Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.
Max Willsey is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, since 2024. He specializes in program optimization, leveraging techniques from programming languages, databases, and systems to develop robust and accessible compiler frameworks. His research focuses on equality saturation, E-Graphs, and the integration of Datalog with compiler optimizations. He has contributed to advancements in unifying algorithmic approaches, enabling faster and more extensible program analysis. Teaching: CS 164 (Programming Languages and Compilers, Spring 2025), CS 265 (Compiler Optimization, Fall 2024), and CS 294-260 (Declarative Program Analysis and Optimization, Spring 2024). Research Highlights: Development of the egg and egglog projects, co-organizing the EGRAPHS workshop, and leading the EGRAPHS Community for e-graphs researchers. His recent articles highlight trends in unifying traditional hash joins with worst-case optimal joins, applying equality saturation to diverse domains like Datalog and tensor graph optimization, and advancing E-Graphs for program synthesis and formal verification. Scientific Awards: SIGMOD Record Research Highlight, 2024 MIT PL Review Selection, 2024 Distinguished Paper, OOPSLA 2021 and POPL 2021 NSF Graduate Research Fellowship Honorable Mention, 2018 Qualcomm Innovation Fellow, 2019 Service: Committee Member, PLDI 2025, POPL 2025, ASPLOS 2025 Co-organizer, EGRAPHS 2024 and 2023 workshops Interviewer, UC Berkeley Graduate Admissions Committee, 2024
Daniela Doneva, Ph.D., is a Lecturer at the Institute for Astronomy and Astrophysics (IAAT) , University of Tübingen. Her work spans theoretical and computational astrophysics, focusing on nonlinear gravitational phenomena in modified theories of gravity. Key affiliations: Institute for Astronomy and Astrophysics (IAAT), University of Tübingen Degree: Ph.D. in Physics Research interests include: Black Hole Scalarization : Spin-induced and curvature-induced scalarization mechanisms in black holes and neutron stars. Modified Gravity Theories : Einstein-Gauss-Bonnet, scalar-tensor theories, and teleparallel gravity extensions. Gravitational Wave Physics : Modeling waveforms, stability analysis, and LISA mission applications. Numerical Relativity : Simulations of compact object mergers and nonlinear evolutions. Her publications emphasize computational methods (e.g., neural network surrogates), stability analysis, and observational constraints from gravitational wave detectors like LISA. No scientific awards or student advisement details were found in the provided texts.
Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.
Jun. Prof. Dr. Ziyue Li is a Junior Professor in Machine Learning in Smart Markets at the Information Systems Department of WiSo Faculty, University of Cologne, Germany (2022–present). They also serve as Chief Machine Learning Scientist at EWI, Germany. Their academic career includes researcher positions at Hong Kong Science and Technology Park Corporation/SenseTime (2021–2022), Nokia Bell Labs (2019), and doctoral studies at The Hong Kong University of Science and Technology (2017–2021). Dr. Li's research focuses on high-dimensional data mining , machine learning , and smart mobility . Their work combines tensor analysis, graph modeling, and spatiotemporal prediction to solve complex problems in transportation systems and data analytics. They have developed innovative approaches for passenger flow prediction and travel pattern analysis. Their publications demonstrate a strong focus on tensor-based machine learning methods applied to mobility data. Key trends include Integration of graph theory with tensor decomposition Development of spatiotemporal prediction models Applications in urban transportation analytics Hybrid transfer learning approaches Multi-clustering methods for travel pattern analysis Data completion techniques for complex networks Scientific recognition includes Multiple INFORMS Data Mining Section awards IEEE CASE Best Conference Paper Award Hong Kong Ph.D. Fellowship Scholarship HKUST Excellent Research Award Three Minute Thesis Competition recognition
Prof. Mario Kupnik is a Full Professor at the Technische Universität Darmstadt , leading the Measurement and Sensor Technology Group within the Department of Electrical Engineering and Information Technology. His academic career includes roles at Stanford University (2005–2011) and Brandenburgische Technische Universität Cottbus (2011–2014). He holds a doctorate from Montanuniversität Leoben (2000–2004) and a master's in Telematics from Graz University of Technology. His research focuses on micromachined sensors and actuators , ultrasonic and electroacoustic systems , and non-destructive testing . He pioneers innovations in wearable sensors, biomedical applications, and additive manufacturing for sensor integration. Notable contributions include air-coupled ultrasonic transducers, 3D-printed ferroelectret sensors, and robotics for STEM education. Recent work emphasizes biodegradable sensors , acousto-optic modulation , and multi-parameter medical measurement systems . His projects span from fundamental material science to applied engineering solutions, often leveraging open-source hardware. Kupnik’s labs integrate interdisciplinary approaches, combining electrical engineering, materials science, and biomedical engineering.
Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.
Philip Taranto is a Lecturer (Assistant Professor) at The University of Manchester's Physics & Astronomy department, where he leads the Quantum Information & Spatiotemporal Phenomena (QuISP) research group. He also serves as an editor for the Quantum journal. Originally from Melbourne, Australia, Taranto completed his undergraduate studies and Masters at Monash University under Dr. Kavan Modi and Dr. Felix A. Pollock, focusing on memory effects in open quantum systems. He then earned his PhD at the University of Vienna under Dr. Marcus Huber, studying quantum thermodynamics and complex temporal correlations. Following this, he held a JSPS Postdoctoral Fellowship at the University of Tokyo in Dr. Mio Murao's group before joining the University of Manchester. Taranto's research centers on quantum complexity, exploring how quantum systems' intricate behaviors can be harnessed for computational advantages. His primary focus areas include quantum information theory, open quantum dynamics, quantum thermodynamics, quantum foundations, correlations & entanglement, stochastic & complex processes, and quantum computation & simulation. His methodological approach heavily relies on the framework of higher-order quantum operations—transformations that act upon transformations themselves—which has proven valuable for developing optimal quantum interactive strategies, clarifying memory effects in open quantum processes, and analyzing foundational notions like causality. He also employs tensor networks, graphical calculus, and semidefinite programming in his research. His recent publications reveal a strong focus on quantum thermodynamics, higher-order quantum operations, and quantum memory effects. Taranto has made significant contributions to understanding the relationship between Landauer's principle and Nernst's unattainability principle in quantum cooling, developing protocols for efficient quantum system cooling with finite resources, and characterizing multi-time quantum processes with classical memory. His work on the quantum switch and higher-order quantum operations has advanced our understanding of quantum causality and indefinite causal order. JSPS Postdoctoral Fellowship (2022-2025) Editor of Quantum Journal (since June 2024) Taranto actively collaborates with multiple research groups globally, including the Murao group at the University of Tokyo, the Huber group at TU Wien, and the Modi group at SUTD Singapore and Monash University. He has worked with prominent researchers such as Simon Milz, Jessica Bavaresco, Marco Túlio Quintino, Felix Binder, Martí Perarnau-Llobet, Patryk Lipka-Bartosik, and Andrea Smirne. He is currently accepting PhD students and encourages collaboration with researchers sharing similar interests. Taranto is also committed to social responsibility, advocating for open science, climate justice, and empowering historically excluded and marginalized groups.
Prof. Marius Pesavento is a Full Professor at the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, leading the Communication Systems Group. His research focuses on sensor array processing, MIMO communication systems, adaptive beamforming, and mathematical optimization in networks. He has held academic and industry roles since 2001, including positions at mimoOn GmbH and FAG Industrial Services. Education: PhD (Doktorate) in Electrical Engineering, Ruhr-Universität Bochum (2001–2005) Master of Engineering, McMaster University (1999–2000) Dipl.-Ing. in Electrical Engineering, Ruhr-Universität Bochum (1992–1999) His research interests span robust high-resolution sensor array processing, 4G/5G mobile networks, and network information theory. Notable projects include developing tensor models for ultrasonic sensor calibration and applying machine learning to anomaly detection in network flows. His work bridges theoretical optimization and practical applications in automotive radar, 6G networks, and medical imaging. Labs/Teams: Leads the Communication Systems Group at TU Darmstadt, focusing on interdisciplinary projects in signal processing and communication systems.
Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.
Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.
