جست‌وجوی استاد و پژوهشگر

Daniel Frischemeierمشاهده پروفایل

استاد

• Mathematics Education
• Statistics Education
• Data Science Education
• +۵ مورد دیگر

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

• Climate Change Adaptation in Agriculture
• Land Use Modeling
• Biodiversity Conservation
• +۳ مورد دیگر

Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.

Gregory D. Erhardtمشاهده پروفایل

دانشیار

• Transportation Engineering
• Urban Planning
• Traffic Forecasting
• +۴ مورد دیگر

Gregory D. Erhardt is an Associate Professor of Civil Engineering at the University of Kentucky and serves as Associate Director of the T-SCORE Center, a multi-university consortium advancing public transit strategies. He holds a PhD from University College London’s Centre for Advanced Spatial Analysis, with prior experience in transportation planning across public and private sectors. His research focuses on transportation forecasting, big data applications, and evidence-based policy decisions, particularly addressing the impacts of emerging mobility technologies like ride-hailing on urban systems. Erhardt’s work emphasizes improving forecast accuracy and advocating for gender equity in engineering. He has been recognized with awards such as the Transportation Research Board’s Certificate of Appreciation and the Chan Wui & Yunyin Rising Star Fellowship. Education: PhD (University College London), M.S. (Northwestern University), B.S. (Cornell University). His research integrates activity-based travel models with big data to evaluate infrastructure investments and mobility trends. Key projects include analyzing public transit ridership decline, ride-hailing effects on congestion, and TNC impacts on urban transportation systems. Current initiatives include developing multi-agent simulation frameworks for on-demand transit systems. As a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study, he collaborates on travel behavior modeling. His advocacy for diversity includes leading 'Advocates and Allies' training to promote gender equity in engineering. Erhardt’s publications span journals like Transportation Research Part A and Science Advances , with a focus on policy-relevant transportation analytics.

Slava Jankinمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Slava Jankin is a Professor of Data Science and Government at the University of Birmingham’s School of Government, where he also serves as Deputy Director of the Institute for Data and AI and Founding Director of the Centre for Artificial Intelligence in Government. He is concurrently a Fellow and Founding Director of the Data Science Lab at the Hertie School in Berlin. Previously, he held a Professorship at the University of Essex and has worked at University College London (UCL) and the London School of Economics (LSE). His research bridges computational methods, governance, and climate policy, with a focus on AI applications in public institutions, climate-health surveillance, and misinformation resilience. Jankin earned a PhD in Political Science from Trinity College Dublin (2009), a Postgraduate Diploma in Statistics (2006), and a BSc from Belarus State Economic University (2002). **Education**: • PhD in Political Science, Trinity College Dublin (2009) • Postgraduate Diploma in Statistics, Trinity College Dublin (2006) • BSc Econ with Distinction, Belarus State Economic University (2002) **Research Interests**: Jankin’s work integrates AI and computational methods with governance challenges, including climate policy, health surveillance, and institutional effectiveness. He leads initiatives like the Lancet Countdown’s climate-health monitoring and the CATALYSE project on climate impacts. His research also explores digital twins for governance systems and the role of cultural diversity in societal resilience against misinformation. **Grants & Collaborations**: He advises the UN and EU on AI and data science, co-leads the Lancet Countdown, and collaborates with institutions like the Alan Turing Institute. His applied work includes developing AI tools for public service optimization and policy simulations. **Labs & Teams**: Directs the Centre for AI in Government (University of Birmingham) and the Hertie School’s Data Science Lab, fostering interdisciplinary teams to advance computational methods in public policy.

Jule Thoberمشاهده پروفایل

پژوهشگر

• Environmental Informatics
• Ecological Modelling
• Computational Hydrosystems
• +۴ مورد دیگر

Jule Thober is a Scientific Manager at the Helmholtz Centre for Environmental Research - UFZ , leading Topic 5 "Landscapes of the Future" in the Helmholtz Program and managing the Integration Platform "Robust Pictures of the Future". She contributes to cross-disciplinary research in Smart Models / Monitoring and Computational Hydrosystems , focusing on agent-based models, sustainable land management, and climate risk analysis. Roles : Scientific Manager (2022–), Postdoc (2017–2021), PhD Student (2013–2016) Projects : Copernicus Contract Ulysses 2, LandYOUs Game Development Research Interests center on socio-environmental systems, integrating agent-based models with ecological economics. Her work explores decision-making under climate uncertainty, land use policy, and resilience of pastoral systems through computational methods. Publication Trends (15 most recent) span ecological complexity, environmental informatics, and computational hydrosystems. Key themes include agent-based land use models , climate risk mitigation , and participatory decision support tools . Collaborations involve UFZ departments, international institutions, and interdisciplinary teams in Germany and abroad. She contributes to Helmholtz POF IV programs and EU-funded initiatives like 4DHydro.

