Current Group Members

Current Group Members


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Arpan Seth
PhD Candidate
setha@purdue.edu

Contamination of a drinking water distribution network can severely impact the public health. This work focuses on the modeling of water distribution systems and using these models to design real-time response applications that help in recovery planning during a contamination incident.

 

 


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Yankai Cao
PhD Candidate
cao142@purdu.edu
Co-advised with Prof. J. Hahn

Today, vaccines play an important role in human lives. To address dynamic national requirements for various pharmaceuticals, there is a need for reconfigurable therapeutics manufacturing facilities that are capable of rapid product changeover. Limitations of the existing manufacturing infrastructure are made painfully clear by recurring drug shortages. This research focuses on developing a flexible modeling and nonlinear programming framework for optimizing virus-based production of therapeutics in a reconfigurable facility.

 


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Alberto Jose Benavides-Serrano
PhD Candidate
albertojbs@neo.tamu.edu

Current strategies to place gas sensors in industrial settings are based upon heuristics or semi-quantitative approaches. Optimal sensor placement is difficult due to the large number of unknown variables that influence the risks associated with gas leaks. Heuristic and semi-quantitative approaches can give results that are far from optimal in terms of cost and risk reduction. This research presents adaptations to a sensor placement formulation which incorporate sensor voting schemes and probability of sensor failure. In many instances, release events are not considered detected until multiple detectors acknowledge the release. Therefore, modified formulations must be developed to take into account this need for sensor voting. Additionally, this research considers adaptations to the original sensor placement formulation that account for the imperfect sensor, or the sensor that has some probability of failure.

 


Jia Kang
PhD Candidate
kangjia1127@gmail.com
Co-Advised with Prof. J. Hahn

Large-scale, nonlinear dynamic models frequently arise when describing the physics of important engineering and biological problems. In this research, we are developing new algorithms that exploit concurrent computing architectures to provide efficient parallel solution of large-scale parameter estimation problems, focusing on those with both temporal and spatial structure that can be exploited.

 


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Michael Bynum
PhD Student
bynumm@purdue.edu

Infectious diseases have plagued communities all over the world throughout history, and they continue to kill countless numbers of people every year. Developing dependable models for the spread of infectious diseases can help provide an understanding of such spread and allow evaluation of public health policies. The goal of this research is to use dynamic optimization for parameter estimation of spatio-temporal models of the spread of measles. 

 


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Jianfeng Liu
PhD Student
liu1172@purdue.edu

Parameter estimation for infectious diseases plays an important role in the understanding of disease dynamics and decision-making in public health policies. Generally, parameters of interest are estimated by solving large-scale nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) optimization problems based on discrete- or continous-time disease models. Such problems, however, are very challenging due to their large scale and strong nonlinearity. This research focuses on developing advanced modeling techniques and solution strategies for disease-related optimization problems.

 


Undergraduate Group Members

Chris Hanselman, Chemical Engineering Student (Summer 2014)
Stefanus Winarto, Electrical Engineering Student (Summer 2014)