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.
Presented by the IEEE Ottawa Women In Engineering (WIE) Affinity Group:
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.
Speaker 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.
Presented by the IEEE Ottawa Section Electromagnetic Compatibility (EMC) Society:
Package Requirements for data rates of 112 Gbps and Beyond
By Dr. Wendem Beyene, Analog and Mixed Signal Architect, Facebook Inc.
Date: Wednesday, June 2, 2021. Time: Online: 5:00 p.m. – 6:30 p.m. EST Location: Online via ZOOM. To register, please reply to the below e-mail. Event Contact: Dr. Syed Bokhari, Chairman, IEEE Ottawa EMC chapter
(Syed.Bokhari@fidus.com)
Abstract
As the data rates increase rapidly in highspeed systems-- such as SerDes and memory systems-- to meet the bandwidth growth intensified by various applications, the electrical performance of packages has become critical. The bump and BGA or pin assignments, the layer stack up, and package material selection are very important to meet the signal and power integrity requirements. In addition, the role of new emerging 2.5D and 3D IC packaging platforms with ever increasing system integration requirements have made the role of packaging even more important. The sources of signal loss, noise coupling and discontinuities in packages must be fully understood and minimized when designing packages. At the same time, the design and development of packages have to meet cost, performance, form factor and reliability goals. In this talk we will examine the key electrical characteristics: signal loss, signal crosstalk, return loss, mode conversion, power integrity and other important factors necessary to meet the performance requirements of high-speed systems with data rates of 112 Gbps and beyond.
Speaker Bio
Dr. Wendemagegnehu (Wendem) T. Beyene (M'88–SM'05) was born in Addis Ababa, Ethiopia. He received the B.S. and M.S. degrees in electrical engineering from Columbia University, New York, NY, USA, in 1988 and 1991, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign, USA, in 1997. In the past, he was employed by IBM, Hewlett-Packard, and Agilent Technologies. In 2000, he joined Rambus Inc., Los Altos, CA, USA, and served as a senior principal engineer responsible for signal integrity of multi-gigabit parallel and serial interfaces. During 2017-2020 he served as principle engineer with responsible for signal and power integrity analysis of high-performance FPGA including fabric and high-speed I/O subsystems as well as I/O modeling. In 2020 he joined Facebook as a Analog & Mixed-Signal Architect in Facebook Reality Lab. Dr. Beyene has authored or co authored numerous refereed publications in various leading IEEE Transactions and conferences. These publications covered various disciplines including package and interconnect modeling, analysis and optimization. He is currently an Associate Editor of IEEE Trans. On CPMT and is a Senior Member of Institute ofElectrical and Electronic Engineers (IEEE). He also serves on several leading technical program committees, including EPEPS and SPI.
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