Astronomical Instrumentation

  • Markus Kasper (ESO, Germany)
    • Requirements and challenges for imaging rocky Exoplanets with ELT-PCS
    • Dr. Kasper has received his PhD degree from the University of Heidelberg in 1999 after which he joined ESO to work on Adaptive Optics, High-Contrast Imaging, and astronomical instrumentation projects. He was involved in many of ESO’s AO projects such as MACAO and NACO LGS and was ESO’s project leader for VLT-SPHERE. He was the principal investigator of the ELT/EPICS phase-A study (2008-2010, predecessor of PCS) and of NEAR, the mid-IR imaging experiment to search for habitable planets in the Alpha Centauri in collaboration with the Breakthrough Initiatives. He is currently working towards kicking-off the ELT-PCS planet imager project.
  • Victoria Hutterer (Univ. Linz, Austria)
    • Novel approach to nonlinear Fourier-type wavefront sensing
    • Victoria Hutterer is a postdoctoral researcher at the Industrial Mathematics Institute at Johannes Kepler University (JKU) Linz in Austria. She obtained her PhD in Engineering Sciences from JKU in 2018. Her mathematical research has direct applications in both astronomical and medical imaging, and focusses on wavefront sensing for Adaptive Optics systems.
  • Anthony Berdeu (Paris Observatory, France)
    • Optimizing an Adaptative Optics loop with an inverse problem approach – Example of the aliasing error
    • Anthony Berdeu is a postdoc researcher at LESIA (Paris Observatory) working on the upgrade of the Adaptive Optics system of GRAVITY+. He is working on inverse problem approaches to optimize the instrumental performances. For this, his research studies either improving the calibration of the instrument or its AO loop prior to the science acquisition, or optimizing the science acquisition itself via optimal and robust data reduction pipelines based on a fine model of the instrument and its sensor. His past researches include the development of the AO system of the Evanescent Wave Coronagraph (EvWaCo), blind deconvolution of asteroids and detection of their moon, SPHERE/IFS robust data reduction and 2D and 3D lensless digital holography microscopy.
  • Maaike van Kooten (NRC Victoria, BC Canada)
    • Adaptive Optics Control: from the integrator to predictive filtering
    • Dr. Maaike van Kooten is an adaptive optics (AO) developer at the National research Council of Canada’s Herzberg Astronomy and Astrophysics Research Centre. She works on developing and improving AO systems for current and future telescopes. Maaike is interested in applying advanced control algorithms for AO systems on-sky and has experience doing so at W.M. Keck Observatory. She is also interested in atmospheric profiling and wavefront sensing. Dr. Van Kooten obtained her PhD at Leiden Observatory in the Netherlands in 2020.

