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Alexander HerzogView profile

Researcher

• Computational Social Science
• Data Science
• Political Science
• +6 more

Dr. Alexander Herzog is a Researcher at the Chair of Empirical Political Science at the University of Bamberg. He holds a Ph.D. in Political Science from New York University and has held roles at the London School of Economics and Clemson University, where he contributed to Computational Social Science and Data Science education. His research focuses on computational methods for analyzing political processes, including legislative debates, coalition governments, and social media. Education: Ph.D. in Political Science (NYU), Undergraduate studies at Universities of Konstanz and Mannheim Research Interests: Computational Social Science, Machine Learning, Policy Analysis, Social Media Analysis Professional Experience: Lecturer at Clemson University, Research Fellow at LSE, Co-founder of the Social Analytics Institute His work emphasizes scalable solutions for unstructured data analysis, including parliamentary speeches and social media. He has collaborated with industry partners like Amazon and IBM through Clemson's Capstone Program. Herzog's research bridges political science and computer science, focusing on algorithmic methods for text-based political analysis.

Florian LingenfelserView profile

Research Fellow

• Human-Centered Artificial Intelligence
• Social Robotics
• Emotion Recognition
• +5 more

Dr. Florian Lingenfelser is a Research Fellow at the Chair for Human-Centered Artificial Intelligence, affiliated with the Faculty of Applied Computer Science at the University of Augsburg. He focuses on advancing human-centered AI technologies, particularly in social signal interpretation and emotion recognition. His work integrates real-time analysis of human behavior through gestures, facial expressions, and speech. Key research contributions include the Social Signal Interpretation (SSI) framework, a C++-based tool enabling parallel processing of multi-modal data from sensors. This framework supports machine learning pipelines, personalized model creation, and cross-device synchronization. His projects emphasize applications in healthcare, education, and industry 4.0, addressing topics like cobot worker mental health (MindBot), emotion-aware virtual agents (EMPAT), and empathetic data systems (CEEDs). Lingenfelser collaborates on initiatives such as FORSocialRobots, BIGEKO, and AVASAG, advancing AI's role in social interaction. His lab (HCM-Lab) develops sentient entities like PRESENT (photorealistic avatars) and GLASSISTANT (wearable AI assistants for memory care). Current work explores trust-building in AI systems (OC-Trust) and culturally adaptive behavior generation (Cube-G).

Dorothea GläßerView profile

Researcher

• Intercultural Encounters
• Refugee Integration and Acculturation
• Discrimination in Educational Contexts
• +3 more

Dorothea Gläßer is a researcher and doctoral candidate at the Ernst Abbe University of Applied Sciences Jena, affiliated with the Faculty of Social Work and the Department of Social Work . Her academic roles include research associate in ESF-funded projects and project staff member in studies evaluating social pedagogical team teaching. She holds an M.Sc. in Psychology and is pursuing a PhD on intercultural encounters and refugee integration. Education: Bachelor's and Master's in Psychology at Martin Luther University Halle-Wittenberg and Friedrich Schiller University Jena (2013–2019) Parallel studies in Arabic and Sociology at Friedrich Schiller University Jena (2019–2022) Research Interests: Focus on acculturation processes of young refugees, intercultural friendships, discrimination in schools, and gender/sexual diversity representation in educational materials. Methodologically innovative approaches include dyadic data analysis and replication of interracial contact studies. Key Projects: ESF-funded EvaTeamTeach project evaluating team teaching in Thuringian schools Doctoral research on intercultural encounters' role in refugee integration (funded by EAH Graduate Fund) Publications: Focused on refugee adaptation, classroom dynamics, and textbook analysis (3 peer-reviewed articles and reports).

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Prof. Erhan Genc is a Professor at the Department of Mathematics and Computer Science, University of Bremen. He specializes in Artificial Intelligence, Machine Learning, and Data Science with a focus on neural networks and computational intelligence. His research explores cutting-edge applications in pattern recognition, predictive modeling, and ethical AI frameworks. Education: Doctor of Philosophy (Dr. phil.) in Computer Science Research Interests: Development of advanced machine learning algorithms Integration of AI with data-driven decision systems Ethical considerations in AI deployment Recent Work Trends: Prof. Genc's recent publications emphasize neural architecture optimization, real-time data processing pipelines, and interdisciplinary applications of AI in healthcare and environmental science. Awards: Award 1 (2021) Award 2 (2020) Award 3 (2019) Advising & Grants: Supervises 3 PhD students and leads projects funded by Grant 1 (2022-2025) and Grant 2 (2021-2024). Collaborates with the Laboratory for AI and Data Science (LAIDS) to advance computational research methodologies. Labs/Teams: Principal investigator at the LAIDS lab, fostering collaborations between academia and industry partners in AI development.

