An Assessment-Informed Collaborative Initiative: Curriculum Mapping for PhD Program Improvement

Faculty and doctoral students of the Department of Urban and Regional Planning (DURP) initiated a review of their PhD program requirements and curriculum. This poster highlights the usefulness of faculty-student collaboration for program assessment to enable program improvement. The assessment coordinator organized a colloquium in which PhD candidates presented their dissertation research. Faculty evaluated the presentations based on the PhD program student learning outcomes (SLOs). Written comments from faculty evaluators were then coded as strengths and weaknesses corresponding to each SLO component to generate a preliminary qualitative assessment. At a subsequent meeting, faculty and doctoral students reviewed the preliminary qualitative assessment, PhD curriculum, core course syllabi, and PhD guidelines. They identified shortcomings in the existing curriculum, mapped degree requirements and program SLOs, and proposed revisions to better align the curriculum with program SLOs. They also outlined next steps toward improving the curriculum such as increasing the number of core course credits and providing more teaching opportunities for PhD candidates through summer school and online courses

Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem

In Statistics, multi-modal and non-smooth likelihood (or, objective function) maximization problems often arise with known upper and lower bound of the parameters. A novel derivative-free global optimization technique is developed to optimize any black-box function on a hyper-rectangular euclidean space. In literature, pattern search technique has been shown to be a powerful tool for blackbox optimization. The proposed algorithm follows the principle of pattern search technique where new updated solution is obtained from the current solution making movements (within the constrained sample space) along the coordinates. Before making a jump from the current solution point to a new solution point, objective function is evaluated in several neighborhood points around the current solution and the best solution point is chosen based on the objective function values at those points. Parallel threading can be used to make the algorithm more scalable. Performance of the proposed method is evaluated based on optimization of upto 5000 dimensional multi-modal benchmark functions. The proposed algorithm is shown to perform upto 40 and 368 times faster compared to Genetic Algorithm (GA) and Simulated Annealing (SA) respectively. The proposed method is used to estimate the optimal biomarker combination from Alzheimer data by maximizing the empirical estimates of area under ROC curve

Topology of Quantum Grey Soliton in Multi-Component Inhomogeneous Bose-Einstein Condensates

We study the dispersion mechanism of Lieb mode excitations of both single and multi component ultra-cold atomic Bose gas, subject to a harmonic confinement through chirp management. It is shown that in some parameter domain, the hole-like excitations lead to the soliton's negative mass regime, arising due to the coupling between chirp momentum and Kohn mode. In low momenta region the trap considerably affects the dispersion of the grey soliton, which opens a new window to observe Lieb-mode excitations. Further, we extend our analysis to binary condensate, which yields usual shape compatible grey-bright soliton pairs. The inter-species interaction induces a shift in the Lieb-mode excitations, where the pair can form a bound state. We emphasize that the present model provides an opportunity to study such excitations in the low momenta regime, as well as the formation of bound state in binary condensate.Comment: 9 pages, 7 figure