Janis Nötzelمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Machine Learning
• Sensor Networks
• +۳ مورد دیگر

Dr. Janis Nötzel is a senior researcher at the Chair of Theoretical Information Technology (Technische Universität München) and leads his independent Emmy Noether research group. Previously, he held a postdoctoral position at Universitat Autónoma de Barcelona and contributed to 5G practical implementations at TU Dresden's 5G Lab. His research spans quantum information theory, physical layer security, and machine learning applications. Key focuses include Quantum channel capacities under adversarial conditions Entanglement-assisted communication Quantum software frameworks (QuNetSim, QuReed) Interplay between classical and quantum communication Security analysis for 6G networks Resource optimization in quantum systems Recent publications (2023-2025) showcase innovations in Quantum satellite communication architectures Hybrid quantum-classical clustering algorithms Photonic processor instability modeling Covert capacity of compound channels Quantum key distribution resilience Free-space Bessel beam communication He actively collaborates with 6G-life research hub and contributes to quantum network simulation tools. Grants include funding from DFG (Leibniz Program), BMBF (6G-life, Q.Link.X), and StMWi (6G Zukunftslabor Bayern).

Mariya Tonevaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Natural Language Processing
• Neuroscience
• +۵ مورد دیگر

Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.

Ralph Luettickeمشاهده پروفایل

استاد

• Macroeconomics
• Fiscal Policy
• Monetary Policy
• +۳ مورد دیگر

Ralph Luetticke is a Professor of Economics at the University of Tübingen, affiliated with the Centre for Economic Policy Research and the Stone Centre on Wealth Concentration at University College London. His research focuses on fiscal/monetary policy, business cycles, and computational methods, emphasizing household heterogeneity. Key contributions include analyzing liquidity channels of fiscal policy, military multipliers, and unconventional policy shocks. Recent work explores military spending multipliers, endogenous gridpoint methods for distributional dynamics, and the distributional impacts of monetary policy. His 2023 ERC Starting Grant ('AIRMAC') supports research into aggregate uncertainty in business cycles. Teaching includes advanced macroeconomics courses at UCL and Tübingen, emphasizing HANK models and policy analysis. Scientific awards include the ERC Starting Grant (2023). Research tools developed include the BASE for HANK toolbox (Julia) and open-source codes for heterogeneous agent modeling. His work bridges theoretical macroeconomics with empirical policy analysis, addressing modern challenges like inequality and pandemic stimulus effectiveness.

Fabian J. Stingمشاهده پروفایل

استاد

• Supply Chain Strategy
• Technology and Innovation Strategy
• Collaboration in Business Networks
• +۵ مورد دیگر

Fabian J. Sting is a Full Professor and Director of the Department of Supply Chain Management - Strategy and Innovation at the University of Cologne since 2016. He also holds a tenured Associate Professorship in Operations Management at the Rotterdam School of Management, Erasmus University since 2014. His academic journey includes a Ph.D. in Productions Management (summa cum laude) from WHU-Otto Beisheim School of Management in 2008 and postdoctoral work at INSEAD in 2008–2010. University of Cologne : Chair of Supply Chain Strategy and Innovation (2016–present) Rotterdam School of Management : Associate Professor (2014–present), Assistant Professor (2010–2014) Education : Ph.D. (2005–2008), M.Sc. in Applied Mathematics (2005–2007), Diploma in Management Science (2002–2005) Sting’s research bridges operations management, innovation strategy, and supply chain dynamics. Key areas include: Supply chain coordination under uncertainty Employee-driven process innovation and cognitive biases Strategic capacity planning and risk hedging Industry 4.0 implementation challenges Interdisciplinary integration of behavioral and technical elements in operations Recent publications focus on dual sourcing strategies, fairness in operational decisions, and employee mobility’s impact on innovation. He contributes to high-impact journals like Management Science and Production and Operations Management . Sting participates in institutional research initiatives such as: ECONtribute: Markets & Public Policy – a Cluster of Excellence addressing digital transformation, inequality, and market failures through interdisciplinary economics, political science, and law Excellent Research Support Programme (ERSP) – supporting collaborative projects like HPDnet (improving pediatric kidney disease care via health networks)

