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Rho Participates in Innovative Graduate Student Workshop for 6th Consecutive Year

Posted by Brook White on Mon, Aug 01, 2016 @ 03:30 PM


At Rho, we are proud of our commitment to supporting education and fostering innovative problem solving in the next generation of scientists, researchers, and statisticians. One way we have been excited to promote innovation is by participating in the annual Industrial Math/Stat Modeling Workshop for Graduate Students (IMSM) hosted by the National Science Foundation-supported Statistical and Applied Mathematical Sciences Institute (SAMSI).  This summer marked our 6th consecutive year as a Problem Presenter.  We were joined by fellow presenters from Sandia National Laboratories, the US Army Corps of Engineers, the Environmental Protection Agency, the Cooperative Institute for Climate and Satellites, Pfizer Inc., and faculty from North Carolina State University, Clemson University, and the University of Cincinnati.

SAMSI participants 2016

Agustin Calatroni (second from left), Hoang Tran (second from right), and Emily Lei Kang (third from right) with students from the SAMSI program.

For the 2016 workshop, Rho was represented at the workshop by investigators Dr. David Hall, Dr. Herman Mitchell, Agustin Calatroni, and Bioinformatics Intern Hoang Tran, who was a student participant in the 2015 workshop. With the assistance of Dr. Emily Lei Kang from the University of Cincinnati, Rho presented their problem to a team of math and stat graduate students: using peptide microarray data, a highly sensitive procedure which is prone to high variability, identify the ‘global’ set of epitopes within a group of tree nut and peanut allergic sufferers by separating biologically-relevant signals from background noise. The application of this project is to better understand the molecular mechanisms of the interactions between the immune system and food allergens, which can be used to develop immunotherapy-based treatments. The IMSM students decided to address this problem by using Positive Unlabeled learning, a machine learning approach, to predict which signals in the microarray data are noise.

Rho is honored to have the opportunity to work with exceptional students and faculty to apply state of the art mathematical and statistical techniques to solve real world problems and advance our knowledge of human diseases.

You can visit the IMSM Workshop website to learn more about the program, including the problem Rho presented and the students’ solution (page 45).

Ryan2.jpgRyan Bailey, MA is a Senior Clinical Researcher at Rho.  He has over 10 years of experience conducting multicenter asthma research studies, including theInner City Asthma Consortium (ICAC) and the Community Healthcare for Asthma Management and Prevention of Symptoms (CHAMPS) project. Ryan also coordinates Rho’s Center for Applied Data Visualization, which developsnovel data visualizations and statistical graphics for use in clinical trials.


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