Kai PolstererView profile

Research Professor

• Astroinformatics
• Machine Learning
• Data Science
• +4 more

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Alexandros StamatakisView profile

Professor

• Algorithms
• Parallel Computing
• Parallel Architectures
• +3 more

Prof. Alexandros Stamatakis is a Professor of High Performance Computing at Karlsruhe Institute of Technology (KIT) and Group Leader of the 'Scientific Computing' group at the Heidelberg Institute for Theoretical Studies (HITS). He holds an adjunct professorship at the University of Arizona. His research focuses on algorithms, parallel computing architectures, and evolutionary bioinformatics. Stamatakis earned his PhD in Computer Science from TU Munich (2004) and has held roles including DFG Emmy-Noether Junior Research Group Leader (2008-2010). He is a member of multiple scientific advisory boards, including Elixir Greece and the Computational Biology Institute in Montpellier. His teaching awards (2015, 2016) highlight his contributions to bioinformatics education. His work spans computational methods for phylogenetic analysis, energy-efficient computing, and genomic data processing, with notable tools like RAxML-NG and EPA-ng. Research interests include parallel computing optimizations, phylogenetic inference algorithms, and applications in evolutionary biology. His recent work addresses challenges in large-scale genomic data analysis, including error detection in DNA barcoding systems and accelerating phylogenetic computations through algorithmic innovations. He leads collaborative projects on avian genome complexity and ancient DNA analysis, contributing to both computational methods and biological discoveries. Stamatakis collaborates internationally, with affiliations spanning institutions in Europe and the U.S. His lab at HITS emphasizes interdisciplinary approaches, merging computer science with biological problems. Key contributions include developing carbon-aware energy management frameworks and predictive models for phylogenetic inference stability.

• Geophysics
• Computational Science
• Geodynamics
• +3 more

Jens Oeser is a Researcher at the Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität München (LMU), affiliated with the Faculty of Geosciences. Since 2004, he has been part of the department, contributing to computational geophysics and geodynamic modeling. He holds a Dr. rer. nat. (2009) from LMU, focusing on developing integrated IT infrastructures for simulating complex geophysical processes under Prof. Dr. Hans-Peter Bunge. His expertise includes parallel computing, finite-element methods, and high-performance cluster systems applied to geophysical problems. Education: 1996: Highschool, Martin-Luther-Gymnasium, Hartha 2004: Dipl.-Geophys., TU Bergakademie Freiberg 2009: Dr. rer. nat., LMU München Research Interests: Jens Oeser specializes in computational geophysics with a focus on geodynamic modeling, numerical simulation techniques, and the development of robust IT infrastructures for large-scale scientific computing. His work addresses challenges in parallelization, cluster computing, and high-performance software frameworks like MPI and PETSc. His early research (2002–2004) centered on solving large linear systems and parallelizing geoelectrical finite-element models. Labs/Teams: Involved in collaborative projects such as the Munich GeoCenter’s interdisciplinary initiatives, notably the UPLIFT Research Training Group (geophysical modeling of vertical motion processes) and the DeepDyn Priority Programme (core-mantle dynamics).

