09:00 - 09:25 EDT
Schedule June 2-3
* All times are based on Canada/Eastern EDT.
09:00
Canada/Eastern
09:25
Canada/Eastern
09:30
Canada/Eastern
09:30 - 10:15 EDT
Balcon du Foyer du Mont-Orford
Exploring the benefits of machine learning in design-fabrication cycle of integrated photonics
Yuri Grinberg - Senior Research Officer, Data Science for Complex Systems, Digital Technologies Research Center, National Research Council Canada Abstract Machine learning and deep learning in particular have been under a spotlight for a number of years due to their ability to effectively identify complex patterns in data and leverage them in multitude of ways across domains and applications. In this seminar, we will look at different problems in integrated photonics and the way they leverage diverse machine learning problem formulations. Finding commonalities among redundant solutions in a large design space, speeding up the design optimization process, and infusing fabrication considerations into this process are among the use cases that will be discussed. Finally, we will wrap up with a few key points that should be considered in the early stages of research to create impactful and practical demonstrations of machine learning in the field. Biography Dr. Yuri Grinberg is a Senior Research Officer within the Digital Technologies Research Center. He obtained his PhD in Computer Science from McGill University in 2014 and was an NSERC Postdoctoral fellow in Ottawa Hospital Research Institute before joining NRC. His expertise is applied and theoretical machine learning and reinforcement learning. In the last several years he has lead the development of AI techniques for the design of photonic components, leveraging a variety of computational methods borrowed from optimization and machine learning fields. In particular, his work focuses on development of methods to improve efficiency, reduce the footprint, and improve fabrication tolerance of photonic components without requiring major human efforts. He has co-authored over 50 peer-reviewed publications.
10:15
Canada/Eastern
11:00
Canada/Eastern
11:00 - 12:00 EDT
Balcon du Foyer du Mont-Orford
Lighting the way forward: inspiring careers in optics and photonics
Panel discussion to inspire upcoming graduates about the world of work in photonics and optics “skills, expectations and advice for a smooth transition”. Angeles Camacho - Coractive & Optica Ambassador Devika Nair - Ansys Lumerical Adrien Longa - DistriQ
13:35
Canada/Eastern
13:35 - 13:55 EDT
Balcon du Foyer du Mont-Orford
Statistical Noise Modelling for THz images
Rejeena Radhika Sebastian, PhD student, ÉTS Montréal Thesis Superviser: Prof. Francois Blanchard Abstract : Although noise may appear random, it often follows specific statistical distribution functions. This project aims to uncover the underlying distribution functions of noise in terahertz (THz) images and establish mathematical correlations between them to enhance image denoising. THz imaging has demonstrated remarkable potential in fields such as material characterization, security screening, and biomedical imaging, but noise remains a significant barrier to achieving high-resolution and reliable images. This work introduces the first statistical treatment of noise in THz imaging using deep neural networks (DNNs). We employed Noise2Clean (N2C) and Noise2Noise (N2N) models to identify and model various noise distributions, including additive noises such as Gaussian, impulse, and Poisson, as well as multiplicative noises like Bernoulli, gamma, and exponential. The noise distributions were derived by iterating through multiple statistical functions, with experimental methods defining the boundaries of these functions. The resulting noise models were used for image denoising, achieving substantial improvements in image quality, with peak signal-to-noise ratio (PSNR) enhancements of 8 dB in the N2C model and 6 dB in the N2N model.
13:55
Canada/Eastern
14:15
Canada/Eastern
14:35
Canada/Eastern
14:55
Canada/Eastern
15:15
Canada/Eastern
15:35
Canada/Eastern
