Data Science Kick-Off Meeting
Regression via Classification with Neural Networks
Monday, February 17, 2020 · 5:30 - 7 PM
We will have our first MeetUp meeting on February 17 at UMBC University Center, Room 312. Visitors can park for free (after 4:00 pm) at the parking lots marked with black arrows in the event photo. The program is as follows
(5:30 - 6:00 pm) Social
(6:00 - 6:50 pm) Talk: Regression via Classification with Neural Networks
(6:50 - 7:00 pm) Question and Answer Session
Abstract:
With the availability of open-source and easy to use libraries and graphics processing units at affordable prices, researchers from various disciplines of science and engineering are using artificial neural networks to learn from and make predictions on data in various forms. Optical material characterization based on reflectometry (or ellipsometry) data is one of these applications, where deep learning has been implemented to identify two-dimensional (2D) nanostructures and to obtain optical constants of particles, thin films, solutions, tissues, and soils. In this talk, Dr. Ergun Simsek will discuss how optical constants of atomically thin layered materials can be determined from multi-angle reflectometry data using neural networks.
(5:30 - 6:00 pm) Social
(6:00 - 6:50 pm) Talk: Regression via Classification with Neural Networks
(6:50 - 7:00 pm) Question and Answer Session
Abstract:
With the availability of open-source and easy to use libraries and graphics processing units at affordable prices, researchers from various disciplines of science and engineering are using artificial neural networks to learn from and make predictions on data in various forms. Optical material characterization based on reflectometry (or ellipsometry) data is one of these applications, where deep learning has been implemented to identify two-dimensional (2D) nanostructures and to obtain optical constants of particles, thin films, solutions, tissues, and soils. In this talk, Dr. Ergun Simsek will discuss how optical constants of atomically thin layered materials can be determined from multi-angle reflectometry data using neural networks.
