جست‌وجوی استاد و پژوهشگر

Annegret Wengمشاهده پروفایل

استاد

• Financial Mathematics
• Insurance Mathematics
• Number Theory
• +۴ مورد دیگر

Annegret Weng serves as Professor and Dean of the School of Applied Mathematics and AI at Stuttgart University of Applied Sciences since 2009, with board membership in the German Society for Financial and Insurance Mathematics (DGVFM) since 2023. Her career bridges industry expertise from Allianz and SV Sparkassenversicherung with academic leadership in quantitative disciplines. Diploma in Mathematics, University of Frankfurt PhD, University of Essen (1999) Postdoctoral Research, Universities of Essen and Mainz (1999-2001) Her research integrates pure mathematics with financial applications, focusing on number theory (De Bruijn sequences, cyclic matrices) and actuarial science (Solvency II compliance, disability risk modeling). Recent work explores AI's role in educational assessment and regulatory frameworks for insurance algorithms, demonstrating consistent innovation across theoretical and applied domains. Publication trends (2015-2024) reveal dual expertise: advancing actuarial methodologies through parameter uncertainty modeling while pioneering AI-driven educational tools. Her work consistently connects recreational mathematics (e.g., magical properties of linear recursions) with industry-critical applications in insurance risk assessment. As head of the Laboratory for Quantitative Methods of Actuarial Mathematics , she leads research in computational risk modeling. Her industry engagement includes the German Actuarial Association's Big Data in Life Insurance Working Group (since 2024) and DGVFM's School Working Group (since 2019), driving collaboration between academia and insurance regulators.

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +۳ مورد دیگر

Rose McCarty is an Assistant Professor of Mathematics at Princeton University's School of Arts and Sciences, specializing in graph theory and combinatorics with a focus on structural properties of sparse graphs. Her academic journey includes: Current position: Assistant Professor, Department of Mathematics, Princeton University Oberwolfach Leibniz Graduate Student (2019) US Junior Oberwolfach Fellow (2025) Collaborations with Paul Wollan and James Geelen on graph theory research Professor McCarty's research explores graph coloring, graph minors, and algorithmic aspects of graph classes with bounded expansion. Her work bridges theoretical mathematics and computer science, emphasizing the structural foundations of sparse graph families. As an early-career faculty member, she is developing her research program and actively participates in the international mathematics community through workshops and collaborative projects at institutions like Oberwolfach.

Miriam Primbsمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Methods
• Mathematical Simulation
• +۴ مورد دیگر

Professor Miriam Primbs is a faculty member at Hochschule Ruhr West (HRW) in the Institute of Natural Sciences, where she teaches Mathematics and Simulation. She has been a professor at HRW since 2011 and coordinates the HRW research focus MARTA since 2022. Professor Primbs studied Mathematics and Physics (Diplom and 1. Staatsexamen) and earned her PhD in Mathematics at the University of Duisburg-Essen. Her career includes a research stay at IMATI CNR in Italy, teaching on the school ship Thor Heyerdahl, and working in Research and Development at Siemens Energy Mülheim before joining HRW. This blend of academic and industry experience informs her teaching and research approach. Her research focuses on applying mathematical and numerical methods to technical problems across engineering disciplines, from mechanics to electrical engineering. As coordinator of the MARTA research focus, she leads various projects including those funded by BMBF and collaborations with industry partners like Siemens Energy. Her scholarly work spans wavelet analysis, numerical methods, signal processing, and power systems analysis. Professor Primbs teaches Engineering Mathematics I and II, Mathematics for E-Commerce, MATLAB, Scientific Simulation, and LaTeX Basics. She employs a flipped classroom approach for foundational courses, providing students with tailored materials and prompt feedback through weekly exercises. In higher semesters, she emphasizes project-based learning and specialized topics. Currently supervising doctoral student Martin Wachs in cooperation with University of Duisburg-Essen Member of the Senate Committee for Research and Transfer at HRW Encourages industry collaboration for practical research applications Professor Primbs maintains strong connections between academic research and industrial applications, leveraging her Siemens Energy experience to bridge theory and practice in mathematical engineering.

• Algorithms
• Bioinformatics
• Comparative Genomics
• +۵ مورد دیگر

Séverine Bérard is a Lecturer in the Computer Science Department at the Faculty of Sciences, University of Montpellier, where she holds a Habilitation à Diriger des Recherches (HDR) since 2016. Her institutional roles include: Co-director of the Bioinformatics Master's program Elected member of the University Board of Directors (since 2021) Elected member of the Faculty of Sciences Council (since 2017) Appointed member of the University Training Committee (since 2016) Her research at ISE-M's Phylogeny and Molecular Evolution Team bridges computer science and bioinformatics, with core focuses on: Development of text algorithms and graph-based methods for genomic analysis Comparative genomics and ancestral genome reconstruction Quality assessment of genome assemblies and meta-assemblies Evolutionary modeling of chromosome rearrangements Her recent publications (2016-2021) demonstrate strong emphasis on algorithmic innovations for evolutionary genomics, including genome scaffolding techniques, DCJ rearrangement models, and ancestral genome reconstruction methods applied to organisms ranging from mosquitoes to bacteriophages. In education, she teaches across all academic levels (L1-M2) in both practical computer science (networks, programming) and theoretical disciplines (discrete mathematics, text algorithms), while leading curriculum development as co-director of the Bioinformatics Master's program.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.