We are looking forward to welcoming you to the third annual HAMLET-PHYSICS Conference, to be held in sunny Copenhagen, August 19 - 21, 2026. It follows two very successful editions in 2024 and 2025.

The workshop has three main goals:

1. To bring together Danish and international physicists using ML to meet, share ideas, and build community across location and physics specialty

2. To bring domain scientists into close contact with ML experts, to build community across the theory - application bridge

3. To provide a friendly environment for researchers to share best practices, for students to interact with experts, and for other sciences and industry to understand the state of ML in physics

Daniel Whiteson

University of California, Irvine

Daniel Whiteson is a Professor of Physics & Astronomy at UC Irvine, where he also holds a joint appointment in Logic and Philosophy of Science. He was one of the earliest researchers to bring deep learning into experimental physics, and his work on inference and learning ranges widely — from the ATLAS experiment at CERN to CRAYFIS, a distributed project that turns ordinary smartphone cameras into a global cosmic-ray detector, and machine-learned exploration of the space of possible universes. He is also a leading science communicator, co-authoring popular-science books with cartoonist Jorge Cham and co-hosting a widely followed physics podcast.

Cecilia Clementi

Free University of Berlin

Cecilia Clementi is an Einstein Professor of Physics and Professor of Theoretical and Computational Biophysics at Freie Universität Berlin. Her work brings together statistical physics, molecular simulation and machine learning to understand biomolecular processes at long timescales, from protein folding to conformational change and molecular function. She has helped define the modern interface between ML and molecular simulation, including work on machine-learned coarse-grained models such as CGnets and CGSchNet, which use neural networks to simulate protein dynamics far more efficiently while retaining molecular detail. She is an ELLIS Fellow in the Machine Learning for Molecule Discovery programme, connecting her work to the broader European AI-for-science community.

Shah Rukh Qasim

ETH Zurich

Shah Rukh Qasim works at the interface of machine learning and reconstruction. He is a co-developer of object condensation — a general, one-shot technique for reconstructing many objects at once from graph- and image-structured data — and of GravNet-style distance-weighted graph neural networks. Much of his work applies these methods to reconstructing particles directly from detector hits in high-granularity calorimeters such as the CMS HGCAL, and extends to real-time graph-network inference on FPGAs.

Gabriel Facini

University College London

Gabriel Facini is an Associate Professor of Data Intensive Science and Physics at UCL and a core member of its Centre for Data Intensive Science and Industry, which brings machine learning to bear across astrophysics, high-energy physics, computer science and industry. He co-organises UCL's interdisciplinary AI for Data-Intensive Science and Industry programme and supervises students on applied ML placements with partners across media, analytics and the public sector. Within fundamental physics he works on charged-particle tracking, jet reconstruction and flavour tagging, and has helped carry modern architectures — such as transformer- and MaskFormer-based models drawn from computer vision — into scientific reconstruction.