Choose timezone
Your profile timezone:
Powered by Indico
v3.3.3
High Energy Physics is a domain of knowledge which looks for tiny signals of new physics in multi-petabyte scale data. After the discovery of the Higgs boson during the past decade, Machine Learning (ML) and Artificial Intelligence (AI) techniques have percolated all domains of particle physics with a rapid rise in the number of new ideas and publications. Similar data-intensive challenges also arise in astroparticle physics, where large-scale observatories detect rare cosmic messengers such as neutrinos, gamma rays, and cosmic rays to probe astrophysical phenomena in the high-energy universe. In the last decade, all the techniques at the frontier of the ML like graph neural networks (GNN), topological networks, physics-informed neural networks(PINN), Pareto conditioned network (PCN) and quantum informed neural networks(QINN) are completely changing the way we look at data.ย The ML4HEP (Machine Learning for High Energy Physics) school series is an initiative focused on statistical methods and advanced machine-learning applications in HEP and astroparticle physics, though the concepts and algorithms used are general enough to be applicable in other domains of the natural sciences. The series began at ICTS Bengaluru in 2023, followed by editions at IOP Bhubaneswar in 2024 and IISER Kolkata in 2025.ย
These schools have provided a stimulating environment for PhD students, postdocs, and young researchers from India with a strong emphasis on hands-on training in tools such as Python, ROOT, Keras/TensorFlow, PyTorch, and modern ML methods, including CNNs, RNNs, GANs, GNNs, transformers, differentiable programming and more. Each edition typically follows a structured format consisting of an online pre-school for foundational concepts, an in-person main school with pedagogical lectures and tutorials, and a workshop aimed at collaborative problem-solving and brainstorming.
There will be a two-weeks main school in TIFR, with lectures followed by hands-on sessions and special topical lectures from domain experts, not limited to academia, in the evenings.ย
ย