Shiting Long
Project 1: Performance-portability of task-based programming models on modern HPC architectures (KTH & BUW)
Shiting is a research software engineer at Jülich Supercomputing Centre (JSC). She has been working on the Data Transfer Service for the Fenix research infrastructure that federates 6 European supercomputing centres. She was in the SECCLO Erasmus Mundus programme and obtained double master's degrees in computer science from Aalto University (Finland) and University of Tartu (Estonia). She decided to join AQTIVATE project because she wants to study HPC systems, and she is curious about what applications they can do or what scientific disciplines they can help with, which she gets to know in her work but would like to understand better.
Mario Papace
Project 2: Efficient methods for traces of matrix functions (BUW & UCY)
Mario is a graduate student in Theoretical Physics of the University of Pisa. During his studies he developed a strong interest in computational methods, such as Monte Carlo simulations, Machine Learning and High-Performance Computing (HPC), applied to high-energy physics and also to other fields. In his master's thesis, carried out at "Istituto Nazionale di Fisica Nucleare" (INFN), he studied the dependence from topology of SU(3) pure gauge theory spectrum under the supervision of Prof. Claudio Bonati. The AQTIVATE project caught his interest thanks to the importance of its objectives and its interdisciplinary nature. In his free time he enjoys playing different sports and spending time with lifelong friends.
Rayan Moussa
Project 3: Machine learning for multigrid methods (BUW & CyI)
Rayan Moussa is a graduate student in Theoretical Physics at the Katholieke Universiteit Leuven, Belgium. His research interests lie within, but are not limited to, the fields of theoretical high energy physics, quantum information theory and computational physics. In his thesis, he studied the connections between the string theoretic and quantum gravitational notion of weak gravity and the possibility of obtaining a truly four-dimensional effective field theory by compactifying the additional higher dimensions into invisible internal spaces whose geometries possess the correct properties as required by experimental data. Project AQTIVATE peeked his interest due to its interdisciplinary nature which connects to his background in theoretical and computational physics and the ambitious goals it has in regards to computational methods and their applications within high energy physics and beyond.
Gabriele Pierini
Project 4: Accelerating simulations of lattice gauge theories with equivariant flows (CyI & TUB)
Gabriele Pierini attained his Master’s Degree in Physics at ETH Zuerich. He has always been interested in theoretical and computational physics, with a focus on particle physics. Over his studies, he learnt about the most important features of
Quantum Field Theory and of the Standard Model and about the fundamental algorithms employed in computational physics. He worked on non-linear electrodynamics simulations on lattice for his Master’s thesis with Prof. Dr. Marina Krstic Marinkovic and Dr. Verónica Errasti Díez, hence merging his two interests in an interdisciplinary project. During the thesis and the following internship, he simulated mainly the Born-Infeld Lagrangian, improving the gauge fixing algorithms and computing several observables while implementing the multilevel algorithm. The AQTIVATE Project caught his interest for its multidisciplinary aim: developing
new algorithms and employing them to obtain better insights into, but not limited to, fundamental physics. In his free time, Gabriele enjoys sitting in a café doing nothing but talking (after all he is Italian), visiting museums and new cities, playing
boardgames, volleyball and cooking.
Gabriele is currently the student representative at the project's Supervisory Board.
Francesco Fossella
Project 5: Deep-data assimilation and deep-feature-based metric for turbulent flows (IPP & UTOV)
Francesco Fossella is a graduate student in Theoretical Physics at the University of Rome ‘Tor Vergata’. His interests primarily lie in the fields of non-equilibrium statistical mechanics and nonlinear physics, with a strong focus on the study of cutting-edge algorithms for fluid dynamics applications and in the realm of stochastic phenomena. His master's thesis concerns the numerical implementation of data assimilation techniques, such as Nudging and Ensemble Kalman Filter, to dynamical models of energy cascade in turbulence, specifically on the so-called Shell Models. Francesco's interest in leveraging machine learning to expand these techniques and develop data-driven tools closely aligns with the objectives of the AQTIVATE proposal. Additionally, the opportunity to contribute to an interdisciplinary international project has fueled his motivation and enthusiasm in applying. Among his passions, cinema and hard rock music undoubtedly fall.
