Min Roh

Research Scientist

Min Roh

Research Scientist


Min Roh has a Ph.D. in Computer Science, as well as a Master of Science in Computer Science from the University of California, Santa Barbara. She also has dual Bachelor of Science degrees, in Computer Science and Mathematics, from the University of British Columbia. Min’s thesis and graduate research focused on numerical methods for discrete stochastic systems with an emphasis on rare event probability estimation. She has also done sensitivity analysis and developed graph-theory related applications for companies doing work in pharmaceuticals, biotechnology, and even real-time geospatial information retrieval. In her role as a research scientist in IDM’s Applied Math team, Min is developing new stochastic compartmental algorithms that will efficiently simulate epidemiological systems.

Biography

Min Roh has a Ph.D. in Computer Science, as well as a Master of Science in Computer Science from the University of California, Santa Barbara. She also has dual Bachelor of Science degrees, in Computer Science and Mathematics, from the University of British Columbia. Min’s thesis and graduate research focused on numerical methods for discrete stochastic systems with an emphasis on rare event probability estimation. She has also done sensitivity analysis and developed graph-theory related applications for companies doing work in pharmaceuticals, biotechnology, and even real-time geospatial information retrieval. In her role as a research scientist in IDM’s Applied Math team, Min is developing new stochastic compartmental algorithms that will efficiently simulate epidemiological systems.

Publications

Friday, November 25, 2016

SParSE++ is an event-oriented stochastic parameter search algorithm that features various leaping methods and improved interpolation over the existing algorithm SParSE.​

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Saturday, November 8, 2014
With recent increase in affordability and accessibility of high-performance computing (HPC), the use of large stochastic models has become increasingly popular for its ability to accurately mimic the
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