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Informs Student Presentations

Join us as two of our very own officers, Nan Jiang and Maede Maftouni, present their research Monday February 1st at 1:00 pm. Abstracts are below.

Presenter: Nan Jiang

Title: ALSO-X is Better Than CVaR: Convex Approximations for Chance Constrained Programs Revisited Abstract: This paper studies and generalizes the ALSO-X, originally proposed by Ahmed, Luedtke, SOng, and Xie (2017), for solving a chance constrained program (CCP). We first show that the ALSO-X resembles a bilevel optimization, where the upper-level problem is to find the best objective function value and enforce the feasibility of a CCP for a given decision from the lower-level problem, and the lower-level problem is to minimize the expectation of constraint violations subject to the upper bound of the objective function value provided by the upper-level problem. This interpretation motivates us to prove that when uncertain constraints are convex in the decision variables, ALSO-X always outperforms the CVaR approximation. We further show (i) sufficient conditions under which ALSO-X can recover an optimal solution to a CCP; (ii) an equivalent bilinear programming formulation of a CCP, inspiring us to enhance ALSO-X with a convergent alternating minimization method (ALSO-X+); (iii) extensions of ALSO-X and ALSO-X+ to solve distributionally robust chance constrained programs (DRCCPs) under Wasserstein ambiguity set. Our numerical study demonstrates the effectiveness of the proposed methods.


Presenter: Maede Maftouni

Title: A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis (Joint work with Andrew Chung Chee Law, Bo Shen, Yangze Zhou, and under supervision of Dr. Zhenyu (James) Kong)

Abstract: The use of efficient computer-aided medical diagnosis has never been more critical, given the global extent of COVID-19 and the consequent depletion of hospital resources. Artificial intelligence (AI) powered COVID-19 detection can facilitate an early diagnosis of this highly contagious disease and further reduce the infectivity and mortality rates. The preferred imaging option for COVID-19 screening and diagnosis is computed tomography (CT). However, there is an inevitable inter- and intra-observer variability when using CT scans for diagnosis leading to label noise. Label noise can significantly impact the performance of deep learning models. We present a robust COVID-19 classifier on lung CT scan images with noisy labels by proposing an ensemble deep learning model of pretrained Residual Attention and DenseNet architectures. This method's novelty is that those two deep networks consolidate each other by focusing on complementary, attention-aware, and global sets of features. Additionally, we have built a large and nationally diverse COVID-19 CT scan dataset by curating open-source datasets to improve our classifier’s generalizability. Extensive experimental evaluations on our dataset illustrate the improved performance of our ensemble model over the base-learners.

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