A Marie Sklodowska-Curie ITN funded by the Horizon2020 program of the European Commission

A week with Nobel Laureates

by Giovanni Banelli

I’m on the plane for Boston, where on Monday I will start my MathWorks internship, i.e. the secondment planned within my AMVA4NewPhysics position. It’s 10PM in the Central European Time, but outside it’s still very bright. Tiredness cannot be hidden. Just this morning I left my hotel in Lindau, where I attended the 2019 Lindau Nobel Laureate Meeting on Physics (Sunday June 30th – Friday July 5th). Continue reading “A week with Nobel Laureates”

A Lecture on Artificial Intelligence in Hamburg

by Tommaso Dorigo

Are you going to be in the Hamburg (Germany) area on July 7th? Then mark the date! The AMVA4NewPhysics and INSIGHTS ITN networks have jointly organized, with the collaboration of the DESY laboratories and the Yandex school of machine learning, a public lecture titled “Artificial Intelligence: past, present, and future“. The lecturer is Prof. Pierre Baldi, from the Center for Machine Learning at the University of California Irvine.
The venue is the auditorium (horsaal) of the Continue reading “A Lecture on Artificial Intelligence in Hamburg”

Q & A on AMVA4NewPhysics

by Tommaso Dorigo

It is a very good thing that the European Commission pays close attention to document the work of the projects that benefited of its funding. So, for instance, the AMVA4NewPhysics network has been described, along with its goals, in a 2016 article on the Horizon magazine.

Since the network is nearing the end of its lifetime, I was asked to provide some information for an update of the above article. I think it is useful to share the questions and my own answers below. Of course, the mentions I made below of some of the network output are only a partial representation, and are Continue reading “Q & A on AMVA4NewPhysics”

Accelerating the search for Dark Matter with Machine Learning

by Tommaso Dorigo

The topic of algorithms that may dramatically improve our statistical inference from collider data is dear to my heart, and has been so since at least two decades (my first invention, which has now become what is called the “inverse bagging” algorithm, is dated 1992, when nobody even knew what bagging was). But now _everybody_ appears to be interested in the topic, and that means all of my particle physics and astroparticle physics colleagues.

A way to gauge the interest of the community on this topic is the number of gatherings to discuss advancements in the field and their impact in experimental research in fundamental science. If I look back at just the past few months, Continue reading “Accelerating the search for Dark Matter with Machine Learning”

Journey through Fast.AI: II – Columnar data

by Giles Strong

Welcome back to the second part of my journey through the Fast.AI deep-learning course; beginning section here. Last time I gave an example of analysing images, now I’ll move on to working with columnar data.

Columnar data is a form of structured data, meaning that the features of the data are already extracted (in this case into columns), unlike in images or audio where features must be learnt or carefully constructed by hand. Continue reading “Journey through Fast.AI: II – Columnar data”

Journey through Fast.AI: I – Introduction and image data

by Giles Strong

For the past few months I’ve been following the Fast.AI Deep-Learning for Coders course. An online series of lectures accompanied with Jupyter notebooks and python library built around PyTorch. The course itself is split into two halves: the first uses a top-down approach to teach state of the art techniques and best practices for deep learning in order to achieve top results on well established problems and datasets, with later lessons delving deeper into the code and mathematics; the second half deals with more with the cutting edge of deep learning, and focuses on less-well-founded problems, such as generative modelling, and recent experimental technologies which are still be developed. Continue reading “Journey through Fast.AI: I – Introduction and image data”

Advanced Results in Lisbon

by Tommaso Dorigo

This week the VII AMVA4NewPhysics workshop is under way in the premises of LIP in Lisbon. During these events the network gets together to discuss the status of the various projects, plan future events and activities, take action on arisen issues, and vote on budget and other topics. But this is a special event in the lifetime of the network, as we are getting toward the mature stage – we are in the  Continue reading “Advanced Results in Lisbon”

Science in the sun: AMVA4NP’s summer events

by Giles Strong

Summer 2018’s been a busy time for the AMVA4NewPhysics network; we’ve had workshops, outreach events, training sessions, meetings, and many more things. I wanted to go through and pick out a few thinks I was involved in. Continue reading “Science in the sun: AMVA4NP’s summer events”

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