Alex Beutel's Research

• Fairness without Demographics through Adversarially Reweighted Learning
  Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi
  NeurIPS, 2020
• CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
  Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi
  EMNLP, 2020
• Building Healthy Recommendation Sequences for Everyone: A Safe Reinforcement Learning Approach
  Ashudeep Singh, Yoni Halpern, Nithum Thain, Konstantina Christakopoulou, Ed H. Chi, Jilin Chen, Alex Beutel
  FAccTRec, 2020
• Improving Uncertainty Estimates through the Relationship with Adversarial Robustness
  Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
  Preprint, 2020
• Learning to Diversify from Human Judgments: Research Directions and Open Challenges
  Emily Denton, Hansa Srinivasan, Dylan Baker, Jilin Chen, Alex Beutel, Tulsee Doshi, Ed H. Chi
  Fair and Responsible AI Workshop at CHI, 2020
• Measuring Recommender System Effects with Simulated Users
  Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel
  FATES at WWW, 2020
  Video
• Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
  Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel
  ML with Guarantees workshop at NeurIPS, 2019
• Toward a better trade-off between performance and fairness with kernel-based distribution matching
  Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel
  ML with Guarantees workshop at NeurIPS, 2019
• Transfer of Machine Learning Fairness across Domains
  Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi
  AI for Social Good workshop at NeurIPS, 2019
• Fairness in Recommendation Ranking through Pairwise Comparisons
  Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow
  KDD (Applied Data Science Track), 2019
  Video
• Towards Neural Mixture Recommender for Long Range Dependent User Sequences
  Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi
  WWW, 2019
• Top-K Off-Policy Correction for a REINFORCE Recommender System
  Minmin Chen*, Alex Beutel*, Paul Covington*, Sagar Jain, Francois Belletti, Ed H. Chi
  WSDM, 2019
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
  Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi
  AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019
• Counterfactual Fairness in Text Classification through Robustness
  Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel
  AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019
• SageDB: A Learned Database System
  Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Jialin Ding, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan
  CIDR, 2019
  • Press: The Morning Paper
• Lifting the Curse of Multidimensional Data with Learned Existence Indexes
  Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, Ed H. Chi
  ML for Systems workshop at NeurIPS, 2018
• Categorical-Attributes-Based Item Classification for Recommender Systems
  Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi
  RecSys, 2018
• Q&R: A Two-Stage Approach Toward Interactive Recommendation
  Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, Ed H. Chi
  KDD Applied Data Science Track, 2018
• The Case for Learned Index Structures
  Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis
  SIGMOD, 2018
  ML Systems at NeurIPS 2017 North East Database Day 2018 SysML 2018
  • Press: O'Reilly, The Morning Paper, Import AI, Linear Digressions, Blogs
  • Lecture from Stanford EE380
  • Interview on ODBMS.org
• Factorized Recurrent Neural Architectures for Longer Range Dependence
  Francois Belletti, Alex Beutel, Sagar Jain, Ed H. Chi
  AISTATS, 2018
• Latent Cross: Making Use of Context in Recurrent Recommender Systems
  Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, Ed H. Chi
  WSDM, 2018
• The Many Faces of Link Fraud
  Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
  ICDM, 2017
• Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
  Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi
  FAT/ML, 2017
• Graph-Based Fraud Detection in the Face of Camouflage
  Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, Christos Faloutsos
  TKDD, 2017
• Joint Training of Ratings and Reviews with Recurrent Recommender Networks
  Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alex Smola
  ICLR workshop track, 2017
• Beyond Globally Optimal: Focused Learning for Improved Recommendations
  Alex Beutel, Ed H. Chi, Derek Zhiyuan Cheng, Hubert Pham, John Anderson
  WWW, 2017
• Recurrent Recommender Networks
  Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alex Smola, How Jing
  WSDM, 2017
• (2017 SIGKDD Doctoral Dissertation
  Award runner-up) User Behavior Modeling with Large-Scale Graph Analysis
  Alex Beutel
  Ph.D. Thesis, Carnegie Mellon University, 2016
  • Award announcement: CMU DB
• (Best Paper Award) FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
  Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
  KDD, 2016
  • Winner of KDD 2016 Best Paper Award and CogX 2017 Award for Best Student Paper in AI
  • Media: NSF, WESA - Pittsburgh NPR, TechXplore, Stanford Scholar, Crain's
• Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algortihms
  Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, Christos Faloutsos
  TKDE, 2016
• User Behavior Modeling and Fraud Detection
  Alex Beutel, Christos Faloutsos
  IEEE Intelligent Systems: Trends and Controversies, 2016
• BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
  Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
  SDM, 2016
  Preprint
• EdgeCentric: Anomaly Detection in Edge-Attributed Networks
  Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos
  ICDM Workshop on Data Mining for Cyber Security, 2016
  Extended Preprint
  • Used in production at Flipkart.
• Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
  Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
  WWW (poster), 2016
  Extended Preprint
• Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach
  Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
  TKDD, 2016
  Best papers in KDD 2014, Special Issue
• TerraNNI: Natural Neighbor Interpolation on 2D and 3D Grids using a GPU
  Pankaj K. Agarwal, Alex Beutel, Thomas Moelhave
  TSAS, 2016
• A General Suspiciousness Metric for Dense Blocks in Multimodal Data
  Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, Christos Faloutsos
  ICDM, 2015
• Inferring Lockstep Behavior from Connectivity Pattern in Large Graphs
  Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
  KAIS, 2015
• ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
  Alex Beutel, Amr Ahmed, Alexander J. Smola
  WWW, 2015
  Code Extended Preprint
• ND-SYNC: Detecting Synchronized Fraud Activities
  Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Alex Beutel, Christos Faloutsos, Athena Vakali
  PAKDD, 2015
• Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
  Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
  ICDM, 2014
• Elastic Distributed Bayesian Collaborative Filtering
  Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay Narayanan
  NeurIPS Distributed Machine Learning and Matrix Computations workshop, 2014
• (Best Paper Finalist
  in KDD 2014) CatchSync: Catching Synchronized Behavior in Large Directed Graphs
  Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
  KDD, 2014
  Code (by Meng) Code + Data
• Inferring Strange Behavior from Connectivity Pattern in Social Networks
  Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
  PAKDD, 2014
• Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models
  Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing
  AISTATS, 2014
  Appendix Related Presentation
  • Taught in "Machine Learning with Large Datasets" graduate course
    (CMU 10-605/10-805 2014 and 2015)
• FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
  Alex Beutel, Abhimanu Kumar, Evangelos E. Papalexakis, Partha Pratim Talukdar, Christos Faloutsos, Eric P. Xing
  SDM, 2014
  Code Related Presentation
  • Taught in "Machine Learning with Large Datasets" graduate course
    (CMU 10-605/10-805 2014 and 2015)
• CoBaFi: Collaborative Bayesian Filtering
  Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J. Smola
  WWW, 2014
• Detecting Suspicious Following Behavior in Multimillion-Node Social Networks
  Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
  WWW (poster), 2014
• FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
  Alex Beutel, Abhimanu Kumar, Evangelos E. Papalexakis, Partha Pratim Talukdar, Christos Faloutsos, Eric P. Xing
  NeurIPS Big Learning Workshop, 2013
  Code Poster
• CopyCatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks
  Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, Christos Faloutsos
  WWW, 2013
  • 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
• (Selected as one of the best papers
  in ASONAM 2012) Network Anomaly Detection using Co-clustering
  Evangelos E. Papalexakis, Alex Beutel, Peter Steenkiste
  ASONAM, 2012
  • Later published as a book chapter in Springer Encyclopedia of Social Network Analysis and Mining
• Interacting Viruses on a Network: Can both survive?
  Alex Beutel, B. Aditya Prakash, Roni Rosenfeld, Christos Faloutsos
  KDD, 2012
  Presentation
• Winner-takes-all: Competing Viruses on fair-play networks
  B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos
  WWW, 2012
• TerraNNI: Natural Neighbor Interpolation on a 3D Grid Using a GPU
  Alex Beutel, Thomas Moelhave, Pankaj K. Agarwal, Arnold P. Boedihardjo, James A. Shine
  ACM GIS, 2011
  Presentation
• Volumetric Grid Construction using 3D Natural Neighbor Interpolation on the GPU
  Alex Beutel, Thomas Moelhave, Pankaj K. Agarwal
  MASSIVE '11: Proceedings of the Workshop on Massive Data Algorithmics, 2011
• From Point Cloud to 2D and 3D Grids: A Natural Neighbor Interpolation Algorithm using the GPU
  Alex Beutel
  Senior Thesis - Graduation with Highest Distinction, Duke University, 2011
  Paper Presentation Poster
• (Best Paper Award) Natural Neighbor Interpolation Based Grid DEM Construction Using a GPU
  Alex Beutel, Thomas Moelhave, Pankaj K. Agarwal
  ACM GIS, 2010
  Presentation

