Portfolio item number 1
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portfolio
Portfolio item number 2
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publications
Robust Data-Driven Accelerated Mirror Descent
Published in IEEE International Conference on Acoustics, Speech and Signal Processing, 2023
Data-driven mirror descent with input-convex neural networks
Published in SIAM Journal on Mathematics of Data Science, 2023
Provably convergent plug-and-play quasi-newton methods
Published in SIAM Journal on Imaging Sciences, 2024
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Published in Transactions on Machine Learning Research, 2024
Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals
Published in Research in Mathematical Sciences, 2024
talks
Data-driven Optimization via Mirror Descent
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Part of the joint seminar series with City University of Hong Kong and Mathematical Institute at University of Oxford.
Deterministic Sampling with Wasserstein Proximals Permalink
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Poster presentation. Poster pitch can be found here
Data-Driven Geometry for Convex Optimization
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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