Early Career Scientist Machine Learning Seminar Tomorrow

[Gaps]

Hi GAPS,

We are happy to invite you to the latest talk in our monthly Early Career Scientist seminar series! This month’s talk will take place tomorrow, March 19th, at 11 AM in the MOB Theory Room for those of you onsite (light snacks will be provided) and on Zoom for those that aren’t (information listed below). The seminar is structured as a 40 minute talk followed by 15 minutes for you to ask any and all questions you might have for the speaker.

The seminars will initially focus on machine learning and its connections with the physics we do here at TRIUMF. This month we are happy to have Dr. Luigi Favaro as our speaker. Dr. Favaro is a postdoctoral researcher at UCLouvain where he focuses on the development and deployment of neural networks for fast detector simulations.

More information about the talk and zoom coordinates can be found below. See you on tomorrow!

Your GAPS, on behalf of the Early Career Talks organizing committee

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Speaker: Dr. Luigi Favaro (remote)

Title: Speeding-up discoveries at the LHC

Abstract: All the recent discoveries in science have the inclusion of machine learning elements. HEP is no exception, where the vast amount of collected data, as at the LHC, and first-principled simulations provide a unique playground for modern machine learning. In particular, through developments in generative AI, we can tackle compelling questions like detecting anomalies, fast detector simulation, and unfolding. Optimal solutions to these problems require knowledge and novel ideas — established in statistics — tailored to the physics domain. A fundamental requirement is the usage of data in all its complexity. I will discuss several applications to the above tasks grounded on these principles. In particular, I will focus on some of the unique challenges we face in science: validating neural networks, defining optimal observables, and embedding symmetries.

Zoom: https://ubc.zoom.us/j/64809533070?pwd=4oFrJ0dVtocSMBKQvgv1BtPbSd7VDf.1
Meeting ID: 648 0953 3070
Passcode: 588513


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