Katharina Riedl is a researcher at the Clinic and Polyclinic for Cardiology at the University Medical Center Hamburg-Eppendorf (UKE). Her work focuses on cardiovascular imaging and machine learning applications in cardiology, particularly in intravascular ultrasound (IVUS) and cardiovascular magnetic resonance (CMR) . Her research explores thrombogenicity of coronary stents, post-COVID multi-organ effects, myocardial scar detection, and AI-driven segmentation of vascular images. Key trends in her publications include automated delineation of coronary structures , FFR (fractional flow reserve) analysis , and myocarditis recovery mechanisms . She collaborates extensively within the Hamburg City Health Study and contributes to multi-organ assessment post-SARS-CoV-2 infection. Her work integrates advanced imaging techniques with computational modeling and clinical data analysis to improve diagnostic and therapeutic strategies in cardiology.
Dr. Sander Borst is a postdoctoral researcher in the Algorithms and Complexity group at the Max Planck Institute for Informatics in Saarbrücken, Germany. Previously, he completed his Ph.D. in the Networks & Optimization group at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, advised by Daniel Dadush. He holds bachelor’s degrees in Mathematics and Computer Science and a master’s degree in Mathematics from Delft University of Technology . Current Role: Postdoctoral Researcher at Max Planck Institute for Informatics. Education: B.Sc. and M.Sc. in Mathematics and Computer Science from Delft University of Technology. Sander's research focuses on the design and analysis of online algorithms , particularly in online network design , hypergraph matching , and graph exploration . His work bridges theoretical computer science with practical applications in optimization and algorithmic randomness. Recent projects include developing algorithms for explorable heap selection , constraint propagation in MIP solvers, and analyzing integrality gaps in integer programming under random data assumptions. The trends in his publications (2020–2025) span online optimization , randomized algorithms , hypergraph theory , and integer programming . Key venues include SODA, IPCO, ITCS, and Algorithmica, with recurring themes in network design , combinatorial optimization , and theoretical guarantees for algorithmic solutions. His technical expertise extends to software development and programming languages , ensuring practical implementation of theoretical models. He has collaborated with researchers such as Daniel Dadush, Neil Olver, and Ambros Gleixner.
Prof. Dr. Dieter Horns is a Professor of Experimental Physics at the University of Hamburg, affiliated with the Institute of Experimental Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. He leads the Astroparticle Physics working group and is a key member of the Quantum Universe Cluster of Excellence. His leadership roles include managing director of the Institute, director of the Quantum Universe Research School, and coordinator of the HAFUN research building. University: University of Hamburg School: Faculty of Mathematics, Computer Science and Natural Sciences Department: Department of Physics Institute: Institute of Experimental Physics Position: Professor of Astroparticle Physics Research Group: Horns Working Group on Astroparticle Physics Dieter Horns' research is centered on high-energy astrophysics and astroparticle physics, with a focus on very high-energy (keV–TeV) gamma-ray sources, dark matter searches (particularly low-mass WISPs and axion-like particles), and the development of experimental techniques for ground-based air Cherenkov telescopes such as H.E.S.S. and CTA. His work bridges observational astrophysics, particle phenomenology, and instrumentation. His recent publications (2024–2025) demonstrate a strong emphasis on gamma-ray observations of pulsars (Geminga, Crab), novae (RS Oph), colliding-wind binaries (Eta Carinae), and AGN (M87), using cutting-edge telescopes like LST-1 and H.E.S.S. These studies involve spectral analysis, emission modeling, and instrumental development. A parallel research thread explores dark matter through both indirect detection (Fermi-LAT sources, cosmic background) and theoretical phenomenology (ALP constraints, decaying DM). Notable scientific achievements include leadership in major collaborations (H.E.S.S., CTA, TAIGA), development of innovative detection techniques, and high-impact publications with over 51,900 citations and an h-index of 116. He has supervised 32 B.Sc., 44 M.Sc., 22 Ph.D. students, and 13 postdocs. Supervised 32 B.Sc. theses Supervised 44 M.Sc. theses Supervised 22 Ph.D. students Trained 13 postdoctoral researchers Prof. Horns leads a vibrant research group actively involved in instrumentation (e.g., BRASS experiment), data analysis, and theoretical modeling. The group is deeply embedded in the Quantum Universe Cluster of Excellence and contributes to the future of gamma-ray astronomy through the Cherenkov Telescope Array (CTA) and related initiatives.
