/27/2016
-  Singleton, Micah. "Terrapattern is the first open-access visual search engine for satellite maps". The Verge, 5/27/2016
-  Stinson, Liz. "Terrapattern is Like a Search Engine for Satellite Imagery". WIRED.com, 5/27/2016
-  Twilley, Nicola. "Meet Terrapattern, Google Earth's Missing Search Engine". The New Yorker, 5/25/2016
-  Visnjic, Filip. "Terrapattern – Neural network visual search tool for satellite imagery". Creative Applications.net, 5/25/2016
Hi! and welcome to my website. My name is Manzil Zaheer. I am a fourth year PhD student in Machine Learning Department, School of Computer Science at Carnegie Mellon University. Prior to that I earned my B.Tech degree at Indian Institute of Technology, Guwahati.
I am fortunate to be advised by Prof Barnabas Poczos, Prof Rulan Salakhutdinov, and Prof Alex Smola. Also I am lucky to able to collaborate with Guy Steele, Jean-Baptiste Tristan, Michael Wick, Prof Xin and to get an opportunity to work with excellent fellow students Rajarshi Das, Satwik Kottur, Jay-Yoon Lee, Hsiao-Yu Fish Tung, Fa Wang, and Chao-Yuan Wu.
A common thread in my research is the harnessing of theoretical techniques from varied disciplines to solve practical problems. I love learning, implementing complicated statistical inference, data-parallel programming, and algorithms in a simple way. Currently I am interested in nonparametric Bayesian methods, scalable machine learning, fast sampling techniques, random graph generative models and applications of machine learning to VLSI CAD.