Selected Presentations
- 2020 edition of Data Science Summer School – due to Covid-19 held virtually in January 2021; (Title: Optimization Challenges in Adversarial Machine Learning)
- 12th OPT Workshop on Optimization for Machine Learning; (Title: Adaptation and Universality in First Order Optimization)
- Tradeoffs in primal-dual optimization (paper1, paper2)
- A totally unimodular view of structured sparsity (paper1, paper2, paper3)
- Optimizing a time-data tradeoff via model-based excessive gap (paper1, paper2)
- Composite Self-concordant Minimization (paper1, paper2, paper3)
- Barrier Smoothing for Nonsmooth Convex Minimization (paper)
- MAP Estimation for Bayesian Mixture Models with Submodular Priors (paper)
- Model-based Sketching and Recovery with Expanders (paper)
- Tractability of Interpretability via Selection of Group-Sparse Models (paper)
- To Convexify or Not ? Regression with Clustering Penalties on Graphs (paper)
- Manifold Sparse Beamforming (paper)
- Scalable and Accurate Quantum Tomography via Sparse Projections onto the Simplex (paper1, paper2)
- A Multipath Sparse Beamforming Method (paper)
- Structured sparse acoustic modeling for speech separation (paper)
- Learning Non-Parametric Basis Independent Models from Point-Queries via Low-rank Methods (paper1,paper2)
- IC Research Seminar (September, 2012)
- Sparse Euclidean projections onto convex sets (paper)
- Combinatorial Selection and Least Absolute Shrinkage via The CLASH Operator (paper)
- Game Theory Meets Compressed Sensing (paper)
- Online Performance Guarantees for Sparse Recovery (paper)
- Approximate Message Passing for Bilinear Models (paper)
- Computational Methods for Structured Sparse Component Analysis of Convolutive Speech Mixtures (paper)
- An ALPS’ view of Sparse Recovery (paper)
- Submodular Dictionary Selectionfor Sparse Representation (paper)
- Recovery of Clustered Sparse Signals from Compressive Measurements (paper)
- Model-based Compressive Sensing (paper)
- Compressible priors for high-dimensional statistics (paper)
- Sensing via Dimensionality Reduction (paper)
- Source Localization on a Budget (paper)