Muhammad Khan

Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan et al.

Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents; understanding where their players are most comfortable can lead them to developing more effective offensive strategies. An automatic tool that can provide these performance profiles in a timely manner can become invaluable for coaches to maximize both the effectiveness of their game plan as well as the time dedicated to practice and other related activities. Additionally, basketball is dictated by many variables, such as playstyle and game dynamics, that can change the flow of the game and, by extension, player performance profiles. It is crucial that the performance profiles can reflect the diverse playstyles, as well as the fast-changing dynamics of the game. We present a tool that can visualize player performance profiles in a timely manner while taking into account factors such as play-style and game dynamics. Our approach generates interpretable heatmaps that allow us to identify and analyze how non-spatial factors, such as game dynamics or playstyle, affect player performance profiles.

Wei Liu, Muhammad Khan, Gabriel Mancino-Ball et al.

Applications such as adversarially robust training and Wasserstein Distributionally Robust Optimization (WDRO) can be naturally formulated as min-sum-max optimization problems. While this formulation can be rewritten as an equivalent min-max problem, the summation of max terms introduces computational challenges, including increased complexity and memory demands, which must be addressed. These challenges are particularly evident in WDRO, where existing tractable algorithms often rely on restrictive assumptions on the objective function, limiting their applicability to state-of-the-art machine learning problems such as the training of deep neural networks. This study introduces a novel stochastic smoothing framework based on the \mbox{log-sum-exp} function, efficiently approximating the max operator in min-sum-max problems. By leveraging the Clarke regularity of the max operator, we develop an iterative smoothing algorithm that addresses these computational difficulties and guarantees almost surely convergence to a Clarke/directional stationary point. We further prove that the proposed algorithm finds an $ε$-scaled Clarke stationary point of the original problem, with a worst-case iteration complexity of $\widetilde{O}(ε^{-3})$. Our numerical experiments demonstrate that our approach outperforms or is competitive with state-of-the-art methods in solving the newsvendor problem, deep learning regression, and adversarially robust deep learning. The results highlight that our method yields more accurate and robust solutions in these challenging problem settings.

Mohamed Hamad, Muhammad Khan, Tamer Khattab et al.

A key challenge in ischemic stroke diagnosis using medical imaging is the accurate localization of the occluded vessel. Current machine learning methods in focus primarily on lesion segmentation, with limited work on vessel localization. In this study, we introduce Stroke Locus Net, an end-to-end deep learning pipeline for detection, segmentation, and occluded vessel localization using only MRI scans. The proposed system combines a segmentation branch using nnUNet for lesion detection with an arterial atlas for vessel mapping and identification, and a generation branch using pGAN to synthesize MRA images from MRI. Our implementation demonstrates promising results in localizing occluded vessels on stroke-affected T1 MRI scans, with potential for faster and more informed stroke diagnosis.

Daya Gaur, Muhammad Khan

We study the design of stochastic local search methods to prove unsatisfiability of a constraint satisfaction problem (CSP). For a binary CSP, such methods have been designed using the microstructure of the CSP. Here, we develop a method to decompose the microstructure into graph tensors. We show how to use the tensor decomposition to compute a proof of unsatisfiability efficiently and in parallel. We also offer substantial empirical evidence that our approach improves the praxis. For instance, one decomposition yields proofs of unsatisfiability in half the time without sacrificing the quality. Another decomposition is twenty times faster and effective three-tenths of the times compared to the prior method. Our method is applicable to arbitrary CSPs using the well known dual and hidden variable transformations from an arbitrary CSP to a binary CSP.