Peter Lofgren Photo 

I'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.

My Resume.

Fun fact: I appear in the Hollywood movie The Internship which was filmed at Google while I was an intern there. 20 second clip


PageRank and Random Walks

Peter Lofgren (joint work with Siddhartha Banerjee and Ashish Goel): Efficient Algorithms for Personalized PageRank. PhD Thesis.

  • Presents our bidirectional algorithms and the prior algorithms they depend on.

Peter Lofgren, Siddhartha Banerjee, and Ashish Goel: Personalized PageRank Estimation and Search: A Bidirectional Approach. WSDM 2016

  • This bidirectional estimator is more efficient and simpler to analyze than our previous estimator, FAST-PPR.

Siddhartha Banerjee, Peter Lofgren: Fast Bidirectional Probability Estimation in Markov Models. NIPS 2015

  • 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: Bidirectional PageRank Estimation: From Average-Case to Worst-Case. Workshop on Algorithms and Models for the Web Graph (WAW) 2015.

  • This gives an alternative bidirectional estimator for Personalized PageRank on undirected graphs.

Peter Lofgren, Siddhartha Banerjee, Ashish Goel, and C. Seshadhri: FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs. KDD 2014

Peter Lofgren, Ashish Goel: Personalized PageRank to a Target Node. Technical Report 2013

Peter Lofgren: On the complexity of the Monte Carlo method for incremental PageRank. Information Processing Letters 114(3): 104-106 (2014)


Steven Euijong Whang, Peter Lofgren, Hector Garcia-Molina: Question Selection for Crowd Entity Resolution. PVLDB 6(6): 349-360 (2013)

Vasilis Verroios, Peter Lofgren, and Hector Garcia-Molina: tDP: An Optimal-Latency Budget Allocation Strategy for Crowdsourced MAXIMUM Operations. SIGMOD 2015

Undergraduate work on Cryptographic Privacy

Peter Lofgren, Nicholas Hopper: BNymble: More Anonymous Blacklisting at Almost No Cost (A Short Paper). Financial Cryptography 2011: 268-275

Peter Lofgren, Nicholas Hopper: FAUST: efficient, TTP-free abuse prevention by anonymous whitelisting. WPES 2011: 125-130

Undergraduate Work on Knot Theory

E. Bunch, P. Lofgren, A. Rapp and D. N. Yetter: On quotients of quandles. Journal of Knot Theory and Its Ramifications: 19(09), 1145-1156 (2010)