## Peter LofgrenI'm excited about data-mining, efficient infrastructure, and machine learning. For my PhD I developed more efficient algorithms for Personalized PageRank, a model of user interests on networks which is used for friend recommendation and personalized search. My awesome PhD advisor was Ashish Goel. I've interned at Google, LinkedIn, and a start-up. After graduating, I developed a high performance graph library and applied deep learning to photos at Teapot, and now I'm using machine learning to thwart credit card fraud at Stripe. Fun fact: I appear in the Hollywood movie ## Publications## PageRank and Random WalksPeter Lofgren (joint work with Siddhartha Banerjee and Ashish Goel): Presents our bidirectional algorithms and the prior algorithms they depend on.
Peter Lofgren, Siddhartha Banerjee, and Ashish Goel: This bidirectional estimator is more efficient and simpler to analyze than our previous estimator, FAST-PPR.
I've implemented our algorithm (with unit tests) in scala at GitHub and in C++ as part of SNAP.
Siddhartha Banerjee, Peter Lofgren: This generalizes our bidirectional estimator to arbitrary Markov Chains, and allows fast estimation of the Heat Kernel between a pair of nodes. (Experiment Code)
Peter Lofgren, Siddhartha Banerjee, Ashish Goel: This gives an alternative bidirectional estimator for Personalized PageRank on undirected graphs.
Peter Lofgren, Siddhartha Banerjee, Ashish Goel, and C. Seshadhri: Open source code is available at github.
KDD talk (12 min) and slides (pptx) (pdf)
Peter Lofgren, Ashish Goel: Peter Lofgren: ## CrowdsourcingSteven Euijong Whang, Peter Lofgren, Hector Garcia-Molina: Vasilis Verroios, Peter Lofgren, and Hector Garcia-Molina: ## Undergraduate work on Cryptographic PrivacyPeter Lofgren, Nicholas Hopper: Peter Lofgren, Nicholas Hopper: ## Undergraduate Work on Knot TheoryE. Bunch, P. Lofgren, A. Rapp and D. N. Yetter: |