Talya Eden is an Assistant Professor in the Department of Computer Science at Bar Ilan University. She previously held a postdoctoral position at the Foundations of Data Science Institute (FODSI), jointly affiliated with MIT and Boston University, hosted by Prof. Ronitt Rubinfeld, Prof. Piotr Indyk, and Prof. Sofya Raskhodnikova. She earned her PhD from the School of Electrical Engineering at Tel Aviv University under the supervision of Prof. Dana Ron. Her research focuses on sublinear-time algorithms and property testing , with a strong emphasis on approximating graph parameters efficiently. Her work lies at the intersection of theoretical computer science, graph algorithms, and data science, addressing fundamental problems in large-scale graph analysis. Her recent publications, appearing in top venues such as STOC, FOCS, SODA, and ICALP, demonstrate consistent contributions to the fields of graph sampling, counting, streaming algorithms, and learning-augmented computation. Key themes include k-clique and triangle estimation , arboricity-based complexity , and robustness in streaming models . She has collaborated extensively with leading researchers including Dana Ron, Ronitt Rubinfeld, Piotr Indyk, and C. Seshadhri. Her work often explores the theoretical limits of what can be computed efficiently on massive graphs with limited access. Notable Scientific Contributions: Developed nearly optimal algorithms for sampling k-cliques in sublinear time. Established tight connections between arboricity and the complexity of edge sampling and degree moment estimation. Contributed to adversarially robust streaming algorithms using dense-sparse trade-offs. Explored learning-based approaches to enhance sublinear-time support estimation. Talya Eden advises students and is actively involved in the theoretical computer science research community. She has not received any explicitly mentioned scientific awards in the provided text, but her publication record in elite conferences indicates high recognition.
Johannes Maly is an Assistant Professor at Ludwig Maximilian University of Munich (LMU) since October 2022, holding the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence. Previously, he held PostDoc positions at Catholic University of Eichstaett-Ingolstadt (2020-2022) and RWTH Aachen University (2019-2020). His educational background includes: PhD in Mathematics from Technical University of Munich (TUM), 2019 (supervised by Prof. Massimo Fornasier) M.Sc. in Mathematics from TUM, 2015 B.Sc. in Mathematics from TUM, 2013 Prof. Maly's research centers on mathematical data science with core focus areas in covariance estimation , neural network approximation properties , implicit bias of gradient descent , and multi-structured signal recovery . A unifying theme across his work is the theoretical investigation of coarse quantization effects , building on his foundational contributions to compressed sensing and extending into modern AI architectures. Analysis of his recent publications reveals consistent exploration of quantization constraints in high-dimensional statistics and neural network training. Key trends include development of tuning-free covariance estimators using dithering techniques, characterization of implicit regularization in overparameterized models, and novel quantization approaches for neural networks that balance hardware efficiency with theoretical guarantees. His scientific recognition includes: relAI Fellow (Zuse School for Reliable AI) MCML Associate (Munich Center for Machine Learning) Prof. Maly's research is supported through his Bavarian AI Chair appointment and fellowships. He teaches advanced courses including Convex Optimization, High-dimensional Probability, and Mathematical Introduction to Data Science at LMU Munich, with prior teaching roles at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University. While specific student names are not listed in the source material, his position involves supervision of graduate researchers. He leads the Mathematical Data Science and Artificial Intelligence research group at LMU Munich under the Bavarian AI Chair, collaborating with MCML and relAI networks. The group investigates theoretical challenges in quantization, low-rank matrix recovery, and optimization dynamics for next-generation AI systems.
