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Mar 22, 2024 · Michael Mahoney is a leading expert on the mathematics of data, such as matrix, graph, and optimization algorithms, and their applications to big data analysis. He is also involved in various projects and initiatives related to data science, such as the FODA Institute, the RandNLA framework, and the NeurIPS Best Paper Award.
A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. MW Mahoney, WL Jorgensen. The Journal of chemical physics 112 (20), 8910-8922. , 2000.
Michael “Mike” Mahoney is Chief Executive Officer of Boston Scientific Corporation and Chairman of the company’s Board of Directors. Boston Scientific is a global medical technology leader with approximately $12.7 billion in annual revenue and commercial representation in more than 130 countries.
Research Areas. Statistical Computing. Applications in the Physical and Environmental Sciences. Applications in the Social Sciences. High Dimensional Data Analysis. Artificial Intelligence/Machine Learning.
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AI and Memory Wall,Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs,Chronos: Learning the Language of Time Series,Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning,Multi-scale Local Network Structure Critically Impacts Epidemic Spread and Interventions,An LLM Compiler for Parallel Function Calling,Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training,DMLR: Data-centric Machine Learning Research -- Past, Present and Future,Gated Recurrent Neural Networks with Weighted Time-Delay Feedback,Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems,Randomized Numerical Linear Algebra: A Perspective on the Field With an Eye to Software,Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes,Learning from learning machines: a new generation of AI technology to meet the needs of science,Long Expressive Memory for Sequence Modeling,Noisy Feature Mixup,Inexact Newton-CG Algorithms With Complexity Guarantees,Sparse sketches with small inversion bias,HAWQV3: Dyadic Neural Network Quantization,A Statistical Framework for Low-bitwidth Training of Deep Neural Networks,Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism,PyHessian: Neural Networks Through the Lens of the Hessian,Exact expressions for double descent and implicit regularization via surrogate random design,LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data,HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks,Trust Region Based Adversarial Attack on Neural Networks,Parameter Re-Initialization through Cyclical Batch Size Schedules,On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent,The Mathematics of Data,Lectures on Randomized Numerical Linear Algebra,Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization,Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior,(click herefor a blog about this paper)LASAGNE: Locality And Structure Aware Graph Node Embedding,Avoiding communication in primal and dual block coordinate descent methods,Feature-distributed sparse regression: a screen-and-clean approach,Multi-label learning with semantic embeddings,Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data,Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent,A Local Perspective on Community Structure in Multilayer Networks,Optimal Subsampling Approaches for Large Sample Linear Regression,Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction,Michael Mahoney is the president, CEO and chairman of Boston Scientific, a medical device company. He was also the worldwide chairman of DePuy, a Johnson & Johnson division. He is on the board of Baxter International and ranked #17 on the 2019 Forbes Innovative Leaders list.
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Michael Mahoney is a professor of statistics and computer science at UC Berkeley. His web page lists his recent and past talks and presentations on various topics related to machine learning, randomized numerical linear algebra, dynamical systems, and more.
