Name: Machine Learning in Natural Sciences: from Quantum Physics to Nanoscience and Structural Biology
Start: 2022-09-19T09:00:00+02:00
End: 2022-09-22T16:00:00+02:00
Location: CFEL (Building 99)

Machine Learning in Natural Sciences: from Quantum Physics to Nanoscience and Structural Biology

from Monday 19 September 2022 (09:00) to Thursday 22 September 2022 (16:00)

Monday 19 September 2022

09:00 Welcome
Welcome
09:00 - 09:15
Room: Seminarraum 1-3
09:15
09:15 - 10:45
Room: Seminarraum 1-3
Contributions

09:15 Turning a physical system into a self-learning machine - Florian Marquardt (MPL Erlangen)

10:00 Depth requirement reduction using sequential execution - Adrián Pérez Salinas (Leiden University)

10:15 Reducing Barren Plateaus in Quantum Algorithm Protocols - Lukas Broers (Universität Hamburg)

10:30 Characterization of Few-femtosecond Near-infrared Pulses using Machine Learning approach - Daria Kolbasova (CFEL DESY, Department of Physics UHH)
10:45 Coffee Break
Coffee Break
10:45 - 11:15
Room: Seminarraum 1-3
11:15
11:15 - 12:45
Room: Seminarraum 1-3
Contributions

11:15 Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks - Marin Bukov (Sofia University)

12:00 Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks - Friederike Metz (Okinawa Institute of Science and Technology)

12:15 Online adaptive estimation of decoherence timescales for a single qubit - Muhammad Junaid Arshad (Heriot-Watt University)

12:30 Robust quantum dot charge autotuning with Bayesian Neural Networks - Victor Yon (Institut Interdisciplinaire d’Innovation Technologique (3IT), Laboratoire Nanotechnologies Nanosystèmes (LN2))
12:45 Lunch Break
Lunch Break
12:45 - 14:45
Room: Seminarraum 1-3
14:45
14:45 - 16:00
Room: Seminarraum 1-3
Contributions

14:45 Creating community driven challenges to advance Science - Gorka Muñoz-Gil (University of Innsbruck)

15:30 Unraveling heterogeneous diffusion in single particle trajectories - Borja Requena (ICFO-The Institute of Photonic Sciences)
16:00 Coffee Break
Coffee Break
16:00 - 16:30
Room: Seminarraum 1-3
16:30
16:30 - 18:00
Room: Seminarraum 1-3
Contributions

16:30 Machine Learning for Accelerated Molecular Discovery - Pascal Friederich (Karlsruher Institut für Technologie (KIT))

17:15 Robust Image Descriptor for Machine Learning based Data Reduction in Crystallography - Vahid Rahmani (DESY)

17:30 PeakNet: Peak Finding for Serial Crystallography - Shah Nawaz (DESY)
18:00
18:00 - 21:00
Room: Seminarraum 1-3
Contributions

18:00 Machine learning parameters of attosecond pulses based on photoelectron momentum distributions - Tomasz Szoldra (Jagiellonian University in Krakow)

18:00 Continuous Machine Learning with generated radio galaxy data - Florian Griese (TU Hamburg)

18:00 Classification of quantum phases with quantum machine learning - Teresa Sancho-Lorente ( Instituto de Nanociencia y Materiales de Arag ́on (INMA) and Departamento de F ́ısica de la Materia Condensada)

18:00 Building separable approximations for quantum states via neural networks - Antoine Girardin

18:00 Object detection on GaN quantum wells SEM images incorporating YOLO network Model - Mahdi Khalili Hezarjaribi (Institut für Angewandte Physik, Technische Universität Braunschweig)

18:00 SciFi: An Embedded Asset Store to Organize and Optimize Data Files for Machine Learning - Martin Poppinga (Universität Hamburg)

18:00 Estimating the entangling power of a two-qubit gate from measurement data: artificial neural networks versus standard tomography methods - Salwa Shaglel (Universität Siegen)

18:00 Adding connections in a restricted Boltzmann machine and testing the Regularized Axons family in deeper architectures - Eloy Piñol Jimenez (ICFO - Institute of Photonic Sciences)

18:00 Utilising charge state distributions for calibration of intense XFEL pulses with Bayesian optimisation - Niels Breckwoldt (Universität Hamburg, Center for Free-Electron Laser Science CFEL)

18:00 Topological characterization of dynamic chiral magnetic textures using machine learning - Tim Matthies (Universität Hamburg)

18:00 Quantum magnetic skyrmions using neural network quantum states - Ashish Joshi (Kyoto University)

