Tal Wagner (original) (raw)

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I am a senior applied scientist in Amazon AWS. Before that I was a postdoc in the Machine Learning Foundations group at Microsoft Research Redmond (2020-2022), and obtained my PhD in Computer Science at CSAIL, MIT in 2020, advised by Piotr Indyk.

My research interests are in designing algorithms for massive datasets and large-scale machine learning, especially in the following contexts:

Email: firstname (dot) lastname (at) gmail (dot) com

Updates

[ **recent** | all ]

| | (Apr'23) | Our work on fast differentially private KDE is accepted to ICML'23 as an oral presentation. | | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | | (Sep'22) | Our work on exponentially improved WL simulation with GNNs is accepted to NeurIPS'22. | | | (Aug'22) | I joined Amazon AWS as a Senior Applied Scientist. | | | (Jul'22) | I spoke at the workshop on Quantitative Geometry of Transportation Metrics in the AMS-EMS-SMF 2022 meeting. | | | (May'22) | Our work on support-aware histograms is accepted to ICML'22. |

| | (May'22) | Our work on generalization bounds for numerical linear algebra is accepted to COLT'22. | | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | (May'22) | Attending the Workshop on Algorithms with Predictions (ALPS 2022) in EPFL. | | | (Jan'22) | Our work on learning-augmented motif counting on graph streams is accepted to ICLR'22. | | | (Nov'21) | Our work on optimal metric compression is accepted to the SIAM Journal on Computing. | | | (Nov'21) | Honored to have been selected for the Rising Stars in Data Science workshop at the University of Chicago. | | | (Sep'21) | Our work on data-driven algorithms for low rank approximation is accepted to NeurIPS'21. | | | (May'21) | Our work on faster kernel matrix algebra is accepted to ICML'21. | | | (Jan'21) | Our work on learning-based support size estimation is accepted to ICLR'21 as a spotlight. | | | (Sep'20) | I joined the Machine Learning Foundations group in MSR Redmond as a postdoc. | | | (Aug'20) | I completed my PhD in MIT. | | | (Jul'20) | I spoke at the "Are we ready for semi-supervised learning for big data? From theory to practice" minisymposium at SIAM Imaging Science 2020. | | | (Jul'20) | We presented our work on scalable nearest neighbor search for optimal transport in ICML'20. Here is the video. |

Publications

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