Prof. Dr. Paulo Drews-Jr is a Visiting Professor at the Department of Computer Science, Faculty of Engineering, University of Freiburg, Germany. His research focuses on Robotics, Computer Vision, and Deep Learning, particularly for autonomous systems operating in underwater and aerial environments. He holds a D.Sc. and M.Sc. in Computer Science with minors in Robotics and Computer Vision from the Federal University of Minas Gerais, Brazil, and a B.Sc. in Computer Engineering from the Federal University of Rio Grande, Brazil. Education: D.Sc. in Computer Science (Minor: Robotics and Computer Vision), Federal University of Minas Gerais, Brazil M.Sc. in Computer Science (Minor: Robotics and Computer Vision), Federal University of Minas Gerais, Brazil B.Sc. in Computer Engineering, Federal University of Rio Grande, Brazil Research Interests: Paulo Drews-Jr specializes in Robot Perception, Robotics, and Computer Vision. His work addresses challenges in Underwater Robotics, Aerial Robotics, and Industrial Automation, including Active Perception to Account for Uncertainty in Deep Learning Applied to Robotics. His recent publications emphasize Deep Reinforcement Learning, Image Processing, and Trans-Media Navigation for Hybrid Unmanned Vehicles.
Jacob Gardner is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he leads a research group focused on probabilistic machine learning. His work bridges theoretical foundations and practical applications, with emphasis on Bayesian optimization and generative modeling for scientific challenges like drug discovery and materials design. Education & Background He earned his Ph.D. in Computer Science from Cornell University under Kilian Weinberger, followed by postdoctoral work in Cornell's Operations Research department. Prior to joining Penn, he was a Research Scientist at Uber AI Labs. Research Focus His lab develops methods for: Bayesian optimization for high-dimensional scientific design problems Scalable Gaussian process inference Black-box variational inference with convergence guarantees Adversarial robustness in machine learning systems Publication Trends Recent work (2022-2023) demonstrates strong focus on efficient Bayesian methods: 80% of publications center on optimization techniques (local/global Bayesian optimization) and variational inference, with applications to latent space modeling and hyperparameter tuning. Papers consistently appear at NeurIPS, ICML, and AISTATS. Research Team Advises five PhD students: Natalie Maus (Bayesian optimization) Kaiwen Wu (optimization theory) Kyurae Kim (variational inference) Haydn Jones (structured data) Yimeng Zeng (generative modeling) Software Contributions Co-founded GPyTorch, a PyTorch-based framework for GPU-accelerated Gaussian processes that replaces traditional Cholesky decomposition with linear conjugate gradient methods.