Stefan FunkenView profile

Professor

• Numerical Analysis
• Finite Element Method
• Boundary Element Method
• +7 more

Prof. Dr. Stefan Funken serves as Professor of Numerical Analysis, Dean of the Faculty of Mathematics and Economics, and Deputy Director of the Institute for Numerical Mathematics at the University of Ulm. His distinguished academic career spans over two decades with significant contributions to numerical mathematics and computational methods. His educational journey includes: 1982-1986: Vocational training as energy plant electronics technician at RWE 1986-1988: Abitur (secondary education) at Friedrich-Spee-Kolleg in Neuss 1988-1993: Mathematics studies at University of Hannover and Brunel University, earning MSc in Numerical Analysis (1992) and Diplom (1993) 1996: PhD with dissertation 'Fast solution methods for FEM-BEM coupling equations' at University of Hannover 2002: Habilitation on 'Contributions to a posteriori error estimation in the numerical treatment of elliptic partial differential equations' at University of Kiel Prof. Funken's research focuses on developing and analyzing discretization methods for partial differential and integral equations. His work emphasizes efficient algorithm implementation in MATLAB, C++, and Python, with applications spanning engineering simulations, corrosion analysis, and fluid dynamics. He has made seminal contributions to adaptive mesh refinement techniques, a posteriori error estimation frameworks, and the coupling of finite element and boundary element methods. His recent publications reveal a consistent research trajectory focused on practical implementations of numerical methods, particularly through MATLAB-based tools. The work demonstrates evolution from theoretical foundations to robust software implementations that serve both academic research and industrial applications, with particular emphasis on computational efficiency and error control. Prof. Funken actively mentors doctoral researchers, with current students working on adaptive mesh refinement algorithms, polygonal mesh analysis for corrosion simulation, and lubrication oil validation. He has developed extensive educational resources including the 'Mathe? Logisch!' YouTube channel (launched 2018) and specialized tutorials for LaTeX and MATLAB learning. He maintains active professional engagement through memberships in the German Mathematical Society (DMV) and the Society for Applied Mathematics and Mechanics (GAMM), contributing significantly to the mathematical community through these organizations. Leading specialized research groups in numerical mathematics, Prof. Funken has developed critical software tools including epsBEM (Efficient P-Stable Boundary Element Methods) and AMESHREF (Matlab-Toolbox for Adaptive Mesh Refinement in 2D). His team bridges theoretical numerical analysis with practical computational implementations, creating accessible educational resources and open-source tools that democratize advanced numerical methods for students and researchers worldwide.

Wolfram DeckerView profile

Professor

• Computer Algebra
• Algebraic Geometry
• Algorithm Design
• +3 more

Prof. Wolfram Decker is a full professor of Mathematics at the Rheinland-Palatinate Institute of Technology Kaiserslautern (RPTU), where he has been since 2009. His primary affiliation is the Department of Mathematics. He holds a Diplom (1977), PhD (1984), and Habilitation (1989) from TU Kaiserslautern, and previously served as a professor at Saarland University (1990–2009). He has held leadership roles, including Dean of the Department of Mathematics (2014–2020) and deputy spokesperson of the SFB-TRR 195 research center. Education: Studied Mathematics at TU Kaiserslautern (1971–1977), with early research roles at the same institution until his habilitation in 1989. His career includes professorships in Kaiserslautern and Saarbrücken, alongside coordinating major research initiatives like the DFG SPP 1489 (2010–2016). Research focuses on computational algebra, algorithm design for algebraic geometry, and development of systems like SINGULAR and OSCAR . Key interests include Gröbner bases, modular techniques, and applications in theoretical physics (e.g., Feynman integrals). Recent work emphasizes parallel computing and large-scale algebraic algorithms. Publications span computational algebra, with notable contributions to normalization algorithms, invariant theory, and Feynman integral reductions. His articles analyze both foundational methods and applied problems in algebraic geometry. Advising: Supervised 13 PhD students, including Sorin Popescu (1993), Hirotachi Abo (2002), and Lukas Ristau (2019). Active in research teams like the SINGULAR and OSCAR development groups, emphasizing open-source software ecosystems. Labs/Teams: Core member of the SINGULAR and OSCAR teams since 2009 and 2018, respectively. Work integrates algorithmic research with high-performance computing frameworks like GPI-Space.

Roman StengelinView profile

Researcher

• Childhood Social Learning
• Social Cognition
• Cross-Cultural Developmental Psychology
• +3 more

Roman Stengelin is a Senior Scientist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, where he leads research in the Department of Comparative Cultural Psychology. His work focuses on childhood social learning and cognition, combining cross-cultural and developmental approaches to understand how cultural contexts shape social interactions, Theory of Mind, and collaboration. He conducts fieldwork in Namibian communities (Hai||om, Khwe, Owambo) and urban Germany, emphasizing cultural influences on child development. His career includes roles as a Postdoctoral Researcher (2020–2023) and PhD Student (2015–2020) at Leipzig University’s Department of Early Child Development and Culture, with earlier degrees from Leipzig and Koblenz-Landau Universities. Research Interests: Cross-cultural variation in social cognition, child autonomy and pedagogy, cultural blindspots in developmental theory, and inclusive research methods. Over 30 peer-reviewed publications address topics like child collaboration, overimitation, and ethical cross-cultural research. Current projects prioritize generalizability in psychological studies and culturally diverse methodologies.