Lukas Mullender
Project 6: Data-driven MD: Calculating free energies by learning from QM Potentials & cryo-EM data (KTH & RWTH)
Lukas Müllender obtained his Master’s degree in Physics at RWTH Aachen with a specialisation in condensed matter theory. Over the course of his studies, he developed a particular interest in computational methods in physics, including molecular dynamics and Monte Carlo simulations, machine learning and tensor networks, as well as their application to problems in condensed matter physics, biophysics and neuroscience. In particular, he is interested in the complex interplay of biomolecules that are involved in the dynamics of the nervous system, and the computational techniques needed to study them in silico. For his Masters thesis, he worked on combining molecular dynamics in trajectory space with machine learning techniques, in order to identify collective variables for enhanced sampling simulations. With his interests thus spread among various disciplines, the AQTIVATE project with its innovative, interdisciplinary and international spirit was a natural choice. Besides his research endeavours, Lukas is also an avid musician.
Asmita Datta
Project 7: Tensor network-based quantum computer simulator (UNIPD & UCY)
Asmita Datta completed a BS-MS Dual Degree and graduated with a Major in Physics and Minor in Data Science and Engineering from Indian Institute of Science Education and
Research (IISER) Bhopal, India. Her interests lie in the fields of correlated quantum systems, quantum information theory and the study & implementation of numerical methods to electronic structure calculations. For her master’s thesis, she worked on a project at the
interface of Theoretical Physics and Computer Science funded by the I-HUB Quantum Technology Foundation, IISER Pune; where she worked on predicting the hybrid functional parameters present in density functional theory calculations of binary semiconductors using
classical and quantum machine learning techniques. She has participated in several research internships including the DAAD-WISE and London Mathematical Laboratory (ICTP-LML) summer projects, on a wide range of topics in computational physics and machine learning. The multifaceted nature of the AQTIVATE Project along with its collaborative spirit motivated her to apply to this cohort. She likes to spend most of her leisure time painting pictures, travelling and
exploring different exquisite cuisines.
Rocco Barač
Project 8: Quantum computing and tensor networks for (2+1)D and (3+1)D QED (UNIPD & UCY)
Rocco Barač is a graduate physics student from Split, Croatia, with a passion for theoretical and computational physics. Alongside his university studies, he has been involved in quantum mechanics research, simulating ultracold quantum droplets in Python using quantum Monte Carlo and density functional theory. He has also participated in astrophysics research, utilizing Python for data analysis to investigate brown dwarf formation in star clusters. During his master's thesis, he learned more about quantum computing, tensor networks and lattice gauge theory. He will contribute to the AQTIVATE project, focusing on quantum computing and tensor networks for (2+1)D and (3+1)D QED.
Samuele Pedrielli
Project 9: Quantum, classical and quantum-inspired machine learning (TUB & UNIPD)
Samuele Pedrielli is a graduate student of theoretical physics from the University of Trieste, Italy. His interests vary from the role topology plays in physics to the world of machine learning, many-body physics, data mining, and everything in between. In his master thesis, he made contact with all these different fields, exploiting machine learning tools to study how topological information is encoded in the data structures that emerge from many body partition functions. His decision to join AQTIVATE was motivated by the international and interdisciplinary spirit of the project, together with an innovative approach to the study of interesting physics problems. When he is not studying/coding, Samuele really enjoys taking photographs of what happens around him or playing chess. However, by far his favorite way of spending time is to be with and meet new friends.
