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= Peter Lofgren
[peter.lofgren@cs.stanford.edu]
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#I completed my computer science PhD at Stanford University in 2015.
I'm excited about data-mining, efficient infrastructure, and machine learning. #In internships I've developed new news feed personalization algorithms at [https://www.linkedin.com/ LinkedIn], improved the core recommendation algorithm at the Palo Alto start-up [http://www.swell.am/ Swell.am], implemented a distributed data-mining algorithm at [www.google.com/ Google], and designed new algorithms at the [http://www.idaccr.org/ Center for Communications Research].
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 [http://www.stanford.edu/~ashishg/ Ashish Goel].#, and I was affiliated with the [https://www.stanford.edu/group/soal/ Social Algorithms Group], [http://theory.stanford.edu/ Theory Group], and [http://infolab.stanford.edu/ InfoLab] at Stanford.
I've interned at Google, LinkedIn, and a start-up. After graduating, I developed a [https://github.com/teapot-co/tempest high performance graph library] and applied deep learning to photos at [https://www.crunchbase.com/organization/teapot Teapot], and now I'm using machine learning to thwart credit card fraud at [https://stripe.com/ Stripe].
[Lofgren_Resume_2018_December.pdf My Resume.]
Fun fact: I appear in the Hollywood movie /The Internship/ which was filmed at Google while I was an intern there. [The_Internship_Peter_Segment.mp4 20 second clip]
#You can see me walk right to left behind Vince Vaughn and Owen Wilson in [The_Internship_Peter_Segment.mp4 this 20 second clip].
#== Personalized PageRank Code
# Open Source for the Fast-PPR algorithm my collaborators and I developed is [https://github.com/plofgren/fast-ppr-scala/blob/master/src/main/scala/soal/fastppr/FastPPR.scala available at github].
= Publications
== PageRank and Random Walks
Peter Lofgren (joint work with Siddhartha Banerjee and Ashish Goel): *[bidirectional_ppr_thesis.pdf 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: *[http://arxiv.org/abs/1507.05999 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.
- I've implemented our algorithm (with unit tests) [https://github.com/plofgren/bidirectional-random-walk in scala at GitHub] and [https://github.com/snap-stanford/snap/blob/master/snap-core/randwalk.h in C\+\+ as part of SNAP].
Siddhartha Banerjee, Peter Lofgren: *[http://arxiv.org/abs/1507.05998 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. ([heat_kernel_experiments.zip Experiment Code])
Peter Lofgren, Siddhartha Banerjee, Ashish Goel: *[ppr_worst_case.pdf 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: *[http://arxiv.org/abs/1404.3181 FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs]*. KDD 2014
- Open source code is [https://github.com/plofgren/fast-ppr-scala/blob/master/src/main/scala/soal/fastppr/FastPPR.scala available at github].
- [http://videolectures.net/kdd2014_lofgren_page_rank_estimation/ KDD talk (12 min)] and slides ([Fast-PPR_KDD_Talk.pptx pptx]) ([Fast-PPR_KDD_Talk.pdf pdf])
Peter Lofgren, Ashish Goel: *[http://arxiv.org/abs/1304.4658 Personalized PageRank to a Target Node ]*. Technical Report 2013
Peter Lofgren: *[http://arxiv.org/abs/1204.5500 On the complexity of the Monte Carlo method for incremental PageRank]*. Information Processing Letters 114(3): 104-106 (2014)
== Crowdsourcing
Steven Euijong Whang, Peter Lofgren, Hector Garcia-Molina: *[http://ilpubs.stanford.edu:8090/1047/1/CrowdER_TR.pdf Question Selection for Crowd Entity Resolution]*. PVLDB 6(6): 349-360 (2013)
Vasilis Verroios, Peter Lofgren, and Hector Garcia-Molina: *[http://ilpubs.stanford.edu:8090/1129/1/maxLowLatencyTechRep.pdf tDP: An Optimal-Latency Budget Allocation Strategy for Crowdsourced MAXIMUM Operations]*. SIGMOD 2015
== Undergraduate work on Cryptographic Privacy
Peter Lofgren, Nicholas Hopper: *[bnymble.pdf BNymble: More Anonymous Blacklisting at Almost No Cost]* (A Short Paper). Financial Cryptography 2011: 268-275
Peter Lofgren, Nicholas Hopper: *[faust-wpes.pdf 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: *[http://www.worldscientific.com/doi/pdf/10.1142/S021821651000839X On quotients of quandles]*. Journal of Knot Theory and Its Ramifications: 19(09), 1145-1156 (2010)