Tutorial speakers

Jelena Ninkovic is Head of the Semiconductor Laboratory of the Max Planck Society. She has been working on silicon detector technologies and semiconductor devices since 2005. She studied physics at the University of Belgrade and obtained her PhD from the Technical University of Munich (TUM).

Her research activities span a broad range of semiconductor detector technologies, from sensor development and device characterization to detector integration and large-scale detector production. At the Semiconductor Laboratory, she is involved in the development of advanced silicon detectors and semiconductor devices for applications in particle physics, astrophysics, and photon science. Her work focuses on low-noise detector systems, excellent spectroscopic performance, radiation hardness, and high-precision particle physics tracking. This includes technologies such as Silicon Drift Detectors (SDDs), DEPFET sensors, pnCCDs, strip and pixel detectors, as well as fast timing devices such as LGADs. Through these activities, she contributes to international research collaborations and next-generation detector systems across multiple scientific fields.

Abstract:

This tutorial will provide an overview of silicon detector technologies used in modern scientific instrumentation. After a short introduction to the operating principles of semiconductor detectors, the tutorial will focus on the main silicon device types, including pad and strip detectors, Silicon Drift Detectors (SDDs), pnCCDs, DEPFET sensors, and fast timing detectors such as LGADs, together with their typical applications, operational principles, advantages, and limitations.

Tommaso Dorigo is an experimental particle physicist who works for the INFN in Padova. In the past 10 years Dorigo has directed his attention to the use of AI techniques for fundamental science, founding the MODE Collaboration for that purpose. He presently is a RECAT guest Professor at Lulea University of Technology, where he collaborates with computer scientists on neuromorphic computing applications to particle detector development. With LTU Dorigo participates in the EIC-PATHFINDER winning project « PHINDER », which will use nanophotonics and neuromorphic computing for ultrafast, ultra-energy-efficient preprocessing of light signals from calorimeters and other devices.

Abstract (given remotly):

In 2012 a true revolution took place as deep neural networks surpassed humans in image classification tasks. The same year marks a transition in fundamental physics, as for the first time a discovery was enabled by machine learning techniques. A new revolution is about to take place as new AI tools are now enabling the end-to-end optimization of full experiments, including hardware, software, and their interplay.

In this lecture we will discuss the problem of optimizing the design of experiments in fundamental physics, using AI techniques. We will look in detail at a few recent examples of medium complexity, to illustrate the problems faced with these holistic optimization techniques, and their potential.