Mark Mitchison is a Senior Lecturer in Quantum Science at King’s College London since April 2025 and an Adjunct Assistant Professor at Trinity College Dublin (TCD). He completed his PhD at Imperial College London and held postdoctoral positions at the University of Ulm and TCD, where he founded the Theory of Controlled Quantum Systems (ToCQS) group in 2021. His research focuses on quantum thermodynamics, open quantum systems, and non-equilibrium statistical mechanics. His work spans quantum nanomachines ultracold atomic gases mesoscopic electronic circuits quantum information theory stochastic thermodynamics with methodologies including tensor networks and full counting statistics. Mark’s publications emphasize non-equilibrium quantum thermodynamics , precision measurement , and quantum coherence . He has mentored PhD students Oisín Ó Conchubhair, Khalak Mahadeviya, Sindre Brattegard, Peter O’Donovan, and Alessandro Summer. Awards include the Royal Society-SFI University Research Fellowship. He co-organized the ASPECTS Hackathon and leads the EU-funded ASPECTS project on quantum-enhanced measurement devices.
Prof. Dr. Arnold Reusken is a full Professor of Numerical Mathematics at RWTH Aachen University, affiliated with the Institute for Geometry and Practical Mathematics (IGPM). He has held the Chair for Numerical Mathematics since 1997 and maintains an active research and academic profile in computational mathematics. Education: Ph.D. in Mathematics, University of Utrecht (1988) M.Sc. in Mathematics, University of Utrecht (1984) His research focuses on the development and analysis of numerical methods for partial differential equations, with particular emphasis on finite element methods, multigrid solvers, and computational techniques for two-phase incompressible flows and PDEs on surfaces. His work bridges theoretical numerical analysis and practical scientific computing applications in fluid dynamics and interfacial phenomena. He has made significant contributions to trace finite element methods, surface Stokes equations, and unfitted discretizations. The recent publication trend shows sustained activity in numerical methods for evolving surfaces, surface fluid dynamics, and preconditioning techniques. His work often involves rigorous error and stability analysis, demonstrating a strong theoretical foundation. Editorial Roles: Associate Editor, Journal of Numerical Mathematics (2015–present) Associate Editor, IMA Journal of Numerical Analysis (2020–present) Former Associate Editor, SIAM Journal on Numerical Analysis (2016–2021) Former Associate Editor, SIAM Journal on Scientific Computing (2002–2008) Former Associate Editor, Computing & Visualization in Science (2010–2021) Member of Advisory Board, Computing (1997–2009) Prof. Reusken has advised numerous students and researchers, though specific names are not listed in the provided text. He has been involved in collaborative research projects and has secured funding for work in numerical simulation and computational fluid dynamics. He co-authored the influential textbook Numerik für Ingenieure und Naturwissenschaftler , now in its third edition, and has contributed to other key publications in the field. He leads a research group at IGPM focused on numerical methods for interface and surface problems, contributing to both fundamental algorithm development and practical implementation in scientific computing. His team works on cutting-edge methods for simulating complex fluid systems with moving boundaries and topological changes.
Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.
Affiliations & Roles Prof. Dr. Stefan Alexander Schneider holds a professorship in Autonomous Driving and Driver Assistance Systems at the Faculty of Electrical Engineering of Kempten University of Applied Sciences. He also serves as Program Coordinator and Academic Advisor for the Master's program in Driver Assistance Systems. Additionally, he is a Visiting Professor at Shibaura Institute of Technology (Tokyo, Japan) . Education & Academic Background He completed his doctoral thesis "Adaptive Solution of Elliptic Partial Differential Equations by Hierarchical Tensor Product Finite Elements" in 2000, laying groundwork for his later research in computational methods. Research Focus His work centers on autonomous driving technologies , including: Simulation methodologies for vehicle systems Safety validation of driver assistance systems Human-machine interface design for elderly mobility solutions Standardization of testing frameworks (e.g., Open Simulation Interface) Key Contributions Recent projects include: ZuMoBe: Exploring autonomous electric vehicles in mountain valleys Development of Virtual Systems Prototyping frameworks for automotive innovation Cross-border collaboration via the VIVID German-Japanese initiative Teaching & Mentorship As a leader in one of the world's few Master's programs dedicated to ADAS/AV technologies, he mentors students in cutting-edge topics like monocular depth estimation, trajectory modeling, and interface design. His advisees have produced impactful works on autonomous scooter usability, localization algorithms, and motion planning validation.
