Xi Wu |
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I am a software engineer at Google. My current primary interest is the interplay of AI and databases for knowledge extraction and representation. I am also interested in building higher-level query foundations for multi-granular data analysis. Our work in data analytics was highlighted in Future of Data Science, Google Cloud Next 2025. In addition to my core work, I also conduct research in machine learning and trustworthy AI. Grained Relational Algebra (GRA). GRA is a minimal, fully compatible extension to relational algebra designed for multi-grain data analytics. Analytical data naturally lives at multiple granularities, yet SQL's data model has no container that holds multiple relations at different grains as a single composable result. This forces users to either join into one flat table — causing silent fan-out errors — or manage separate tables that do not compose. GRA addresses this structural gap by introducing the Grained Relation Space (GRS): a principled, schema-level container that organizes relations by their dimensional footprint rather than ad-hoc table names. Within a GRS, each relation retains its native grain, operators produce a GRS as output (algebraic closure), and grain-aware analysis models (GAMs) encapsulate multi-grain computation as reusable, composable modules. The result is grain safety by construction: if a required grain is absent, the system produces an explicit error rather than a silent wrong answer. GRA has been implemented and deployed at Google as a query service, serving hundreds of internal teams at scale. For the formal foundations, see our paper on arXiv. Prior to Google, I received my Ph.D. in Computer Science from the University of Wisconsin-Madison, advised by Jeffrey F. Naughton and Somesh Jha. I was awarded a Google PhD Fellowship in 2016. |
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Publications |
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Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, Somesh Jha ICML 2024, arXiv 2023 |
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Stratified Adversarial Robustness with Rejection
Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha ICML 2023, arXiv 2023, AAAI Workshop on Adversarial Machine Learning and Beyond 2022 (Oral Presentation and Best Paper Award) (code) |
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The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha ICLR 2023 (Spotlight) (code) |
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Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha ICLR 2022, arXiv 2021 (code) |
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Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha NeurIPS 2021, arXiv 2021 (code) |
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ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang and Somesh Jha ECML 2021, arXiv 2020, ICML UDL 2020 (code) |
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DIFF: A Relational Interface to Large-Scale Data Explanation Firas Abuzaid, Peter Kraft, Sahhana Suri, Edward Gan, Eric Xu, Atul Shenoy, Avsin Anathanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia The VLDB Journal (2021), VLDB 2019 (Invited to "Best of VLDB 2019" Special Issue) |
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Concise Explanations for Neural Networks using Adversarial Training Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu ICML 2020, arXiv 2018 (code) |
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Robust Attribution Regularization Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha NeurIPS 2019, arXiv 2019 (code, slides, poster, Alta Cognita) |
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Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha EuroS&P 2019, arXiv 2018 (code) |
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Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Xi Wu, Jeffrey F. Naughton, Jignesh M. Patel SIGMOD 2019, arXiv 2017 |
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Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha ICML 2018, arXiv 2017 (slides) |
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Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton SIGMOD 2017, arXiv 2016 (slides) |
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Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning
Uyeong Jang, Xi Wu, Somesh Jha ACSAC 2017 |
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A Study of Stability in Data Privacy
Advisors: Jeffrey F. Naughton, Somesh Jha Ph.D. Thesis, UW-Madison, August 2016 ProQuest |
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A Methodology for Modeling Model-Inversion Attacks Xi Wu, Matthew Fredrikson, Somesh Jha, Jeffrey F. Naughton CSF 2016 (slides) |
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami S&P (Oakland) 2016, arXiv 2015 |
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A Completeness Theory for Polynomial (Turing) Kernelization with Danny Hermelin, Stephan Kratsch, Karolina Soltys, Magnus Wahlstrom Algorithmica 2015, IPEC 2013 |
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Uncertainty Aware Query Execution Time Prediction Wentao Wu, Xi Wu, Hakan Hacigümüs, Jeffrey F. Naughton VLDB 2014 |
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Weak Compositions and Their Applications to Polynomial Lower Bounds for Kernelization with Danny Hermelin SODA 2012, ECCC 2011 (slides) |
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COREMU: A Scalable and Portable Parallel Full-system Emulator Zhaoguo Wang, Ran Liu, Yufei Chen, Xi Wu, Haibo Chen, Binyu Zang PPoPP 2011 |
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Extended Islands of Tractability for Parsimony Haplotyping with Rudolf Fleischer, Jiong Guo, Rolf Niedermeier, Johannes Uhlmann, Yihui Wang, Mathias Weller CPM 2010 |
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Experimental Study of FPT Algorithms for the Directed Feedback Vertex Set Problem with Rudolf Fleischer, Liwei Yuan ESA 2009 (slides) |
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Control Flow Obfuscation with Information Flow Tracking Haibo Chen, Liwei Yuan, Xi Wu, Bo Huang, Pen-chung Yew, Binyu Zang MICRO 2009 |
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From Speculation to Security: Practical and Efficient Information Flow Tracking using Speculative Hardware Haibo Chen, Xi Wu, Liwei Yuan, Binyu Zang, Pen-chung Yew, Frederic T. Chong ISCA 2008 |
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Manuscripts |
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Multi-Relational Algebra for Multi-Granular Data Analytics
Xi Wu, Eugene Wu, Zichen Zhu, Fengan Li, Jeffrey F. Naughton arXiv 2025 |
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Towards Adversarial Robustness via Transductive Learning
Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, Yingyu Liang, Somesh Jha arXiv 2021 |
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Robust Out-of-distribution Detection for Neural Networks Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha arXiv 2020 |
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Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani arXiv 2020 |
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Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu arXiv 2019 |
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Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
Xi Wu, Matthew Fredrikson, Wentao Wu, Somesh Jha, Jeffrey F. Naughton arXiv 2015 |
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