Abstract In the digital age, the amount of worldwide data that is being generated on a daily basis is rapidly growing, reaching 175 zettabytes by 2025. These massive volumes of data have led to growing interest in using Machine Learning (ML) algorithms to extract valuable insights from databases. ML techniques can be considered as the foundation of a broad spectrum of next-generation technologies, including medical applications. In this presentation, the role of ML in medical applications will be discussed. A newly developed data-driven classification algorithm will be explained, and its performance for the classification of biological datasets will be investigated and compared with the well-known classification models. Bio Behnaz Fakhar Firouzeh is in the final semester of her Ph.D. in Electrical and Computer Engineering at Carleton University. She has been working on signal processing and Compressive Sensing (CS) for over 8 years. She also has 5 years of experience in developing constraint optimization algorithms. Her developed algorithms successfully have been applied in different areas such as signal processing, Machine Learning, and artificial intelligence. Behnaz has (co)authored several articles in different journals and conference proceedings. Register

Abstract: Data is everywhere and can lead to incredibly valuable insights when harnessed correctly. From automating daily tasks to discovering breakthrough science, to making predictions about the future, utilizing data with machine learning is a fast and ever-evolving field of technology. In this talk, Kelsey will discuss her experiences and challenges using machine learning for space applications, focusing on uses for satellites orbiting Earth, how we can better understand our Sun and its interaction with our atmosphere, and how we can incorporate machine learning into future Mars rover missions. About the speaker: Kelsey Doerksen is a Space Systems Engineer in satellite operations at Planet, a San Francisco-based company that operates the world's largest Earth Observation satellite constellation. In her role, she is responsible for maintaining the health and productivity of 100s of satellites daily and develops software to automate operations and detect anomalous satellite behavior. Kelsey holds a bachelor's degree from Carleton University in Aerospace Engineering: Space Systems Design and a Master's in Electrical and Computer Engineering from the University of Western Ontario. She researches with the Paris Observatory in the fields of Space Weather and Space Debris and has previously interned at the NASA Jet Propulsion Lab with the Machine Learning and Instrument Autonomy Group. Kelsey is beginning her Ph.D. in the Autonomous Intelligent Machines and Systems program at the University of Oxford this Fall, where she will be utilizing machine learning and Earth Observation imagery to research the impacts of climate change on our planet.   Register  

Join us as we discuss career transitions - from undergrad to graduate school, graduation to academia, and graduation to industry - in an open roundtable forum. Hear each of our panel members share an overview of their research area and why they chose it. We are proud to announce that Dr. Hala As’ad, Applied Signal Processing, Audio & Speech Processing, Machine Learning, Deep Learning will be representing us. Everyone is welcome. Amazon & Tim Horton’s gift cards to be won! Register