Pauli Miettinen is a Professor of Data Science at the University of Eastern Finland, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. His research focuses on data science methodologies, including matrix and tensor decompositions, redescription mining, and social network analysis. Key applications span ecological niche modeling, health data analysis, and parliamentary candidate opinion analysis. He leads the Algorithmic Data Analysis research group and contributed to the Neuro-Innovation project (2021–2026). Recent work includes advancements in differentially private redescription mining and hyperbolic community graph generation. His publications emphasize efficient algorithms for data mining tasks like biclustering and non-negative matrix factorization. Selected achievements include developing the HyGen graph generator and pioneering techniques for interpretable data representation. His research bridges theoretical method development with practical applications in diverse domains.
Marcus ANG Teck Meng is an Associate Professor of Operations Management (Education) at the Lee Kong Chian School of Business, Singapore Management University. He is also the Academic Director and Track Coordinator of the International Trading Institute (ITI). His academic journey began at the National University of Singapore, where he earned dual B.Sc. degrees in Mathematics, followed by an S.M. in High Performance Computing and a Ph.D. in 2008. Ph.D., National University of Singapore, 2008 S.M., High Performance Computing for Engineering Systems, Singapore-MIT Alliance under NUS, 2003 B.Sc. (Hon), Mathematics, National University of Singapore, 2002 B.Sc., Mathematics, National University of Singapore, 2001 His research focuses on the theoretical and applied aspects of operations management, particularly in inventory management , stochastic models , and robust optimization . His work bridges mathematical rigor with real-world applications in supply chains, healthcare, and e-commerce. He has made significant contributions to warehouse storage optimization, hospital resource planning, and online retail fulfillment under uncertainty. The recent articles reflect a strong trend in applying robust optimization and stochastic modeling to logistics and service operations. His research spans domains such as healthcare analytics, e-commerce fulfillment, and inventory systems. Keywords like operations research , supply chain management , and data analytics dominate, with sub-fields including appointment scheduling, risk measures, and warehouse layout optimization. His teaching excellence has been widely recognized: Winner, SMU Undergraduate Excellent Teacher Award (2020) Winner, SMU Undergraduate Innovative Teacher Award (2018) Nominee, SMU Undergraduate Innovative Teacher Award (2021, 2022) Multiple appearances on SMU Dean’s Teaching Honour List (Undergraduate and Postgraduate) MPA Research Fellow (AY2019–2023) Dean’s List, NUS Department of Mathematics (AY1998–2001) He leads the International Trading Institute (ITI) and has been involved in pedagogical innovation through TEL grant-funded projects such as Inn or Out and Pricing Boss . While no formal list of advisees is provided, his role as Academic Director and course coordinator suggests active mentorship and supervision of students in operations and analytics. He has no known lab or research team explicitly mentioned, but his case studies and projects indicate strong industry engagement and applied research leadership.
Prof. Dr. Martin Erdmann is a University Professor of Experimental Physics (High Energy Physics) at RWTH Aachen University, affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. He leads research in high-energy particle physics through the CMS experiment at CERN and the Pierre Auger Observatory in Argentina. His work integrates cutting-edge digital methods, including deep learning and cloud-based data analysis via the VISPA platform. PhD, University of Freiburg (1990) Habilitation, University of Heidelberg (1996) Heisenberg Fellow at DESY and University of Karlsruhe (1997–2002) Professor at RWTH Aachen since 2004 His research interests span Higgs and top-quark physics, cosmic ray detection, radio-based shower measurement, and AI-driven data analysis. He actively contributes to physics education through textbooks and open-access video lectures. His recent publications reflect strong trends in applying deep learning to particle and astroparticle physics, particularly in event reconstruction and simulation. He leads major initiatives like the ErUM-Data-Hub and DIG-UM, advancing digital transformation in fundamental research. Heisenberg Fellowship (DESY and Karlsruhe) Chair, DPG Working Group on Physics, Modern IT, and AI (2019–2021) Project Leader, ErUM-Data-Hub (since 2021) Chair, DIG-UM Community Organization (2021–2024) Prof. Erdmann advises students at all levels and fosters innovation in data science for physics. He has secured leadership roles in international collaborations and promotes sustainable, resource-aware computing in research. His lab develops advanced detector technologies and simulation tools like CRPropa for cosmic ray propagation. Future work includes probing Higgs self-coupling, identifying cosmic ray sources, and refining AI models for physics discovery.
Quentin Stiévenart is a researcher at Université du Québec à Montréal, focusing on abstract interpretation, concurrency, and static analysis. His work spans programming language design, software verification, and tool development for WebAssembly and functional languages like Racket and Scheme. Active in organizing and reviewing for conferences including SPLASH, ICFP, ECOOP, and SAS Developed tools such as Wassail for WebAssembly static analysis and RacketLogger for educational purposes Contributions include theoretical work on effect-driven flow analysis and practical advancements in concolic execution abstraction His research addresses challenges in concurrency verification, cyclic reinforcement in incremental analysis, and security-focused taint tracking across multiple language paradigms.
Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.
Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning
Luca DE'MEDICI is a Professor at the Laboratoire de Physique et Etudes des Matériaux (LPEM) at ESPCI-PSL in Paris. His research focuses on condensed matter theory, particularly strongly correlated materials, unconventional superconductors, and theoretical methods for quantum materials. He leads a research group funded by an ERC Consolidator Grant (2016-2022) exploring correlated materials and their applications in superconductors and thermoelectrics. His work includes groundbreaking studies on Hund’s metals, Mott transitions, and electronic compressibility in FeSe. He has organized international conferences like 'Cutting-Edge Topics in Quantum Materials' and contributed to the development of the EDIpack computational package for quantum impurity problems. DE'MEDICI has been recognized for his contributions to understanding electronic correlations in superconductors and correlated systems.
Dr. Ruth Misener is a Professor in the Department of Computing at Imperial College London and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022-27). Her research focuses on computational optimization, including global optimization of mixed-integer nonlinear programs, optimization under uncertainty, and integration with machine learning. She leads the Computational Optimization Group, which develops open-source tools like GALINI, and collaborates with industry partners such as Royal Mail for logistics optimization. Her research interests span optimization challenges in engineering, bioprocess design, and healthcare, including bioreactor optimization and disease trajectory modeling for leukemia. She has been awarded the Royal Academy of Engineering Research Fellowship, supporting her work on hybrid computational/experimental platforms for biomedical applications. Recent projects include scheduling optimization under uncertainty, Bayesian methods for experimental design, and robust optimization for heat recovery networks. Her work emphasizes explainable optimization and safety-critical decision-making, with applications in energy systems, pharmaceuticals, and manufacturing. Ruth’s contributions include over 50 publications, with a focus on algorithm development and industrial impact. She actively participates in conferences and chairs workshops on optimization, such as the Mixed Integer Programming Workshop and the British-French-German Conference on Optimization.
Victor J. Milenkovic is a Professor and Department Chair in the Department of Computer Science at the University of Miami's College of Arts and Sciences. His research focuses on computational geometry, geometric algorithms, and their applications in robotics, computer-aided design (CAD), and high-performance computing. He specializes in developing robust algorithms for handling degenerate cases in geometric computations, free space construction for robotic motion planning, and efficient implementations of geometric operations using modern hardware like GPUs. Key contributions include: Advanced techniques for detecting degenerate predicates in computational geometry GPU-accelerated Minkowski sum computations Robust free space construction for polyhedra Geometric rounding methods for preserving mesh topology His work emphasizes practical implementations with provable correctness and efficiency, validated through applications in CAD, robotics, and HPC environments. No academic awards are explicitly listed in the provided materials. Advising and grants: No student advisees or grant details are provided in the profile. His research has been applied to problems in algorithmic robustness, geometric modeling, and parallel computing. Labs/Teams: No specific lab or team affiliations are mentioned beyond his departmental role.
Victoria Shao is a Teaching Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC). She specializes in electromagnetic compatibility (EMC), computational electromagnetics (CEM), and high-power microwave technology. Her work focuses on advancing numerical methods for transient electromagnetic analysis, stochastic modeling in complex enclosures, and the design of integrated electronic systems. Affiliations: Holonyak Micro and Nanotechnology Laboratory at UIUC Education: B.S. in Electrical Engineering (USTC, 2003), Ph.D. in Electromagnetics (Chinese Academy of Sciences, 2008) Prior positions: Researcher at ElectroScience Laboratory, Ohio State University (2009–2014) Research Interests: Dr. Shao’s work bridges computational methods with practical engineering challenges, emphasizing: Stochastic Green’s function approaches for statistical wave physics Multi-physics analysis of electronic systems Development of scalable algorithms for high-performance computing Nanotechnology integration for 3D RF components Her research has led to innovations in: Self-rolled-up membrane (S-RuM) nanotechnology for compact inductors Supercomputing-driven radio wave propagation models for urban environments Parallel-in-space-and-time electromagnetic simulation methods Awards: She has received multiple recognitions, including Best EMC Paper finalist awards (2022, 2023) and a Best Paper Award in IEEE Transactions (2017). Teaching and Contributions: Dr. Shao teaches core ECE courses such as ECE 110, ECE 210, and specialized EMC courses (ECE 498 YS3/YVS). She pioneers educational strategies using visualization tools and asynchronous learning to enhance STEM accessibility.
Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA
Pooran Memari is a CNRS Researcher at the Laboratoire d'Informatique de l'École Polytechnique (LIX), UMR CNRS 7161, Institut Polytechnique de Paris, and an Affiliate Professor (part-time) at École Polytechnique. She leads research in geometric modeling within the GeomeriX team at LIX-Inria, focusing on theoretical foundations and applications in accessibility and neurocognition. Her academic journey includes: HDR (Habilitation à diriger des recherches), Institut Polytechnique de Paris, 2024 Ph.D. in Geometric Modeling, INRIA Sophia-Antipolis, 2010 Master in Image and Geometry, University of Nice-Sophia Antipolis, 2006 Engineering Degree, École Polytechnique, 2005 Bachelor in Mathematics, Sharif University of Technology, 2002 Dr. Memari's research bridges geometric modeling, computational geometry, and computer graphics with real-world impact. She pioneers techniques in shape representation, point pattern synthesis, and surface reconstruction, advancing proximity encoding and clustering algorithms. Her work extends to accessibility applications—developing geometric models for visually impaired navigation through multisensory perception—and neurocognition validation via tactile interfaces. This interdisciplinary approach integrates theoretical rigor with practical tools like the CGAL library. Recent publications (2024-2019) reveal a cohesive trajectory in geometric processing: advancing point pattern synthesis through image-based editing (Patternshop), stability-incorporated neighborhood graphs (SING), and multi-class disk distributions; innovating surface reconstruction via Voronoi-based methods (BallMerge); and expanding applications to virtual worlds simulation and neurocognitive accessibility. Key themes include bridging discrete geometry operators with high-dimensional data analysis and translating theoretical insights into tools for visual computing. Dr. Memari actively mentors the next generation of researchers, advising eight PhD students on topics ranging from generalized Voronoi diagrams to neurocognition-driven tactile navigation. She coordinates Computer Science Projects for École Polytechnique's Bachelor program since 2019 and co-leads the Interaction, Graphics & Design master's program at IP-Paris. Her leadership extends to editorial roles at Computer Graphics Forum and Graphical Models Journal, alongside prominent conference positions including SGP Program Co-Chair (2023) and Eurographics STARs Co-Chair (2025). As a core GeomeriX team member, she drives collaborative projects in geometric modeling and virtual environments. She coordinates the LIX Seminar since May 2025 and serves on the French Eurographics Chapter board, fostering community engagement through initiatives like the IGD master's program and Eurographics symposia.
Stephen Siegel is an Associate Professor at the University of Delaware with a joint appointment in the Department of Computer and Information Sciences and the Department of Mathematical Sciences . Holding a PhD in Mathematics from the University of Chicago (1993), he transitioned from finite group theory research to formal methods in computer science, focusing on verification of parallel and scientific software. His research centers on the Verified Software Laboratory (VSL) and the CIVL Model Checker for HPC program verification. Recent work includes formal verification of PETSc components at CAV 2025 and collective contract frameworks for message-passing programs. Research Interests Formal methods for software verification Parallel and HPC software reliability Model checking techniques Application of mathematical logic to computing Academic Service Highlights Program Committee & Publication Chair, CAV 2025 Co-organizer, International Workshop on Verification of Scientific Software (VSS 2025) Chair, VerifyThis competition (2023) Editorial service at IEEE Transactions on Software Engineering (2015-2019) Teaching Portfolio CISC 404/604: Logic in Computer Science CISC 414/614: Formal Methods in Software Engineering CISC 372: Parallel Computing (MPI/OpenMP/CUDA instruction) Advanced Topics courses: Model Checking, Abstract Interpretation
Alejandro Lleras is a Professor at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts and Sciences and the Beckman Institute for Advanced Science and Technology . He serves as the Associate Dean for Inclusive Excellence and is a member of the U.S. National Committee for Psychological Science . Ph.D. from Pennsylvania State University (2002) Current co-director of the Vision Lab with Simona Buetti His research focuses on visual attention, perception, and feature-based processing , with recent work exploring emotional memory modulation, color-shape interactions in search tasks, and peripheral vision limitations. Key themes include: Temporal attention and sustained concentration failures Feature guidance in complex visual environments Role of perceptual similarity in search efficiency Impact of emotional arousal on relational memory His publications (2012–2025) demonstrate expertise in visual cognition, attentional theories, and cognitive modeling , with a strong emphasis on experimental validation of computational models. Awards include the NSF CAREER award and Psychonomic Society Mid-Career award . As co-founder of the Spark Society , he actively promotes diversity in cognitive sciences. His lab investigates topics like contextual cueing , feature boosting theories , and online eye-tracking methodology , supported by NSF grants including the Louis Stokes Alliance for Minority Participation program.
Francisco Carreras Martínez is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His research is centered on differential geometry, particularly in the areas of Riemannian and pseudoriemannian almost-product manifolds, curvature tensors, and geometric invariants. His research interests include Differential Geometry , Riemannian Geometry , Almost-Product Manifolds , Curvature Tensor , Index Form of Geodesics , and Kaehler Submanifolds . His work bridges pure mathematics with applications in mathematical biology, notably in computational modeling of cardiac electrophysiology. The most recent publications indicate a sustained focus on geometric structures and their analytical properties, with a notable interdisciplinary contribution in 2011 on a parallel computational model of the heart. This reflects a trend toward applying advanced geometric and analytical methods to complex biological systems. Francisco Carreras Martínez completed his doctoral studies at the Universitat de València with a thesis on linear invariants of Riemannian almost-product manifolds, supervised by Dr. Antonio Martínez Naveira. He has collaborated with researchers such as Vicente Miquel, Fernando Giménez, and Antonio M. Naveira. While specific grant information is not provided, his publication record suggests involvement in collaborative and possibly funded research projects, particularly in interdisciplinary domains. He is affiliated with the research group in Geometry and Topology at the University of Valencia, contributing to both theoretical developments and applied mathematical modeling.
