Alex Beutel's Research
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Deliberative Alignment: Reasoning Enables Safer Language Models
..., , , , , , , , , , , , , Alex Beutel,Preprint, 2024
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Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning
...Alex Beutel, , ,NeurIPS 2024 workshop on Red Teaming GenAI: What Can We Learn from Adversaries?, 2024
- Blog post
- Press: MIT Technology Review
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First-Person Fairness in Chatbots
..., Alex Beutel, , , Anna-Luisa Brakman, , Meghan Shah, , ,Preprint, 2024
- Blog post
- Press: MIT Technology Review
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Rule Based Rewards for Language Model Safety
..., , , , , , , Alex Beutel, ,NeurIPS, 2024
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The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
..., , , , , Alex BeutelPreprint, 2024
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Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
..., , , , , Alex BeutelTMLR, 2024
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Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images
..., , , , , Alex Beutel, ,FAccT, 2024
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Practices for Governing Agentic AI Systems
..., , , Steven Adler, Cullen O’Keefe, Rosie Campbell, Teddy Lee, , , Alan Hickey, Katarina Slama, , Paul McMillan, Alex Beutel, ,White paper, 2023
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Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
...Lucas Monteiro Paes, , Alex Beutel, Flavio P. Calmon,IEEE Journal on Selected Areas in Information Theory, 2024
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Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
..., , , , , Alex Beutel,NeurIPS, 2023
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Controlled Decoding from Language Models
..., Jong Lee, , YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, , , , Alex Beutel,ICML, 2024
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Improving Diversity of Representation in Large Language Models via Collective-Critiques and Self-Voting (CCSV)
..., Nick Blumm, , Ragha Kotikalapudi, , , , , , Alex Beutel,EMNLP, 2023
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Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
..., , , , , , , Alex BeutelFindings of EMNLP, 2023
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Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning
..., , , , , , Alex BeutelPreprint, 2023
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Learning From Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
...Yueqi Wang, , Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao BiRecSys Industry Track, 2023
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Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning
..., Swaroop Mishra, , Alex Beutel,Neural Conversational AI Workshop at ICML, 2023
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Towards A Scalable Solution for Compositional Multi-Group Fair Classification
...James Atwood, Tina Tian, , Meghana Deodhar, , Alex Beutel, ,The Second Workshop on Spurious Correlations, Invariance and Stability at ICML, 2023
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Towards Robust Prompts on Vision-Language Models
..., , , Alex Beutel, Philip Torr,Preprint, 2023
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What are effective labels for augmented data? Improving robustness with AutoLabel
..., , , , Alex BeutelIEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023
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Striving for data-model efficiency: Identifying data externalities on group performance
..., , Alex Beutel,Trustworthy and Socially Responsible Machine Learning (TSRML) workshop at NeurIPS, 2022
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Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
..., , , , Pierre Kremp, Nicholas Blumm, Susan Wang, , , , , Alex BeutelPreprint, 2022
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Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
..., , , , Alex Beutel,NeurIPS, 2022
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(Best Paper Award)
A Human-ML Collaboration Framework for Improving Video Content Reviews
...Meghana Deodhar, , Yixin Cai, Alex Koes, Alex Beutel,CIKM Workshop on Human-in-the-Loop Data Curation, 2022
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Flexible text generation for counterfactual fairness probing
..., , , Alex Beutel, ,Sixth Workshop on Online Abuse and Harms (WOAH) at ACL, 2022
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Improving Calibration through the Relationship with Adversarial Robustness
..., , Alex Beutel,NeurIPS, 2021
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Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
..., , , , , Alex BeutelEMNLP, 2021
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Understanding and Improving Fairness-Accuracy Trade-offs in Multi-task Learning
...Yuyan Wang, , Alex Beutel, , ,KDD, 2021
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Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
..., , Nick Blumm, Aditee Kumthekar, Trevor Potter, , , , , Alex BeutelAAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021
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Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
..., , Elaine Le, , , Alex Beutel, , ,TheWebConf, 2021
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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
..., Alex Beutel, Matthaus Kleindessner, ,FAccT, 2021
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Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
..., , , , , , Alex BeutelWSDM, 2021
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Enhancing Neural Recommender Models through Domain-Specific Concordance
..., Alex Beutel,WSDM, 2021
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Underspecification Presents Challenges for Credibility in Modern Machine Learning
..., , Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, , Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, , Max Vladymyrov, , , Steve Yadlowsky, Taedong Yun, Xiaohua Zhai,JMLR, 2021
- Press: MIT Technology Review
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Learned Indexes for a Google-scale Disk-based Database
..., , Alex Beutel, , , , Xiaozhou, Li, Andy Ly,ML for Systems workshop at NeurIPS, 2020
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Measuring and Reducing Gendered Correlations in Pre-trained Models
..., , , Alex Beutel, , , , ,Preprint, 2020
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Fairness without Demographics through Adversarially Reweighted Learning
..., Alex Beutel, , Kang Lee, , , ,NeurIPS, 2020
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CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
...Tianlu Wang, , , , Kang Li, , Alex Beutel,EMNLP, 2020
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Building Healthy Recommendation Sequences for Everyone: A Safe Reinforcement Learning Approach
..., , , , , , Alex BeutelFAccTRec, 2020
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Learning to Diversify from Human Judgments: Research Directions and Open Challenges
..., , Dylan Baker, , Alex Beutel, ,Fair and Responsible AI Workshop at CHI, 2020
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Measuring Recommender System Effects with Simulated Users
..., , , , Kang Lee, , , , Alex BeutelFATES at WWW, 2020
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Toward a better trade-off between performance and fairness with kernel-based distribution matching
..., Hai Qian, Qiuwen Chen, , , Alex BeutelML with Guarantees workshop at NeurIPS, 2019
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Transfer of Machine Learning Fairness across Domains
..., , Alex Beutel, , Hai Qian,AI for Social Good workshop at NeurIPS, 2019
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Fairness in Recommendation Ranking through Pairwise Comparisons
...Alex Beutel, , , Hai Qian, , Yi Wu, , , , ,KDD (Applied Data Science Track), 2019
- Incorporated in a large-scale production recommender (Google Research's 2019 Year in Review).
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Towards Neural Mixture Recommender for Long Range Dependent User Sequences
..., Francois Belletti, , , Alex Beutel, Can Xu,WWW, 2019
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Top-K Off-Policy Correction for a REINFORCE Recommender System
..., Alex Beutel*, , , Francois Belletti,WSDM, 2019
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Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
...Alex Beutel, , , Hai Qian, , Christine Luu, Pierre Kreitmann, Jonathan Bischof,AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019
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Counterfactual Fairness in Text Classification through Robustness
..., , , , , Alex BeutelAAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019
- Improved production classifiers of toxicity in online content (Google Research's 2019 Year in Review).
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SageDB: A Learned Database System
..., , Alex Beutel, , Jialin Ding, Ani Kristo, Guillaume Leclerc, , Hongzi Mao, Vikram NathanCIDR, 2019
- Press: The Morning Paper
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Lifting the Curse of Multidimensional Data with Learned Existence Indexes
..., Alex Beutel, , , ,ML for Systems workshop at NeurIPS, 2018
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Categorical-Attributes-Based Item Classification for Recommender Systems
..., , , , Alex Beutel, Francois Belletti,RecSys, 2018
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Q&R: A Two-Stage Approach Toward Interactive Recommendation
..., Alex Beutel, , ,KDD Applied Data Science Track, 2018
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The Case for Learned Index Structures
..., Alex Beutel, , ,SIGMOD, 2018
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Factorized Recurrent Neural Architectures for Longer Range Dependence
...Francois Belletti, Alex Beutel, ,AISTATS, 2018
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Latent Cross: Making Use of Context in Recurrent Recommender Systems
...Alex Beutel, , , Can Xu, Jia Li, Vince Gatto,WSDM, 2018
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The Many Faces of Link Fraud
..., , Alex Beutel,ICDM, 2017
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Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
...Alex Beutel, , ,FAT/ML, 2017
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Graph-Based Fraud Detection in the Face of Camouflage
..., , , Alex Beutel, ,TKDD, 2017
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Joint Training of Ratings and Reviews with Recurrent Recommender Networks
..., , Alex Beutel,ICLR workshop track, 2017
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Beyond Globally Optimal: Focused Learning for Improved Recommendations
...Alex Beutel, , , Hubert Pham, John AndersonWWW, 2017
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Recurrent Recommender Networks
..., , Alex Beutel, , How JingWSDM, 2017
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(2017 SIGKDD Doctoral Dissertation
Award runner-up) User Behavior Modeling with Large-Scale Graph Analysis...Alex BeutelPh.D. Thesis, Carnegie Mellon University, 2016- Award announcement: CMU DB
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(Best Paper Award)
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
..., , Alex Beutel, , ,KDD, 2016
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Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algortihms
..., Alex Beutel, , , ,TKDE, 2016
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User Behavior Modeling and Fraud Detection
...Alex Beutel,IEEE Intelligent Systems: Trends and Controversies, 2016
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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
..., , Alex Beutel, , , , Disha Makhija,SDM, 2016
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EdgeCentric: Anomaly Detection in Edge-Attributed Networks
..., Alex Beutel, , , , Disha Makhija, ,ICDM Workshop on Data Mining for Cyber Security, 2016
- Used in production at Flipkart.
