Faculty Features

Shuchismita Sarkar

Applied Statistics and Operations Research
What most excites you about being at Bowling Green?
BGSU promotes an inclusive learning environment with a focus on public good which matches with my personal philosophy as an academic. I strongly believe in open and respectful exchanges among scholars which leads to smooth transfer of knowledge by creating a space for comfortable curiosity in a diverse community. As a scientist, I am motivated by BGSU's commitment to serve the public interest. My responsibility as a teacher involves leading my students to an engaged and compassionate citizenship in a multicultural global environment. I am confident that going forward BGSU will act as a supporting pillar for achieving my academic goals which also aligns with my personal development plan.
What made you decide to go into your field?
As a young adult, I was interested in not just one, but many fields.. I was interested in astronomy. I was curious about geology and physics. I loved literature. I enjoyed history. I also had an aptitude for social science. Since the Indian educational system didn't allow me to get a flavor of these subjects simultaneously, I chose to do my undergrad in a mathematics based subject which happened to be statistics. Very soon I was intrigued by the unique flavor of this discipline which helps one to find patterns in our every day life and understand the factors controlling them. I also realized that studying this subject is my key towards contributing to the other areas I want to pursue. My field has an extraordinary flexibility of making collaborations with other disciplines and I feel amazed at the prospect of educating myself in a continuous fashion.
Why is your work important?
According to Forbes report, 90% of data of the world was generated in the last two years alone. This emphasizes the need of developing statistical techniques for analyzing and summarizing this colossal amount of data. My research interest is focused in the area of computational statistics and machine learning. In particular, I mostly work on problems that involve finite mixture models and model based clustering. Cluster analysis is a technique of partitioning data points into multiple groups or clusters so that data points within the same group are more similar to each other than the data points belonging to different groups. Most of my research projects involve developing clustering methodologies for high dimensional objects such as matrices and tensors. Recently I have become interested in studying network data as well. Putting aside the technical aspect, my research projects enjoy a wide range of application. Four of my ongoing projects have application in hand written numerals processing, competitive salary determination, international trade and stylometry respectively. My aim is to develop inter- and intra-disciplinary partnership and contribute to the local as well as the global community by exercising my expertise in model based clustering.