18:00 Neural quantum states for the simulation of nonequilibrium quantum systems - Damian Hofmann (Max Planck Institute for the Structure and Dynamics of Matter)
Tuesday 20 September 2022
09:00
09:00 - 10:30
Room: Seminarraum 1-3
Contributions

09:00 Insights into quantum many-body systems through Hamiltonian reconstruction - Annabelle Bohrdt (Harvard University)

09:45 Solving quantum and classical dissipative dynamics with artificial neural networks - Martin Gärttner (Heidelberg University)
10:30 Coffee Break
Coffee Break
10:30 - 11:00
Room: Seminarraum 1-3
11:00
11:00 - 12:30
Room: Seminarraum 1-3
Contributions

11:00 Quantum physics data as a playground for machine learning - Evert van Nieuwenburg (Leiden University)

11:45 Circuit complexity through phase transitions and its consequences in quantum state preparation - David Zueco (CSIC (Spanish National Research Council))
12:30 Lunch Break
Lunch Break
12:30 - 15:00
Room: Seminarraum 1-3
15:00
15:00 - 16:45
Room: Seminarraum 1-3
Contributions

15:00 Physical Parametrisations of Mixed Quantum States with Deep Neural Networks - Filippo Vicentini (EPFL - CQSL)

15:15 Machine learning of thermodynamic observables in lattice quantum field theory - Kim Nicoli (Technische Universität Berlin)

15:30 Unraveling Quantum Scrambling with Neural Networks - Jan Olle (Max Planck Institute for the Science of Light)

16:00 Data-Enhanced Variational Monte Carlo with Neural Network Quantum States - Stefanie Czischek (University of Waterloo)
Wednesday 21 September 2022
09:00
09:00 - 10:30
Room: Seminarraum 1-3
Contributions

09:00 AI Usage in Structural Biology - Andrea Thorn (Universität Hamburg)

09:45 Chiral Structure Classification by a Convolutional Neural Network - Peer Kasten (Institut für Angewandte Physik, Technische Universität Braunschweig)

10:00 Learning Conductance: Gaussian Process Regression for Molecular Electronics - Michael Deffner (Universität Hamburg)
10:30 Coffee Break
Coffee Break
10:30 - 11:00
Room: Seminarraum 1-3
11:00
11:00 - 12:30
Room: Seminarraum 1-3
Contributions

11:00 OpenFold: Democratizing access to modeling biomolecules and their interactions - Mohammed AlQuraishi (Columbia University)

11:45 Quantum flows neural network for variational solutions of the Schrödinger equation - Andrey Yachmenev (Center for Free-Electron Laser Science, DESY)
12:30 Lunch Break
Lunch Break
12:30 - 14:30
Room: Seminarraum 1-3
14:30
14:30 - 15:30
Room: Seminarraum 1-3
Contributions

14:30 What do neural networks learn? On the interplay between data structure and representation learning - Sebastian Goldt (International School of Advanced Studies (SISSA))

15:15 Tensor networks and efficient descriptions of classical data - Ivan Kukuljan (Max Planck Institute of Quantum Optics)
15:30 Panel Discussion
Panel Discussion
15:30 - 16:15
Room: Seminarraum 1-3

16:15 Coffee Break
Coffee Break
16:15 - 16:30
Room: Seminarraum 1-3
16:30
16:30 - 18:00
Room: Seminarraum 1-3
Contributions

16:30 Autonomous Materials Research and Discovery at NIST - Aaron Gilad Kusne (NIST)

17:15 Deep Learning for Topological Materials - Marcin Płodzień (International Research Centre MagTop, Poland; ICFO – The Institute of Photonic Sciences, Spain)
18:00

18:00 - 21:00
Room: Seminarraum 1-3
Thursday 22 September 2022
09:00
09:00 - 10:30
Room: Seminarraum 1-3
Contributions

09:00 Towards an Artificial Muse for new Ideas and Understanding in the natural Sciences - Mario Krenn (MPL Erlangen)

09:45 Revealing physical concepts learned by artificial neural networks - Sebastian Wetzel (Perimeter Institute)
10:30 Coffee Break
Coffee Break
10:30 - 11:00
Room: Seminarraum 1-3
11:00
11:00 - 12:30
Room: Seminarraum 1-3
Contributions

11:00 Machine-learning from heterogenous data: dream or reality? - Claudia Draxl (Humboldt Universität)

11:45 Tackling Multimodal Device Distributions in Inverse Photonic Design: Lessons learned from Invertible Neural Networks - Michel Frising (Universidad Autonoma de Madrid)
12:30 Lunch Break
Lunch Break
12:30 - 14:30
Room: Seminarraum 1-3