Alex Beutel

Alex Beutel

Senior Research Scientist at Google

About Me

I am a Senior Research Scientist at Google Research, based in New York City. My research there spans neural recommender systems, machine learning fairness, and machine learning systems.

In 2016, I completed my Ph.D. in computer science at Carnegie Mellon University, advised by Christos Faloutsos and Alex Smola. My thesis research focused on large-scale user behavior modeling, covering fraud detection, recommender systems, and scalable machine learning. Over the course of graduate school, I interned with Facebook's Site Integrity team and News Feed Ranking team, Microsoft's Cloud and Information Service Lab, and Google Research.

Before graduate school, I majored in computer science and physics at Duke University. While there, I worked with Pankaj Agarwal and Thomas Mølhave on computational geometry for terrain modeling.

Check out a complete list of my publications, tutorials, code, and talks.

Email me at or contact me on Twitter or LinkedIn.

Recent News

October 2018 - Top-K Off-Policy Correction for a REINFORCE Recommender System, with Minmin Chen, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi, was accepted to WSDM 2019.
July 2018 - Categorical-Attributes-Based Item Classification for Recommender Systems, with Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Francois Belletti, and Ed H. Chi, was accepted to RecSys 2018.
May 2018 - Q&R: A Two-Stage Approach Toward Interactive Recommendation, with Konstantina Christakopoulou, Rui Li, Sagar Jain and Ed H. Chi, was accepted to KDD 2018. See you in London!
April 2018 - Our paper The Case for Learned Index Structures was accepted to SIGMOD 2018.
March 2018 - Our paper Factorized Recurrent Neural Architectures for Longer Range Dependence was accepted to AISTATS 2018, and I will be presenting the work there in April.
November 2017 - I will be attending the ML Systems workshop at NIPS to give a talk on our paper The Case for Learned Indexes.
October 2017 - Our paper Latent Cross: Making Use of Context in Recurrent Recommender Systems was accepted to WSDM 2018.
July 2017 - My thesis received the 2017 SIGKDD Doctoral Dissertation Award runner-up!
June 2017 - I will be presenting my recent work, with Jilin Chen, Zhe Zhao, and Ed Chi, at FAT/ML 2017 (at KDD) on fairness properties of adversarial training.
December 2016 - Beyond Globally Optimal: Focused Learning for Improved Recommendations, with Ed Chi, Zhiyuan Cheng, Hubert Pham, and John Anderson, was accepted to WWW 2017.
October 2016 - Recurrent Recommender Networks, with Chao-Yuan Wu, Amr Ahmed, Alex Smola, and How Jing, was accepted to WSDM 2017.
August 2016 - Our paper, FRAUDAR, won the Best Paper Award at KDD!
May 2016 - I have completed my Ph.D. (thesis) and will be joining Google Research in Mountain View, CA!
May 2016 - FRAUDAR: Bounding Graph Fraud in the Face of Camouflage, with Bryan Hooi, Hyun Ah Song, Neil Shah, Kijung Shin, Christos Faloutsos, was accepted to KDD 2016 as a full paper with oral presentation.
December 2015 - BIRDNEST: Bayesian Inference for Ratings-Fraud Detection, with Bryan Hooi, Neil Shah, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija and Christos Faloutsos, was accepted to SDM 2016.
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

Machine Learning Systems