Medical Imaging

  • Chen Qin (Imperial College, UK)
    • Deep Learning-Based Image Reconstruction in Cardiac Magnetic Resonance
    • Dr. Chen Qin is a Lecturer in Computer Vision and Machine Learning at Department of Electrical and Electronic Engineering and Imperial-X, Imperial College London. Previously, she was a Lecturer at School of Engineering, University of Edinburgh. She obtained her Ph.D. in Computing Research from Imperial College London in January 2020. Her research is interdisciplinary in nature and at the intersection between machine learning and medical imaging. Her current research mainly focuses on the development of effective and robust machine learning algorithms for medical image computing and analysis, including MR image reconstruction, medical image segmentation, and image registration. She has published more than 40 papers in top-tier peer-reviewed engineering and medical imaging journals and conference proceedings with around 2000 google scholar citations. She also served as an area chair for MICCAI 2022 and a member of organising and programme committee at several international workshops, e.g., CMRxMotion and MLMIR.
  • Paris Perdikaris (University of Pennsylvania, US)
    • Random weight factorization improves the training of continuous neural representations
    • Paris Perdikaris is an Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He received his PhD in Applied Mathematics at Brown University in 2015, and, prior to joining Penn in 2018, he was a postdoctoral researcher at the department of Mechanical Engineering at the Massachusetts Institute of Technology working on physics-informed machine learning and design optimization under uncertainty. His work spans a wide range of areas in computational science and engineering, with a particular focus on the analysis and design of complex physical and biological systems using machine learning, stochastic modeling, computational mechanics, and high-performance computing. Current research thrusts include physics-informed machine learning, uncertainty quantification in deep learning, engineering design optimization, and data-driven non-invasive medical diagnostics. His work and service has received several distinctions including the DOE Early Career Award (2018), the AFOSR Young Investigator Award (2019), the Ford Motor Company Award for Faculty Advising (2020), and the SIAG/CSE Early Career Prize (2021).
  • Christoph Kolbitsch (PTB Berlin, Germany)
    • Model-based quantitative MR imaging
    • Dr Christoph Kolbitsch is head of the quantitative MRI group at the Physikalisch-Technische Bundesanstalt (PTB), the German national metrology institute. His main research focus lies in advanced image reconstruction approaches to achieve higher diagnostic quality and reduce scan times. This includes correction of respiratory and cardiac motion for MRI of the heart and liver and also machine learning-based approaches to achieve faster and better image reconstruction. His special interest lies in model-based image reconstruction, i.e., including MR signal models into the image reconstruction chain to directly obtain quantitative parameters from the acquired raw data. He is also very active in developing open-source image reconstruction frameworks not just for MR, but also PET-MR, SPECT and CT applications
  • Tabita Catalán (ACIP, Chile)
    • Highly accelerated Cardiac CINE MRI using Neural Fields
    • Tabita Catalán is an engineer at Millennium Nucleus For Applied Control And Inverse Problems, Santiago, Chile. She is working on deep learning approaches for cardiac magnetic resonance image reconstruction. She obtained her Master’s degree in Applied Mathematics from the University of Chile in 2022.


  • Rodrigo Cofré (CNRS-Neuropsi, France)
    • Deep Brain Stimulation and the Local Orchestration of Global Functional Patterns Supporting Primate Wakefulness
    • Rodrigo Cofré received his B.Phil in Aesthetics and B.sc. and professional degree in Mathematical Engineering in 2009 from Pontificia Universidad Católica de Chile. He obtained a Ph.D. in Science at the University of Nice (supervised by Prof. Bruno Cessac) and held a postdoctoral position at the Department of Theoretical Physics at UNIGE Switzerland (supervised by Prof. J.-P Eckmann). He is currently a Postdoc CNRS at NEUROPSI Institute, Gif-sur-Yvette, France (supervised by Prof. Alain Destexhe)  and joint Professor at the Institute of Mathematical Engineering at Universidad de Valparaíso. His main research interests include complex systems, computational neuroscience and altered states of consciousness.
  • Javier Baladron (USACH, Chile)
    • Neuro-computational models of the basal ganglia
    • Javier Baladron is a Civil Engineer in Computer Science and Master in Computer Engineering from the University of Santiago de Chile. He obtained a PhD in Computer Science from the University of Nice Sophia Antipolis, France in 2013 as part of the European FACETS-ITN project in Computational Neuroscience. He was then part of the Artificial Intelligence Lab at the University of Chemnitz in Germany where he participated in multiple projects focused on studying the role of the basal ganglia in reinforced learning.
  • Ismael Jaras (Universidad de Chile, Chile)
    • Energetics, dynamics and structure: spiking neural networks under metabolic constraints
    • Ismael Jaras has line of research is bio-inspired artificial intelligence and neuromorphic computing, i.e. the area of study that emerges from the intersection between artificial intelligence and computational neuroscience. The main goal in this area is to use knowledge about how the brain works to inspire the creation of better artificial intelligence algorithms and, simultaneously, to use these models to gain a deeper understanding of learning mechanisms in living beings.
  • Christ Devia (CENIA, Universidad de Chile, Chile)
    • Network representations of dynamical brain activity
    • Christ Devia is an Electrical Civil Engineer and holds a PhD in Biomedical Sciences from the University of Chile. Her experimental research focuses on visual perception in humans using electroencephalogram analysis, eye-tracking and pupillometry. Her computational research focuses on the study and simulation of biophysical models of neurons and the circuits they form in the brain. She is currently Assistant Professor at the Department of Neurosciences of the Faculty of Medicine of the Medicine of the University of Chile.