The Waterloo Institute for Nanotechnology (WIN) has four main thematic research areas; Smart and Function Materials, Connected Devices, Next Generation Energy Systems and Therapeutics and Theranostics. To showcase the work going on within these areas, we will be holding monthly WIN Thematic Seminars featuring our members and their research group members. For the second event in the series, Professor Vassili Karanassios from the Connected Devices theme will be giving a seminar on "Applications of Artificial Intelligence (AI) for Spectral Interference Correction in optical emission spectrometry and of Machine Learning for development of nano-materials for sustainable energy".
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Applications of Artificial Intelligence (AI) for Spectral Interference Correction in optical emission spectrometry and of Machine Learning for development of nano-materials for sustainable energy
In this presentation, two topics will be covered. First, the use of Artificial Neural Networks (ANNs) for spectral interference correction using a lab-scale optical spectrometer and of Deep Learning for spectral interference correction using a portable optical spectrometer and a battery-operated microplasma will be discussed. And second, the application of Machine Learning (ML) for the development of nano-materials for sustainable energy will be described.
Vassili Karanassios is a Professor of Chemistry at the University of Waterloo (Ontario, Canada) and a co-founder of a degree program in nanotechnology engineering at the same University. Professor Karanassios received his Ph. D. from the University of Alberta (Edmonton, Canada) and was a Post Doctoral Fellow at McGill University (Montreal, Canada). In 2009, he held a Leverhulme award in the UK where he was a visiting Professor in Chemistry (Sheffield University), an Overseas Fellow of Churchill College (Cambridge University, UK), and a visiting Professor of Engineering (Cambridge University, UK) in the Center for Advanced Photonics and Electronics (CAPE).
Professor Karanassios and his group published (among others) on microfluidics and nanofluidics, on 3D printing and on rapid prototyping, on battery-operated microplasmas, on spectral interference correction using Artificial Neural Networks (ANNs) and Deep Learning, and on smartphone-enabled data acquisition and signal-processing from a variety of sensors for on-site chemical analysis and (potentially) for IoT applications.