Biography
Arka Ganguli is a Postdoctoral Researcher at Argonne National Laboratory, specializing in statistical machine learning and its application to nuclear physics. He holds a Ph.D. in Statistics from Michigan State University, where his research focused on advanced statistical methodologies for feature selection in ultra-high dimensional datasets. Arka’s academic background includes a Bachelor’s and Master’s degree in Statistics from the University of Calcutta, India. His research interests revolve around developing and applying statistical methods for analyzing high-dimensional datasets, encompassing feature selection, deep learning, and generative models.
Awards, Honors, and Memberships:
- NSF MSGI Fellow at Argonne National Laboratory, May-August 2022.
- Presented at various conferences and seminars, including ENAR conference, JSM conference, and NSF MSGI Summer Research Symposium.
- Received the student travel award from the Institute of Mathematical Statistics (IMS) for presenting at ICSDS conference 2022 in Florence, Italy.
- Received the graduate travel award from Michigan State University for presenting research at international conferences.
- Recipient of the INSPIRE Scholarship 2012-2017 provided by the Ministry of Science and Technology, Government of India.
Publications:
- “Nonparametric Scanning for Nonrandom Missing Data With Continuous Instrumental Variables” (by A. Ganguli, D. Todem) - under revision, https://arxiv.org/abs/2111.09429 .
- “Dynamic modeling of practice effects across the healthy aging-Alzheimer’s disease continuum” (by A. Bender, A. Ganguli, M. Meiring, B. Hampstead, C. Driver) - published in Frontiers in Aging Neuroscience. https://www.frontiersin.org/articles/10.3389/fnagi.2022.911559/full
- “Feature Selection Integrated Deep Learning for Ultrahigh Dimensional and Highly Correlated Feature Space” (by A. Ganguli, D. Todem, T. Maiti) - submitted, https://arxiv.org/abs/2209.07011.