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Applied Mathematics and Statistics

News & Events

[Seminar] Representation Learning for Complex Data and My Experience after Math Major at Stony Brook University

AuthorApplied Mathematics & Statistics REG_DATE2021.10.28 Hits53

Speaker: Dr. Irene Kim  Place: Online via Zoom, Zoom ID: 997 6634 3524 / Passcode: 556273
Time: 

About the speaker
Dr. Irene Kim graduated from the University of California at Davis with Ph.D. in Statistics and graduated from Stony Brook University with Bachelor’s degree in Mathematics. Her research interests are in Machine Learning, deep learning, representation learning, and uncertainty quantification

Abstract
This talk will be divided into two parts where the first part will be on my current research, and the second part will be focused on sharing my experience at Stony Brook University and studying Math/Applied Math as an undergraduate degree.

PART 1: In this talk, I will take an oil reservoir modeling and a history matching problem as an example to address the challenges of modern data analysis. As modern data become more complex and higher dimensional, finding a way to represent the data in a concise and useful form is an important problem. An auto-encoder can be used to find a low dimensional representation for an oil reservoir data and used for history matching problem.

PART 2: Experience at Stony Brook University as a math major, life after graduation, job interview etc.