Max Pellertمشاهده پروفایل

استادیار

• Computational Social Science
• Digital Traces
• Affective Expression
• +۹ مورد دیگر

Max Pellert is a computational social scientist and cognitive scientist with faculty appointments at multiple institutions. Since 2022, he has served as an Assistant Professor at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim . He previously held an interim Professor position at the University of Konstanz and an Assistant Researcher role at Sony Computer Science Laboratories Rome. His work bridges computational methods with social science theory. Education M.Sc., University of Vienna (2017) in Middle European interdisciplinary Master's program in Cognitive Science (with distinction) Ph.D., Medical University of Vienna (2022) in Medical Informatics, Biostatistics & Complex Systems Research interests center on Computational Social Science , Digital Traces , and Natural Language Processing for emotion and sentiment analysis. He develops Temporal Adapters for tracking longitudinal emotional patterns and FAULTANA pipeline for polarization studies. His AI Psychometrics framework assesses personality-like traits in large language models. Recent publications include: (1) 2025 ACL work on political bias in LLMs; (2) 2025 ICWSM study of temporal emotion analysis; (3) 2024 Perspectives on Psychological Science paper on LLM psychometrics; (4) 2024 PNAS Nexus polarization analysis; (5) 2023 Emotion cross-cultural study of pandemic emotions. Scientific Awards Habilitation candidate status at University of Mannheim Teaching includes IS 616: Large Scale Data Analysis , IS 809: Advanced Text Mining Lab , and IS 723: Data Science Seminar at master’s and PhD levels. His Barcelona Supercomputing Center role focuses on principal investigator duties for computational social science projects.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

Oya Beyanمشاهده پروفایل

استاد

• Biomedical Informatics
• Semantic Web Technologies
• Clinical Decision Support
• +۵ مورد دیگر

Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.

Stephan Gollaمشاهده پروفایل

استاد

• Entrepreneurship
• Corporate Venture Capital
• Business Finance
• +۳ مورد دیگر

Stephan Golla is a full-time Professor of Business & Management at the Fulda University of Applied Sciences. His academic focus spans Corporate Management and Entrepreneurship, with an emphasis on venture capital dynamics, entrepreneurial orientation, and lean startup methodologies. He contributes extensively to research in technology commercialization and social identity in entrepreneurship. Ph.D. in Business & Management (2009): Empirical thesis on Corporate Venture Capital M.Sc. in Business & Management (2000): Majors in Banking, Mechanical Engineering, and Innovation Management Golla's research explores intersections between social identity and entrepreneurial performance, startup finance, and innovation ecosystems. His recent publications analyze how founders' identities influence business outcomes and the evolution of entrepreneurial finance frameworks. His awards include the 2018 Sauer Foundation Best Paper Award in Social Entrepreneurship and the 2010 Vice Chancellor’s Award for Excellence at the University of Western Sydney. He has taught courses in Start-up Management, Innovation & Technology Management, and International Management. 2018: Best Paper Award (Social Entrepreneurship) 2010: Vice Chancellor’s Award for Excellence

Sandra Transchelمشاهده پروفایل

استاد

• Supply Chain Management
• Inventory Control
• Revenue Management
• +۵ مورد دیگر

Prof. Dr. Sandra Transchel is a Full Professor of Supply Chain and Operations Management at Kühne Logistics University (KLU) in Hamburg, Germany. She has held this position since 2019, previously serving as Associate Professor (2011-2019) and Dean of Programs (2014-2015). Her academic journey includes appointments as Assistant Professor at Pennsylvania State University (2008-2011) and Visiting Assistant Professor at Tuck School of Business at Dartmouth (2011). Education includes: PhD in Business Administration, University of Mannheim (2008) Diploma in Business Mathematics, Otto-von-Guericke University Magdeburg (2004) Her research integrates supply chain management, inventory control, and revenue management with a strong focus on retail operations optimization and food supply chain sustainability . Key investigations examine perishable inventory systems, demand-supply synchronization, and substitution behavior. Current projects address food waste reduction through contract-based coordination in fresh food supply chains and development of urban food production networks (FabCity). Publications demonstrate consistent focus on inventory optimization under uncertainty, with recent work extending into pandemic impacts on humanitarian logistics and perishable inventory systems with lead-time variability. Research consistently bridges theoretical models with retail/manufacturing applications. Teaching includes Decision Analysis, Inventory and Warehouse Management, and Warehousing and Intralogistics across BSc, MBA, and MSc programs at KLU.