"I applied to the scholars program because I was already interested in the subject, and applying to the scholars program would give me recognition from the University and some financial compensation."
Industrial and Systems Engineering
My primary academic interests lie in the fields of Optimization and Supply Chain.
Academic and Other Awards
- University Scholars Program Scholarship (2011-2012)
- Institute of Industrial Engineers
- Institute for Operations Research and Management Sciences
Hobbies and Interests
- Reading, and going to the gym.
A Greedy Randomized Adaptive Search Procedure (GRASP) for the Non Unique Probe Selection Problem (NUPSP)
A probe is a segment of DNA or RNA labeled with a radioactive isotope, dye, or enzyme used to find a specific target sequence on a DNA molecule by hybridization. Selecting unique probes through hybridization experiments is a difficult task, especially when targets are very similar. Non-unique probes are probes that bind to more than one target sequence. They can be utilized when unique probes are not able to accurately provide the results of the hybridization experiments. The non-unique probe selection problem aims to select a minimum set of probes that determines the presence or absence of targets in biological sample in order to minimize the use of resources required to identify a virus in the sample. To avoid experimental errors, each pair of targets should be distinguished by at least d_min probes. These requirements are called separation constraints. On the other hand, coverage constraints require that each target has at least c_min probes that will hybridize to it. Common approaches fix the thresholds for the separation and coverage constraints to values higher than 1. Several methods have been proposed to find optimal solutions for the NUPSP, including greedy heuristics, evolutionary strategies, cutting plane algorithms. We propose a Greedy Randomized Adaptive Search (GRASP) for this problem. GRASP is an iterative randomized search technique proposed for the first time in 1989 by Feo and Resende. Each GRASP iteration consists of two phases: the first constructs an initial solution through an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in order to improve it. GRASP has been successfully applied to many optimization problems, since it is able to find good solutions in a reasonable amount of time. The results seem promising and comparable to the state-of-the-art methods.