Christian Schneider
Project 10: Precision lattice quantum chromodynamics matrix elements (UCY & BUW)
Christian Schneider studied physics at the University of Siegen in Germany and graduated with a master’s degree. His studies were primarily focused on theoretical particle physics but he also took some interest in quantum optics and quantum information theory. During his thesis, he worked on a project within the field of lattice field theory (LFT), which was an entirely new topic for him at that time. He especially liked the combination of physics with computational methods that LFT provided. He also found this interdisciplinary nature in the AQTIVATE project, which is why he decided to apply for a position within the program. In his free time he is doing fencing as well as strength training at the gym. When he is not doing any sports, he enjoys playing video games and listening to music.
Christian Kummer
Project 11: Improved computation of GPDs with learnable denoising approaches (UCY & TUB)
Christian Kummer is a graduate student at the JLU Giessen in Germany. During his studies, he worked on classical statistical simulations, parton distribution functions (PDFs), and most recently on transport codes. In his thesis he utilized transport models to investigate pion production in heavy ion collisions. In addition to his studies, Christian Kummer has spent in total more than two years on international exchanges, gaining valuable experience and exposure to different cultures. With his diverse academic background and global perspective, he fits well to the international and interdisciplinary nature of AQTIVATE.
André Freitas
Project 12: Large eddy simulation models in a deep machine learning loop (UTOV & IPP)
André Freitas is a graduate student in Aerospace Engineering at TU Delft. He is specialized in Aerodynamics, with a primary focus on computational fluid dynamics (CFD). During his studies, he participated in multiple student projects, such as Moto Student and Formula Student, working as an Aerodynamics and CFD engineer. For his Master's thesis, he worked on the validation of a synthetic turbulence model applied to a fluidstructure interaction (FSI) framework to simulate turbulence-induced vibrations in nuclear fuel rods. He found this research to be very interesting due to its multidisciplinary nature, requiring understanding of turbulence physics, as well as FSI and CFD. Recently, André has become very interested in applying machine learning techniques to CFD, which led him to apply for the AQTIVATE project.
Elisa Bellantoni
Project 13: Complex wetting problems using neural networks (CyI, UTOV & IPP)
Sachin Shivakumar
Project 14: Accelerating QM/MM simulations via machine learning (RWTH & KTH)
Sachin was a Junior Research Fellow at the Indian Institute of Science Education and Research Bhopal (IISER B). There, he worked as a lead software developer on the project Layer Integrated Toolkit and Engine for Simulations of Photo-induced Phenomena (LITESOPH), funded by the National Supercomputing Mission (NSM). He is interested in designing and developing new-age scientific software in the field of computational quantum chemistry that integrates traditional computationally intensive methods with machine learning models to accelerate material simulations. He obtained his master's degree in physics from the National Institute of Technology Hamirpur (NITH), India. He applied for the AQTIVATE project because its research objectives to apply AI and ML approaches to challenging problems in physics and biology, matches his research interests. Also, the project's collaborations with industry partners and its international nature will give him the opportunity to learn from leading experts in the field and give him a solid platform to kickstart his research career.
Marta Devodier
Project 15: Improved free energy calculation and extreme scalability of molecular dynamics simulation (RWTH & KTH)
Marta Devodier attained a master's degree in Physics from the University of Parma, Italy. During her Master, she developed a particular interest in computational methods in physics, including molecular dynamics and their application in biophysics and neuroscience. For these reasons, she decided to apply to the ERASMUS+ Program, and she worked on her Master’s thesis at Forschungszentrum Jülich as an ERASMUS+ student. There, she had the priceless opportunity to acquire knowledge in the biophysics of membrane proteins essential for brain function and to gain expertise in setting up and running QM/MM simulations using the supercomputers of the Jülich Supercomputing Centre. She decided to apply to this position because of the international and interdisciplinary nature of the AQTIVATE project. Moreover, I she was really excited about the opportunity to apply innovative approaches to challenging problems in physics. In her free time, she enjoys playing the piano, hiking and spending time with her friends.