STT graduate students expand their skills at IMSI internships
Graduate students from the Department of Statistics and Probability – Andrews Boahen and Rachita Mondal – both spent their summer as interns with the Institute for Mathematical and Statistical Innovation (IMSI) at the University of Illinois Urbana-Champaign.
Andrews' internship involved developing sparse structure-aware inference methods for brain connectomics research, with a particular focus on building a Python pipeline for Network-Based Statistics (NBS) and its variants as part of a Fragile X syndrome study.
His work involved developing network-based statistics (NBS) pipelines for multiple testing, working with fMRI data, and data pre-processing. He also read and discussed network-based statistics papers with labmates in neuroscience and bioengineering, joined collaborative lab meetings, and presented his weekly progress.
Andrews said that his work in multiple testing and statistical inference, along with computational statistics, at MSU were the skills that came up most directly during his internship.
“This internship opened my interest in applying my methods to the neuroscience community, Andrews said. “I've become fascinated by learning more about the brain, and I'm currently thinking about how to build a brain digital twin by drawing on my surrogate modeling and uncertainty quantification background for neuroscience research.”
Rachita's internship focused on statistical computing, bioinformatics, data visualization, and scalable methods for analyzing genomic sequence data. In particular, she worked with SARS-CoV-2 sequence data and explored ways to summarize genetic variation over time.
Her work involved developing computational and statistical methods for analyzing large viral sequence datasets. In addition, she worked on efficient methods for computing genetic similarities, visualizing how viral sequences change over time, and developing hierarchical clustering approaches that can handle large numbers of sequences.
"This experience strengthened my interest in developing statistical and computational methods for complex, large-scale scientific data," she said. "It also gave me the opportunity to work on interdisciplinary problems, which is something I would like to continue pursuing in my future research career."
Rachita said that her training in statistics, statistical computing, and high-dimensional data analysis at MSU was especially useful during the internship. She also relied heavily on programming, problem-solving, and the ability to translate statistical ideas into computational methods that can work with large datasets.
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