From particles to bits: The journey of information (Edge computing for radiation detectors)
Radiation and particle detectors let us observe the universe beyond what our senses can achieve. Scientists come up with new ways to collect more and more information about our universe using detectors to transform these particles into electric signals and binary bits. However, the push to make bigger and more precise systems has increased the raw data production exponentially – to the point where the data acquisition systems and storage technologies cannot keep up. To mitigate this problem the analyses algorithms need to be distributed along the data acquisition chain including right up at the edge of the detector as to perform real-time analysis on incoming data, validating relevant information and compressing the overall data volume before it gets to long-term storage. This approach presents several challenges with limited computing power and memory availability. Machine learning and other artificial intelligence techniques have shown good results for physics reconstruction problems. Extending this approach for implementation at the edge can contribute to reducing the data volumes at the source and reduce compute time requirements downstream, diminishing the number of data links, power requirements and cost. Several challenges remain, particularly for the inference validation and tracking as well as obtaining data for training those models.
Building efficient and impactful communications
A large part of a successful scientific career is being able to communicate your research in a clear and efficient way. Everyone can improve their public speaking skills and build more successful oral and poster presentations. The first part will guide you in structuring your message to create an impactful communication using a simple step-by-step process. The second section will address visual supports for both oral and poster presentations and suggest methods to eliminate clutter and highlight the essential elements of your contributions. The last section offers strategies for practicing public speaking and offers advice on presenting both in person and in virtual contexts. With these tools you will be able to build interesting, concise, and efficient presentations that will communicate your work clearly.
Note : This can be given as an interactive workshop (1/2 day) or in a lecture format (90 minutes).
Audrey Corbeil Therrien is an Assistant Professor in the Department of Electrical and Computer Engineering at the Université de Sherbrooke in Québec, Canada. She currently holds the Tier-2 Canada Research Chair in real-time embedded intelligence for ultra-high rate detectors. She has worked at CERN (2015) in Geneva, Switzerland and at the SLAC National Accelerator Laboratory where she held a Banting Fellowship (2018-2020). Her research aims to improve real-time analyses at the edge with the integration of machine learning in high performance radiation instrumentation systems. She is part of several conference committees and since 2019 she is an elected member of the IEEE NPSS RISC Committee. She also completed a program in pedagogy in superior education, with a focus on professionalizing programs and active learning. She has been strongly involved with the promotion of technical careers and teaching to diverse groups for UdeS, SLAC, Stanford University, local non-profits and at several conferences. She received several awards, including the 2017 IEEE Glenn F. Knoll Graduate Educational Grant and the 2019 Best Thesis Award in Science and Engineering at the Université de Sherbrooke.