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Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
..., Alex Beutel, ,WWW (poster), 2016
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(Best papers in KDD 2014,
Special Issue) Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach..., , Alex Beutel, ,TKDD, 2016 -
TerraNNI: Natural Neighbor Interpolation on 2D and 3D Grids using a GPU
..., Alex Beutel,TSAS, 2016
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A General Suspiciousness Metric for Dense Blocks in Multimodal Data
..., Alex Beutel, , , ,ICDM, 2015
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Inferring Lockstep Behavior from Connectivity Pattern in Large Graphs
..., , Alex Beutel, ,KAIS, 2015
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ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
...Alex Beutel, ,WWW, 2015
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ND-SYNC: Detecting Synchronized Fraud Activities
...Maria Giatsoglou, Despoina Chatzakou, , Alex Beutel, , Athena VakaliPAKDD, 2015
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Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
..., Alex Beutel, ,ICDM, 2014
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Elastic Distributed Bayesian Collaborative Filtering
...Alex Beutel, , , , Vijay NarayananNeurIPS Distributed Machine Learning and Matrix Computations workshop, 2014
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(Best Paper Finalist
in KDD 2014) CatchSync: Catching Synchronized Behavior in Large Directed Graphs..., , Alex Beutel, ,KDD, 2014 -
Inferring Strange Behavior from Connectivity Pattern in Social Networks
..., , Alex Beutel, ,PAKDD, 2014
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Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models
..., Alex Beutel, ,AISTATS, 2014
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Taught in "Machine Learning with Large Datasets" graduate course
(CMU 10-605/10-805 2014 and 2015)
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Taught in "Machine Learning with Large Datasets" graduate course
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FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
...Alex Beutel, , , , ,SDM, 2014
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Taught in "Machine Learning with Large Datasets" graduate course
(CMU 10-605/10-805 2014 and 2015)
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Taught in "Machine Learning with Large Datasets" graduate course
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CoBaFi: Collaborative Bayesian Filtering
...Alex Beutel, , ,WWW, 2014
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Detecting Suspicious Following Behavior in Multimillion-Node Social Networks
..., , Alex Beutel, ,WWW (poster), 2014
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FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
...Alex Beutel, , , , ,NeurIPS Big Learning Workshop, 2013
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CopyCatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks
...Alex Beutel, Wanhong Xu, , Christopher Palow,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
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(Selected as one of the best papers
in ASONAM 2012) Network Anomaly Detection using Co-clustering..., Alex Beutel,ASONAM, 2012- Later published as a book chapter in Springer Encyclopedia of Social Network Analysis and Mining
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Interacting Viruses on a Network: Can both survive?
...Alex Beutel, , ,KDD, 2012
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Winner-takes-all: Competing Viruses on fair-play networks
..., Alex Beutel, ,WWW, 2012
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TerraNNI: Natural Neighbor Interpolation on a 3D Grid Using a GPU
...Alex Beutel, , , Arnold P. Boedihardjo, James A. ShineACM GIS, 2011
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Volumetric Grid Construction using 3D Natural Neighbor Interpolation on the GPU
...Alex Beutel, ,MASSIVE '11: Proceedings of the Workshop on Massive Data Algorithmics, 2011
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From Point Cloud to 2D and 3D Grids: A Natural Neighbor Interpolation Algorithm using the GPU
...Alex BeutelSenior Thesis - Graduation with Highest Distinction, Duke University, 2011
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(Best Paper Award)
Natural Neighbor Interpolation Based Grid DEM Construction Using a GPU
...Alex Beutel, ,ACM GIS, 2010
Other Publications
- Pattent Application: Systems and Methods for Performing Automatic Label Smoothing of Augmented Training Data, Yao Qin, Alex Beutel, Ed Huai-Hsin Chi, Xuezhi Wang, Balaji Lakshminarayanan.