Other Publications

• Defensive publication: Elastic multi-resolution model-serving to compute inferences, Christopher Olston, Noah Fiedel, Ed H. Chi, Alex Beutel.
• Patent: Detection of lockstep behavior, Alex Beutel, Wanhong Xu. Patent Number 9077744; issued July 7, 2015.

Tutorials

• Responsible Recommendation and Search Systems
  Alex Beutel, Ed H. Chi, Fernando Diaz, Robin Burke
  WWW, 2020
• Graph-Based User Behavior Modeling: From Prediction to Fraud Detection
  Alex Beutel, Leman Akoglu, Christos Faloutsos
  KDD, 2015
  Slides Video
• Fraud Detection through Graph-Based User Behavior Modeling
  Alex Beutel, Leman Akoglu, Christos Faloutsos
  ACM CCS, 2015
  Slides

Talks

• Understanding Recommendations over Time
  SIGIR'20 Workshop on Deep Reinforcement Learning for Information Retrieval, Zoom, July 2020
• Measuring Recommender System Effects with Simulated Users
  FATES, Zoom, April 2020
• Fairness in Recommendation
  Netflix, Los Gatos, CA, November 2019
• Challenges and Progress in Scaling ML Fairness
  AISys at SOSP, Huntsvilla, Ontario, Canada, October 2019
• Putting Fairness Principles into Practice
  Salesforce Research, Palo Alto, CA, August 2019
• Fairness in Recommendation Ranking through Pairwise Comparisons
  FACTS-IR, Paris, FR, July 2019
• Learned Data Systems
  QCon, New York, NY, June 2019
• Putting Fairness Principles into Practice
  University of California at Riverside, Riverside, CA, May 2019
• Putting Fairness Principles into Practice
  QCon.ai, San Francisco, CA, April 2019
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
  AIES, Honolulu, HI, January 2019
• ML for Data Systems
  Stanford EE380 Colloqium, Palo Alto, CA, October 2018
• Q&R: A Two-Stage Approach Toward Interactive Recommendation
  KDD, London, UK, August 2018
• Dynamics and Context in Neural Recommender Systems
  LearnIR Workshop at WSDM, Los Angeles, CA, February 2018
• Latent Cross: Making Use of Context in Recurrent Recommender Systems
  WSDM, Los Angeles, CA, February 2018
• Dynamics and Context in Neural Recommender Systems
  Pinterest, San Francisco, CA, February 2018
• A Machine Learning Approach to Databases Indexes
  ML Systems at NeurIPS, Long Beach, CA, December 2017
• Using Context when Modeling User Behavior: Improving Fraud Detection, Neural Recommenders, and Fairness
  M.I.T., Cambridge, MA, November 2017
• Using Context when Modeling User Behavior: Improving Fraud Detection, Neural Recommenders, and Fairness
  Brown University, Providence, RI, November 2017
• Building Blocks of Neural Networks and Research Applications of RNNs
  Guest Lectures in "Introduction to Topics in Data and Computational Science" (DATA 1030), Providence, RI, November 2017
• Beyond Globally Optimal: Focused Learning for Improved Recommendations
  Google Student Research Summit, Mountain View, CA, September 2017
• Beyond Globally Optimal: Focused Learning for Improved Recommendations
  WWW, Perth, Australia, April 2017
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  Northeastern University, Boston, MA, March 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  Arnhold Institute for Global Health, Mount Sinai School of Medicine, New York, NY, March 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  IOMS, Stern School of Business, New York University, New York, NY, March 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  Google Research, Mountain View, CA, March 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  Microsoft, Redmond, WA, March 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  Georgia Institute of Technology, Atlanta, GA, February 2016
• Beyond Who and What: Answering How and Why by Modeling Large Graphs
  New York University, Courant Institute, New York, NY, February 2016
• ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
  University of Pennsylvania, Philadelphia, PA, November 2015
• Collaborative Bayesian Filtering: Patterns and Methods
  WIN, New York, NY, October 2015
• ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
  WWW, Florence, Italy, May 2015
• Distributed Machine Learning for User Behavior Modeling
  Facebook, New York, NY, May 2015
• Distributed Machine Learning for User Behavior Modeling
  Google Research, New York, NY, May 2015
• SGD on Hadoop for Big Data and Huge Models
  Guest Lecture in "Machine Learning with Large Datasets" (CMU 10-805), Pittsburgh, PA, Spring 2015
• CoBaFi: Collaborative Bayesian Filtering
  WWW, Seoul, South Korea, April 2014
• SGD on Hadoop for Big Data and Huge Models
  Guest Lecture in "Machine Learning with Large Datasets" (CMU 10-805), Pittsburgh, PA, Spring 2014
• SGD on Hadoop for Big Data and Huge Models
  Duke University, Durham, NC, 2014
• CopyCatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks
  WWW, Rio de Janeiro, Brazil, May 2013
• Interacting Viruses on a Network: Can both survive?
  KDD, Beijing, China, August 2012
• TerraNNI: Natural Neighbor Interpolation on a 3D Grid Using a GPU
  ACM GIS, Chicago, IL, November 2011
• Natural Neighbor Interpolation Based Grid DEM Construction Using a GPU
  ACM GIS, San Jose, CA, November 2010

