Congratulations to two new Masters (Spring 2018)

By | August 14, 2018

Congratulations to the Spring 2018 Masters!

Congratulations to the new Spring 2018 Masters from my research lab!  This announcement is a few months late, nonetheless both of these individuals deserve to be congratulated on their achievements!

Alexander Rodriguez, Masters of Science, Data Science and Analytics

Spring 2018 Masters Thesis - Alexander RodriguezAlexander David Rodriguez completed the Masters of Data Science and Analytics.  I first met Alexander when he took my graduate course in Intelligent Data Analytics as a foreign-exchange undergraduate student from Peru.  He performed so incredible in my course that I immediately started talking with him about the possibility of continuing his graduate studies with me.  After returning to Peru and working one year in industry, he came back and joined the DSA program at OU.  He work for me as both a GTA and an GRA, funded in part by the NIST Center of Excellence on Risk-Based Community Resilience Planning. His MS thesis is entitled “Data-based Stochastic Network Mitigation”.  The abstract follows.

Data-based Stochastic Network Mitigation abstract: Current decision-support frameworks to assist mitigation planning do not include uncertainty and complexity of network failures, either one or both. To close this research gap, this thesis walks through a demonstration of the importance of including uncertainty in the decision analysis to later propose a novel methodology that employs simulation data that encapsulates both uncertainty and complexity of failures modeled by domain experts. Thus, this work is divided in two parts. The first part of this work examines how component importance measures fail to give the necessary intuition for mitigation planning in the light of uncertainty. The analysis is assisted by a novel component importance measure called probabilistic delta centrality that demonstrates how previously neglected stochastic considerations change decisions suggested. In the second part, a new paradigm for stochastic network mitigation is proposed. The approach leverages realizations from scenario event simulations to develop a probabilistic framework that supports constrained decision making. This scenario event simulation framework is capable of comprising component fragilities, correlation among random variables, and other physical aspects that affect component failure probabilities. On the top of that, a statistical learning model is built to enable a rapid estimation of post-disruption impact, which permits a metaheuristic to intelligently explore feasible discrete enhancements from mitigation strategies. The search for near-optimal solutions can be restricted by limited resources and potential political, social, and safety limitations. Two examples are presented to exhibit how this method provides detailed information for mitigation. The level of complexity embedded in search along with its detailed solutions are pioneering in network mitigation planning.

From this work Alexander and I have published one conference paper, and have two journal papers in progress.   Alexander will be pursuing his PhD in Computer Science at Virginia Tech starting Fall 2018.

Yanbin Chang, Masters of Science, Industrial and Systems Engineering

Yanbin completed his Masters of  Industrial and Systems Engineering in the Spring as well.  His MS thesis is entitled “Heuristic approach to network recovery”

Abstract: This study addresses optimization modeling for recovery of a transportation system after a major disaster. In particular, a novel metric based on the shape of the recovery curve is introduced as the objective to minimize.  This metric is computed as the distance from the pre-disaster system performance at a time immediately before disruption to the two-dimensional location of the centroid point of the area beneath the recovery curve. The recovery trajectories derived from optimization models with this new metric are considered along with two other recovery goals from literature, i.e., minimizing the total recovery time and minimizing the skew of the recovery trajectory. A genetic algorithm is implemented to search for optimal restoration schedules under each objective and empirical analysis is used to evaluate the corresponding quality of the solutions. Additionally, a particle swarm optimization algorithm is employed as an alternative metaheuristic and the quality of the recovery schedules, as well as the observed computational efficiency is analyzed.

Yanbin is currently preparing this thesis work for submission as a journal article.  He will begin his PhD in Industrial Engineering at Clemson this Fall as well.

BONUS Material

Brad Osborn, Bachelors of Science, Industrial and Systems Engineering

Brad Osborn, completed his undergraduate in Industrial and Systems Engineering.  While Brad was not officially a part of the OU Analytics Lab, he worked with AT&T as an intern and used some of the skills he mastered in my class ISE 4113 Spreadsheet-based Decision Support Systems to wow his superiors.  They offered him a full-time job and relocated him to Seattle, WA.  However, his relocation is also bittersweet — Brad was a key player on my soccer team “Total Chaos” in the Norman area adult soccer league.  Regardless, I wish you great success at AT&T!