Alex Beutel
Carnegie Mellon University
Ph.D. Candidate in Computer Science

About Me

I am a fifth year Ph.D. candidate in computer science at Carnegie Mellon University, advised by Professor Christos Faloutsos. I study scalable graph modeling. My thesis research is focused on large-scale user behavior modeling, covering fraud detection, recommendation systems, and scalable machine learning. A complete list of my publications can be found here.

I am on the academic job market this year: C.V., Research Statement, Teaching Statement

Email me at or contact me on Twitter or LinkedIn.

Recent News

November 2015 - I will be attending NIPS in Montreal to co-host the Machine Learning Systems workshop and to present Additive Co-Clustering of Gaussians and Poissons for Joint Modeling of Ratings and Reviews at the workshop on Nonparametric Methods for Large Scale Representation Learning.
September 2015 - I will be speaking at WIN 2015 on CoBaFi - Bayesian collaborative filtering, robust recommendation, and polarized ratings.
August 2015 - A General Suspiciousness Metric for Dense Blocks in Multimodal Data with Meng Jiang, Peng Cui, Christos Faloutsos and Shiqiang Yang was accepted to ICDM 2015.
July 2015 - My tutorial with Leman Akoglu and Christos Faloutsos Fraud Detection through Graph-Based User Behavior Modeling was selected for ACM CCS 2015.
April 2015 - My tutorial with Leman Akoglu and Christos Faloutsos Graph-Based User Behavior Modeling: From Prediction to Fraud Detection was selected for KDD 2015.
April 2015 - I was selected to attend the Heidelberg Laureate Forum in August.
March 2015 - I will be spending the summer at Google Research in Mountain View.
January 2015 - My paper ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly with Amr Ahmed and Alex Smola was accepted to WWW 2015. I will be in Florence in May to present the work.
November 2014 - My paper Elastic Distributed Bayesian Collaborative Filtering with Markus Weimer, Tom Minka, Yordan Zaykov, and Vijay Narayanan, based on our work this summer at Microsoft, was accepted to the NIPS Distributed Machine Learning workshop. I will be at NIPS in December to present the work.
October 2014 - My paper Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective with Neil Shah, Brian Gallagher, and Christos Faloutsos has been accepted to ICDM 2014.
July 2014 - CatchSync has been selected as one of the best papers in KDD 2014.
June 2014 - A research proposal that I co-authored with Christos Faloutsos, Amin Mantrach, and Alejandro Jaimes on spam and fraud detection in Tumblr was selected for the Yahoo! Faculty Research and Engagement Program Award.
June 2014 - August 2014 - I am spending the summer at Microsoft, working with the CISL team to scale machine learning on top of REEF.
May 2014 - My paper CatchSync: Catching Synchronized Behavior in Large Directed Graphs with Meng Jiang, Peng Cui, Christos Faloutsos and Shiqiang Yang was accepted to KDD. I will post the camera-ready version soon.
Feb. 2014 - I was lucky enough to win the Facebook Graduate Fellowship for 2014-2015.
Jan. 2014 - The paper Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models with Abhimanu Kumar, Qirong Ho, and Eric Xing was accepted to AISTATS for a full presentation in Reykjavik, Iceland in April.
Jan. 2014 - My paper CoBaFi: Collaborative Bayesian Filtering with Kenton Murray, Alex Smola, and Christos Faloutos was accepted to WWW. I will be presenting it in Seoul, South Korea in April.
Jan. 2014 - The paper FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop with Abhimanu Kumar, Vagelis Papalexakis, Partha Talukdar, Christos Faloutsos, and Eric Xing was accepted to SDM and will be presented in Philadelphia in April. We will relase the source code soon.
Jan. 2014 - The paper Inferring Strange Behavior from Connectivity Pattern in Social Networks with Meng Jiang, Peng Cui, Christos Faloutsos, and Shiqiang Yang was accepted to PAKDD. Meng will be presenting it in Tainan, Taiwan in May.
Select Publications (Complete List, DBLP, Google Scholar)

Fraud Detection

Recommendation Systems

Scalable Machine Learning