Students Mentored and Advised

• Ashudeep Singh, PhD Student, 2020 (Cornell)
• Ananth Balashankar, PhD Student, 2019 (NYU)
• Preethi Lahoti, Intern, 2019 (MPI)
• Sirui Yao, Intern, 2019 (Virginia Tech)
• Sahaj Garg, Intern, 2018 (Stanford undergraduate)
• Candice Schumann, Intern, 2018 (UMD, next position: Google Research)
• Stephen Macke, Intern, 2018 (UIUC)
• Konstantina Christakopoulou, Intern, 2017 (UMN, next position: Google Research)
• Francois Belletti, Intern, 2017 (UC Berkeley, next position: Google Research)

Code

• ACCAMS - Bayesian Additive Co-Clustering to Approximate Matrices Succintly
• FlexiFaCT - Scaling matrix, tensor, and coupled factorization on stock Hadoop.
• TerraNNI - Interpolate grids from 2D and 3D point clouds on the GPU

Funding

• Facebook Fellowship (2013). See also: fellow page.
• NSF Graduate Research Fellowship Program (2011)
• Yahoo! Faculty Research and Engagement Program (2014) - Helped write research proposal
• NSF Collaborative Grant IIS-1408924 - Helped edit research proposal

Other Projects

• My rarely updated blog
• FlexiFaCT - Scaling matrix, tensor, and coupled factorization on stock Hadoop.
• TerraNNI - Interpolate grids from 2D and 3D point clouds on the GPU
• Gradutrip - Find conferences by location
• Duke Schedulator - Course organizing software for Duke students
• Real-time Voronoi diagrams and natural neighbor queries with WebGL
• Interactive Quadtrees and Well-Separated Pairs Decomposition
• Time Series Twitter
• PitRho - NASCAR data analytics
• Duke Webfiles - Online front-end for AFS.
• SQL Injection - I gave a talk on SQL injection at Duke TechExpo 2009. [Demo from presentation]
• Web Application Security - While working for the Duke IT Security Office as a web application security expert, I contributed to the security standard released by the university.
• 2D Hilbert Curve with HTML5