Roland Leißa is an Assistant Professor in the School of Business Informatics and Mathematics at the University of Mannheim, Germany. His research focuses on programming languages, compilers, and domain-specific languages (DSLs) for high-performance computing across heterogeneous architectures. He teaches courses on parallel programming, compiler construction, and advanced programming topics. His work emphasizes automatic parallelization, intermediate representations, and program optimizations, particularly through partial evaluation techniques. He has contributed to tools like MimIR, AnyDSL, and FLOWER, which address challenges in GPU programming, FPGA synthesis, and ray tracing. Roland leads research on abstracting industrial and scientific application problems into reusable, theoretically sound compiler solutions. His projects span sequence alignment accelerations, dataflow compilation, and vectorization strategies, targeting modern hardware including GPUs and SIMD architectures. Contact: leissa@uni-mannheim.de | Personal Website | ORCID: 0000-0002-2444-6782
Jun Shen is a Professor at Southeast University's School of Computer Science and Engineering in Nanjing, China. With over 100 publications spanning from 1991 to 2025, Professor Shen has established a prolific research career across multiple disciplines including biomedical engineering, computer security, artificial intelligence, and wireless communications. Professor Shen's research interests focus on the intersection of computer science and practical applications, particularly in medical imaging, cybersecurity, and AI-driven solutions for healthcare and engineering problems. His recent work demonstrates significant contributions to breast cancer diagnosis through advanced medical imaging techniques, automotive cybersecurity, and novel approaches to drug discovery using deep learning. Analysis of Professor Shen's recent publications reveals a strong trend toward interdisciplinary research that combines computer vision, machine learning, and domain-specific knowledge to solve complex problems in healthcare and engineering. His work often involves collaborations across institutions and disciplines, indicating an extensive professional network. Professor Shen has made notable contributions to medical image analysis for cancer diagnosis, particularly in breast cancer metastasis detection using multi-parametric MRI and dual-energy CT. His cybersecurity research focuses on automotive systems and cyber-physical systems security. In AI applications, he has developed innovative approaches for drug-target binding prediction and vision-language models for medical applications. Professor Shen's publication record demonstrates consistent productivity with 13 papers in 2024 and 9 papers already published or accepted in 2025, indicating active and ongoing research programs across multiple domains.
Dr. Michael Hecht leads the Mathematical Foundations of Complex System Science group at the CASUS - Center for Advanced Systems Understanding , part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) . His research focuses on computational mathematics, numerical methods, and their applications in physics-informed machine learning and partial differential equations. Research Interests: Polynomial interpolation in high-dimensional spaces Topological data structure preservation in neural networks Hybrid surrogate models for complex systems Uncertainty quantification and numerical integration Recent Trends in Publications highlight advancements in polynomial-based methods for machine learning, numerical solutions to PDEs, and open-source tools like UQTestFuns and Minterpy , emphasizing efficiency and approximation accuracy across disciplines. Scientific Awards & Recognition: HZDR Innovation Contest participant ORCID: 0000-0001-9214-8253 Labs & Teams: Affiliated with the CASUS - Center for Advanced Systems Understanding , contributing to complex system science through interdisciplinary research in mathematics, computer science, and physics.
Dr. Tilo Arens is a Lecturer at the Institute for Applied and Numerical Mathematics , part of the Karlsruhe Institute of Technology (KIT) . His research focuses on numerical methods for scattering problems , particularly involving electromagnetic waves , chiral scatterers , and inverse scattering problems . He actively contributes to the Working Group 4: Inverse Problems and collaborates on projects within the CRC 1173 Wave Phenomena . His teaching portfolio includes courses such as Higher Mathematics II for engineering disciplines, Optimization Theory , and Scattering Theory . He also supervises bachelor’s and master’s theses on topics like Approximation of rotation-minimizing frames using B-spline curves and Numerical methods for integral equations . Dr. Arens has authored numerous publications on inverse scattering, integral equations, and computational methods. Notable works include studies on monotonicity-based shape reconstruction , high-order numerical methods for bi-periodic structures , and electromagnetic chirality measurement . His research intersects with applied mathematics, physics, and computational science. He previously collaborated on a student seminar with Heisenberg-Gymnasium Karlsruhe to bridge university and secondary education in mathematics. His habilitation (2010) and PhD (2000) are in electromagnetic and elastic wave scattering, respectively.
