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PhD candidate Ganguli receives 2022 ICSDS travel award to Italy

Arkaprabha Ganguli, STT PhD candidate, received a 2022 IMS International Conference on Statistics and Data Science student travel award to present his work “Feature selection integrated deep learning for ultrahigh dimensional and highly correlated feature space.” In recognition of this award, his name appeared in the first page of the 2022 October/November issue of the IMS Bulletin https://imstat.org/wp-content/uploads/2022/07/Bulletin51_7.pdf .

Ganguli’s paper, joint work with Dr. Taps Maiti, STT MSU Foundation Professor, has been accepted for the student travel award at the upcoming 2022 ICSDS conference to be held in Florence, Italy, December 13-16, 2022.

“As a mentor, it is my great honor and pleasure to see Arka win a student award from IMS. This award is in recognition of his novel work in data science, focusing on high-dimensional, model-free dimension reduction, and feature selection problems, “ said Dr. David Todem, Professor in the Department of Epidemiology and Biostatistics.  “Although this work is motivated by important research questions in neuroimaging and neurodegeneration, his methods have wider applications to various domain science investigations, including genetics. Arka is a very motivated and independent student with a great love of learning. It really has been a joy for me to work and interact with him during this early stage of his research career. I am really looking forward to seeing his career develop with important contributions to the field of statistical learning.”

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods and applications in data science.

“I am honored to receive this travel award from IMS. I sincerely thank the IMS committee members for their kind consideration,” said Ganguli. “Also, I’d like to express my sincere thanks to my advisors Dr. Maiti and Dr. Todem for their immense support and encouragement. In this work, we developed a deep learning integrated feature selection method which will be applicable in modern high-dimensional datasets. As a budding researcher, I am very excited about this conference as I will get the opportunity to present my work to a broader audience as well as to meet some of the renowned statisticians whose works are the main reference for my research. ”