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Randall Balestriero is an Assistant Professor of Computer Science at Brown University. His research focuses on learnable signal processing, particularly parametrized wavelets and deep wavelet transforms. His work has applications in areas such as NASA's Mars rover for marsquake detection. In 2016, he joined Rice University as a PhD student under Professor Richard Baraniuk, where he broadened his scope to explore Deep Networks from a theoretical perspective, employing affine spline operators. This effort led to improvements in state-of-the-art methods, such as batch normalization. After receiving his PhD, he joined Meta AI Research (FAIR) as a postdoc under Professor Yann LeCun, where he expanded his research interests to include self-supervised learning and biases in emerging data-augmentation regularization. As of 2023, he is working at GQS, Citadel, focusing on providing AI solutions for highly noisy nonstationary financial time-series prediction and representation learning. With over a decade of experience in both academic and industry settings, he aims to develop practical solutions aligned with sound theoretical principles.
Meta AI, FAIR • New York City, NY, USA
Conducted research in self-supervised learning and data augmentation.
Department: Department of Economics