Professor Mathias Trabs is a faculty member at the Karlsruhe Institute of Technology (KIT), where he has been serving as a Professor since 2021. He is affiliated with the Department of Mathematics, specifically within the Institute of Stochastics. Previously, he held positions as Heisenberg professor at Universität Hamburg (2021) and Assistant professor at Universität Hamburg (2016-2021). His research spans several key areas in modern statistics and probability theory. Professor Trabs specializes in Nonparametric and high-dimensional Statistics, Statistics for stochastic processes, Statistical inverse problems, Statistical Learning, and Stochastic (partial) differential equations. His work bridges theoretical statistics with practical applications in physics, machine learning, and high-energy experiments. Analysis of his recent publications reveals a strong focus on the intersection of statistical theory and machine learning, particularly in generative models, neural networks, and high-dimensional data analysis. His work also demonstrates significant contributions to the mathematical foundations of stochastic processes and their applications in physical sciences. Professor Trabs has supervised numerous doctoral students including Lea Kunkel, Thea Engler, Jan Rabe, Sebastian Bieringer, Maximilian F. Steffen, and Florian Hildebrandt. His research has been supported by various projects including the Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH), DFG project TR 1349/3-1 on high-dimensional statistics, and the LD-SODA research project. He is actively involved in academic leadership, serving as Deputy speaker of the KIT Center MathSEE (Mathematics in Sciences, Engineering, and Economics), and as a member of the steering boards of both the DMV-Fachgruppe Stochastik (Probability and Statistics Group of the German Mathematical Society) and the KIT Graduate School Computational and Data Science (KCDS).
Peter Spichtinger is a Professor for Theoretical Cloud Physics at the Institute for Atmospheric Physics (IPA) , Johannes Gutenberg-Universität Mainz , Germany. Since 2010, he has led research on cloud dynamics, multiscale modeling, and atmospheric pattern formation. His work bridges mathematical modeling with climate science , focusing on ice supersaturation , gravity wave-cloud interactions , and machine learning applications in atmospheric physics. Key Affiliations : Institute for Atmospheric Physics (Mainz), Mainz Institute of Multiscale Modeling (M3O), Waves to Weather (W2W) SFB, TPChange CRC Research Themes : Cloud-scale microphysics, ice nucleation pathways, tropopause dynamics, stochastic Galerkin methods, fractal properties of atmospheric systems His 15 most recent publications emphasize ice microphysics in climate models, GPU-accelerated weather front detection , asymptotic cloud modeling , and machine learning for atmospheric datasets . Current projects include BINARY (Big Data in Atmospheric Physics) and leadership of the SCALES Conference 2025 . He serves as Deputy Institute Director (2020-2022) and Spokesperson for M3O (since 2022) . Scientific awards include the Marie Curie Fellowship (2007-2009) . He supervises theses in interdisciplinary contexts and co-organizes seminars on cloud dynamics and machine learning for atmospheric systems.
Ralph Neininger is a Professor at the Department of Computer Science and Mathematics, Goethe University Frankfurt. His research focuses on Probability Theory, Algorithms, Stochastic Analysis, and Data Structures, with a particular emphasis on the analysis of randomized algorithms and combinatorial structures. Key research areas: Randomized algorithms, stochastic processes, data structures, and distributional analysis. Recent publications address complexity fluctuations in algorithms, analysis of satisfiability problems, and probabilistic models for trees and urns. His methodological work leverages advanced probabilistic techniques to derive limit theorems and convergence rates for algorithms. Though no scientific awards are explicitly mentioned, his extensive publication record underscores significant contributions to theoretical computer science and applied probability. He has not been listed with advisees or students in the provided materials.
Dr. Boris Novikov is a prominent Russian computer scientist affiliated with St Petersburg University and the Higher School of Economics . His research focuses on distributed stream processing, temporal databases, and XML query optimization. Key collaborators include Artem Trofimov , Elena Mikhailova , and Anna Yarygina . Research Areas : Distributed Systems (stream processing, substream management) Database Optimization (vertical partitioning, XML caching) Temporal Data Management Machine Learning (concept drift detection) His recent work on distributed stream processing (2023-2022) explores substream bounding, deterministic models, and resource allocation strategies. Earlier contributions (2012-2008) focused on XML query performance, similarity indexing, and access control frameworks. Technical Trends : Event time alignment in distributed architectures Dynamic residual projection for cybersecurity Bi-objective optimization in database systems Speculative execution with conflict resolution Incremental Lagrangian dual solutions Matrix clustering for partitioning problems
