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Pieter RoffelsenView profile

Research Fellow

• Riemann-Hilbert Theory
• Monodromy Manifolds
• Painlevé Equations
• +7 more

Mr Pieter Roffelsen is a Research Fellow at the University of Sydney , affiliated with the Integrable Systems group within the School of Mathematics and Statistics . Prior to this, he was a Research Fellow in the Geometry and Mathematical Physics group at SISSA, Trieste. His research focuses on the interplay between integrable systems, complex analysis, and algebraic geometry. Education : PhD in Mathematics (2017) from the University of Sydney under Nalini Joshi. Riemann-Hilbert theory, Painlevé equations, and monodromy manifolds form the core of his work. He explores connections between nonlinear differential/difference equations and algebraic surfaces like Segre and Jimbo-Fricke cubic surfaces, with applications to special functions and quantisation problems. His publications reveal a strong emphasis on: q-difference Painlevé equations Monodromy manifolds and Segre surfaces Orthogonal polynomials on elliptic curves Quantisation and asymptotics Nonlinear connection problems Roffelsen collaborates with leading mathematicians including Nalini Joshi , Davide Masoero , and Marta Mazzocco . Key recent works investigate the crystal limit of q-Painlevé equations, geometric visualizations of Segre surfaces, and asymptotic distributions of polynomial zeros.

• Quantum Physics
• Materials Science
• Machine Learning
• +3 more

Dr. Andreas Leitherer is a Postdoctoral Researcher in the Department of Quantum Information Theory at the Institute of Photonic Sciences (ICFO). He holds a PhD in Physics from Humboldt University (Germany). His research focuses on interdisciplinary areas combining quantum systems analysis, materials science, and machine learning applications in crystallography and microscopy. Leitherer's work bridges theoretical quantum physics with computational methods, particularly in open quantum systems and algebraic quantum gravity. He also pioneers AI-driven approaches for automated crystal structure identification and materials characterization using electron microscopy. His publications since 2017 reflect a progression from foundational quantum gravity studies to applied machine learning in materials science. His research interests include quantum dynamics, computational materials science, Bayesian deep learning for crystallography, and data-centric materials discovery. Notably, he has contributed to developing robust machine learning frameworks for feature classification in atomic resolution images and exploring materials dataspaces through hybrid learning models. No scientific awards or formal student advisees are listed. His current research is affiliated with ICFO's Quantum Information Theory group, where he applies advanced computational techniques to solve problems at the intersection of quantum physics and materials innovation.

• Biomedical Engineering
• Prosthetics
• Mechanical Engineering
• +3 more

Philip Davenport is a researcher affiliated with Bournemouth University 's Faculty of Science and Technology , focusing on biomedical engineering and prosthetic socket pressure distribution. He collaborates with Chas A Blatchford and Sons Ltd to develop systems for prosthetic alignment analysis and clinical risk detection. His research spans biomedical engineering , mechanical engineering , and clinical applications , emphasizing machine learning and sensor technology for lower-limb prosthetics. His work includes artificial neural networks , augmented reality visualization , and inverse problem modeling to optimize prosthetic socket fit and alignment across varying terrains. Recent publications highlight his contributions to marker-less motion capture , pressure measurement systems , and data interpretation methods for prosthetic users. He holds a BEng (Hons) in Electrical and Mechanical Engineering (University of Edinburgh, 2009) and an MSc in Biomedical Engineering (University of Surrey, 2010) . As a registered clinical scientist with the UK Healthcare Professions Council (2015-) and member of the Institute of Physics and Engineering in Medicine (2014-), his expertise bridges clinical practice and engineering innovation. Publications include peer-reviewed journal articles, conference proceedings, and a 2017 PhD thesis on ensemble neural network applications for prosthetic pressure estimation.

• Stochastic Analysis
• Partial Differential Equations
• Nonlinear PDEs
• +5 more

Benjamin Gess is a Professor of Mathematics at Technische Universität Berlin and leads the 'Stochastic analysis in the Sciences' research group at the Max Planck Institute for Mathematics in the Sciences (MIS) in Leipzig. He holds an ERC Consolidator Grant (2023) for his project 'FluCo', focusing on fluctuations in continuum systems and conservative SPDEs. His research spans stochastic partial differential equations (SPDEs), nonlinear PDEs, stochastic dynamics, interacting particle systems, machine learning, and fluid dynamics, with an emphasis on conservative SPDEs and their applications. Education: PhD (summa cum laude) from Universität Bielefeld (2011), MSc from University of Warwick (2009). Postdoctoral positions included University of Chicago (2013–2015) and Technische Universität Berlin (2012). He has held academic roles at Universität Bielefeld (W3 Professor from 2019–2024) and the Max Planck Institute since 2015. Key research interests include: regularization by noise, large deviations, stochastic flows, and the interplay between stochasticity and complex systems. His work combines stochastic analysis, nonlinear PDE theory, and kinetic theory to address challenges in singular SPDEs and supercritical phenomena. Recent grants include the ERC Consolidator Grant, and his research has led to advancements in understanding fluctuating hydrodynamics, machine learning dynamics via stochastic gradient descent, and self-organized criticality models. He supervises a research group and collaborates internationally with institutions like the University of Chicago and MPI MIS.