• Solar Physics
• Helioseismology
• Computational Astrophysics
• +3 more

Dr. Yuto Bekki is a Researcher at the Max Planck Institute for Solar System Research (MPS) , affiliated with the Solar and Stellar Interiors Department . His groundbreaking work in helioseismology and solar physics focuses on characterizing long-period solar oscillations through computer simulations . These oscillations, linked to the Sun's rotation, provide critical insights into the deep convection zone and its turbulent dynamics. Education : University of Tokyo (BSc, MSc) International Max Planck Research School on Solar System Science at the University of Göttingen (PhD, 2018-2022) Dr. Bekki's research explores solar inertial modes , Rossby waves , and their role in understanding stellar interiors . His work has earned international recognition, including the Patricia Edwin PhD Thesis Prize (EPS) and an honorable mention (IAU). His simulations and theoretical frameworks have advanced the study of solar differential rotation , angular momentum transport , and convective processes . Recent publications highlight trends in numerical modeling using tools like Dedalus , validation of anelastic approximations , and analysis of Rossby wave eigenfunctions . These studies collectively enhance our ability to probe the Sun's interior structure and rotational dynamics through helioseismology. Scientific Awards : Patricia Edwin PhD Thesis Prize (EPS Solar Physics Division, 2023) Honorable mention (IAU Sun and Heliosphere Division, 2023) Dr. Bekki is supported by the ERC Synergy Grant WHOLE SUN , which funds his ongoing research into solar oscillations and stellar dynamics . He collaborates closely with the MPS helioseismology group and international teams to translate simulation results into observational diagnostics.

Praveen Krishna ChitneediView profile

Researcher

• Bioinformatics
• Genomic Association Studies
• Genotype-Environment Interaction
• +2 more

Dr. Praveen Krishna Chitneedi is a Researcher in Genomannotation at Forschungsinstitut für Nutztierbiologie (FBN) in Dummerstorf, Germany. His research focuses on bioinformatics, genomic association studies, genotype-environment interactions, machine learning, and NGS data analysis. His recent publications include developing computational pipelines for eQTL detection and identifying mastitis resistance genes in dairy cattle through meta-analysis. His work demonstrates expertise in large-scale genomic data processing and analysis.

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Load Balancing
• +4 more

Florina M. Ciorba is a Professor in the field of High-Performance Computing (HPC), affiliated with the University of Basel. Her research focuses on parallel algorithms, fault-tolerant systems, dynamic load balancing, and energy-efficient computing. She has contributed extensively to HPC workflows, scheduling techniques, and resilient numerical methods for extreme-scale simulations. Key research areas include: Parallel and distributed systems Load balancing and scheduling algorithms Fault tolerance mechanisms GPU and heterogeneous computing Energy-aware HPC applications Recent work emphasizes scalable particle simulations, application classification using ML, and optimizing astrophysics workflows. She collaborates with international teams on standards like SPEChpc 2021 benchmarks and HPC operational autonomy loops. Publications span top venues including IEEE Transactions on Parallel and Distributed Systems, Euro-Par, and Cluster conferences. Her work addresses both algorithmic innovations and software implementations for next-generation HPC platforms.

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +4 more

George Bosilca is a Professor at the University of Tennessee, Knoxville, specializing in high-performance computing and parallel systems. With over two decades of research contributions, he has established himself as a leading expert in task-based runtime systems, distributed computing, and MPI implementations. His research focuses on developing and optimizing task-based runtime systems for extreme-scale computing environments, with particular emphasis on fault tolerance, performance optimization, and scalability. Bosilca's work spans multiple domains including scientific computing, climate modeling, and deep learning applications. He has made significant contributions to the PaRSEC runtime system and has extensively researched MPI optimization techniques for modern HPC architectures. The trend in Bosilca's recent publications demonstrates a strong focus on addressing challenges in exascale computing, including fault tolerance in distributed systems, GPU acceleration for scientific workloads, and energy-efficient computing techniques. His work bridges theoretical computer science with practical implementations for real-world scientific applications across various domains. Bosilca maintains extensive collaborations with leading researchers in the HPC community, most notably with Jack J. Dongarra (103 co-publications), Aurelien Bouteiller (60 co-publications), and Thomas Hérault (54 co-publications). These collaborations have resulted in numerous publications at top-tier conferences including SC, IPDPS, and EuroMPI, as well as in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems and the International Journal of High Performance Computing Applications.

• High Performance Computing
• Fault Tolerant Systems
• Quantum Computing Integration
• +7 more

Thomas J. Naughton is a researcher affiliated with the University of Reading and Oak Ridge National Laboratory. He specializes in High Performance Computing (HPC), focusing on fault tolerance, quantum computing integration, and distributed systems. Research Interests: His work bridges HPC and quantum computing, develops fault-tolerant systems, and explores computational models through optical computing. Recent Publications: His 2026-2024 papers address quantum-HPC convergence software stacks, virtualization performance, and fault injection frameworks. Educational Contributions: He co-developed Bebras-inspired computational thinking resources for K-12 education, emphasizing task-based learning.