- 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
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Responsible Recommendation and Search Systems
Alex Beutel, , ,
WWW, 2020 -
Graph-Based User Behavior Modeling: From Prediction to Fraud Detection
Alex Beutel, ,
KDD, 2015 -
Fraud Detection through Graph-Based User Behavior Modeling
Alex Beutel, ,
ACM CCS, 2015
Teaching
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My Journey to Responsible ML
Guest Lecture in "Introduction to the Design & Analysis of Algorithms" (Duke University CS 330), Durham, NC, March 2023 -
Putting Fairness Principles into Practice
Guest Lecture: Data Mining (Penn State IST557), Zoom, Fall 2019 -
Building Blocks of Neural Networks and Research Applications of RNNs
Guest Lectures in "Introduction to Topics in Data and Computational Science" (Brown DATA 1030), Providence, RI, November 2017 -
SGD on Hadoop for Big Data and Huge Models
Guest Lecture in "Machine Learning with Large Datasets" (CMU 10-805), Pittsburgh, PA, Spring 2015 -
SGD on Hadoop for Big Data and Huge Models
Guest Lecture in "Machine Learning with Large Datasets" (CMU 10-805), Pittsburgh, PA, Spring 2014
Keynotes
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Practical Robustness
AACL 2023 Workshop on The ART of Safety: Workshop on Adversarial testing and Red-Teaming for generative AI, Virtual, November 2023 -
Understanding and Improving Recommenders for All
KDD 2022 Workshop on Data Science and Artificial Intelligence for Responsible Recommendations (DS4RRS), Washington D.C., August 2022 -
Building and Understanding Recommenders for Long-Term User Experiences
2nd International Workshop on Online and Adaptive Recommender Systems at KDD, Washington D.C., August 2022 -
Understanding Recommendations over Time
SIGIR'20 Workshop on Deep Reinforcement Learning for Information Retrieval, Zoom, July 2020 -
Challenges and Progress in Scaling ML Fairness
AISys at SOSP, Huntsvilla, Ontario, Canada, October 2019 -
Dynamics and Context in Neural Recommender Systems
LearnIR Workshop at WSDM, Los Angeles, CA, February 2018
Invited Talks
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Toward Safe and Robust AGI
2024 Netflix Workshop on Personalization, Recommendation and Search (PRS), Los Gatos, CA, May 2024 -
Practical Robustness
NeurIPS 2023 R0-FoMo Workshop: Robustness of Few-shot and Zero-shot Learning in Foundation Models, New Orleans, LA, December 2023 -
Building ML for All
Duke University, Distinguish CS Alumni Lecture, Durham, NC, March 2023 -
Building and Understanding Recommenders for Long-Term User Experiences
2021 SIGIR Workshop On eCommerce, Zoom, July 2021 -
Building and Understanding Recommenders for Long-Term User Experiences
Twitter, Zoom, May 2021 -
Building and Understanding Recommenders for Long-Term User Experiences
Spotify, Zoom, April 2021 -
Fairness in Recommendation
Netflix, Los Gatos, CA, November 2019 -
Putting Fairness Principles into Practice
Salesforce Research, Palo Alto, CA, August 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 -
ML for Data Systems
Stanford EE380 Colloqium, Palo Alto, CA, October 2018 -
Dynamics and Context in Neural Recommender Systems
Pinterest, San Francisco, CA, February 2018 -
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 -
Beyond Globally Optimal: Focused Learning for Improved Recommendations
Google Student Research Summit, Mountain View, CA, September 2017 -
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
University of Pennsylvania, Philadelphia, PA, November 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
Duke University, Durham, NC, 2014
Other Talks
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Measuring Recommender System Effects with Simulated Users
FATES, Zoom, April 2020 -
Fairness in Recommendation Ranking through Pairwise Comparisons
FACTS-IR, Paris, FR, July 2019 -
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
AIES, Honolulu, HI, January 2019 -
Q&R: A Two-Stage Approach Toward Interactive Recommendation
KDD, London, UK, August 2018 -
Latent Cross: Making Use of Context in Recurrent Recommender Systems
WSDM, Los Angeles, CA, February 2018 -
A Machine Learning Approach to Databases Indexes
ML Systems at NeurIPS, Long Beach, CA, December 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 -
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 -
CoBaFi: Collaborative Bayesian Filtering
WWW, Seoul, South Korea, April 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, Intern, 2020 (Cornell)
- Ananth Balashankar, PhD Student, 2019-2021 (NYU), Award for NYU's Best Computer Science Dissertation
- Preethi Lahoti, Intern, 2019 (MPI)
- Sirui Yao, Intern, 2019 (Virginia Tech)
- Sahaj Garg, Intern, 2018 (Stanford undergraduate, next position: Luminous Computing)
- Candice Schumann, Intern, 2018 (UMD, next position: Google Research)
- Stephen Macke, Intern, 2018 (UIUC, next position: Facebook)
- Konstantina Christakopoulou, Intern, 2017 (UMN, next position: Google Research)
- Francois Belletti, Intern, 2017 (UC Berkeley, next position: Google Research)
Code
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
Some code from random assignments and work can be found on my Github account.