Research Scientist at Google
I am a
Before graduate school, I majored in computer science and physics at. While there, I worked with and on computational geometry for terrain modeling.
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.
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.
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.
CopyCatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks
Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, Christos Faloutsos
- ACM Computing Review Editor's Highlight on CopyCatch
- Patent by Facebook (Patent Number 9077744)
- Discussion by Facebook
- Included in courses at Carnegie Mellon and University of Florida
(Best Paper Finalist
in KDD 2014) CatchSync: Catching Synchronized Behavior in Large Directed Graphs
Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
Code (by Meng) Code + Data
(Best Paper Award)
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
, , Alex Beutel, , ,
Latent Cross: Making Use of Context in Recurrent Recommender Systems
Alex Beutel, , Sagar Jain, Can Xu, Jia Li, Vince Gatto,
Recurrent Recommender Networks
, , Alex Beutel, , How Jing
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Alex Beutel, Amr Ahmed, Alexander J. Smola
Code Extended Preprint
Machine Learning Systems
The Case for Learned Indexes
, Alex Beutel, , ,
arXiv, ML Systems at NIPS, 2017
FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
Alex Beutel, Abhimanu Kumar, Evangelos E. Papalexakis, Partha Pratim Talukdar,
Christos Faloutsos, Eric P. Xing
Code Related Presentation
Taught in "Machine Learning with Large Datasets" graduate course
(CMU 10-605/10-805 2014 and 2015)
- Taught in "Machine Learning with Large Datasets" graduate course
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models
Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing
Appendix Related Presentation