IACM Colloquium
Speaker: Grigorios Tsagkatakis
Title: Machine Learning for Big Science Data Analysis
Abstract:
A wealth of scientific instruments, from microscopes monitoring the
neuronal activity of the brain to orbiting telescopes trying to estimate
the distribution of dark matter in the universe, is offering
unprecedented views of our cosmos. Understanding the massive amounts of
data and observations requires a rethinking of traditional scientific
data analysis approaches, through the introduction of Big Science Data
analytics. At the same time, the past ten years have witnessed a
revolution in how to understand and infer meaningful patterns from
massive amounts of data through the introduction of data-driven machine
learning models. The aim of this talk is to present a number of
challenges associated with introducing machine learning approaches for
analyzing massive scientific data including topics like handling high
dimensional observations, quantifying uncertainties, and dealing with
data-related issues like class imbalance or missing observations.
Approaches for addressing these challenges will be discussed in the
context of different scientific domains including Earth Observation,
Astrophysics, and Neuroscience.
Short Bio:
Dr. Grigorios Tsagkatakis is an Assistant Professor at the Department of
Computer Science of the University of Crete and an affiliated researcher
with the Institute of Computer Science of FORTH. He received his BEng
and MS degrees in Electronics and Computer Engineering from the
Technical University of Crete, Greece in 2005 and 2007 respectively, and
his Ph.D. in Imaging Science from the Rochester Institute of Technology,
New York, in 2011. His work has been funded by different agencies
including a European Commission Marie Skłodowska-Curie fellowship with
the Department of Electrical and Computer Engineering at the University
of Southern California, as well as projects with ESA and NASA. His
research focuses on topics related to signal/image processing and
machine learning with applications in remote sensing, astrophysics, and
biology.
Time, Date & Location: 15:00, Thursday, 1st of June, 2023
Host: Yannis Pantazis