جستجوی استاد راهنمای دانشگاهی

Niels Henzeمشاهده پروفایل

استاد

• Human-Computer Interaction
• Predictive Models in Interactive Systems
• Mobile Human-Computer Interaction
• +۵ مورد دیگر

Niels Henze is a Professor at the Chair of Media Informatics within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg, where he has been serving since May 2018. His research centers on human-computer interaction, with a strong focus on predictive models in interactive systems, mobile interaction, augmented and virtual reality, and attention-aware computing. His research interests include: Human-Computer Interaction (HCI) Predictive modeling for runtime adaptation Mobile and wearable interaction Augmented and Virtual Reality (AR/VR) Attention-aware and context-sensitive systems Sociocognitive aspects of interactive technologies The analysis of his recent publications reveals a consistent focus on leveraging user behavior and contextual cues to build intelligent, adaptive interfaces. His work integrates machine learning with interaction design, emphasizing real-time model updates, implicit feedback, and cognitive load awareness to improve usability and user experience across mobile and immersive platforms. Scientific awards: No awards mentioned in the provided text. Niels Henze leads research in adaptive interactive systems and advises students in the field of media informatics. He has previously held a junior professorship at the University of Stuttgart and completed his doctorate at the University of Oldenburg under Susanne Boll. He is actively involved in advancing the theoretical and practical foundations of predictive and attention-aware computing. Grants and funding sources are not specified in the text. He is associated with the Institute for Information and Media, Language and Culture (I:IMSK) at the University of Regensburg, contributing to a multidisciplinary environment that bridges informatics with cultural and linguistic studies.

• Human-Automation Interaction
• Human-Robot Interaction
• Robot Anthropomorphism
• +۶ مورد دیگر

Linda Onnasch is an Assistant Professor at Humboldt Universität zu Berlin in the Department of Engineering Psychology since 2017. She specializes in human-automation and human-robot interaction, focusing on function allocation, automation reliability, anthropomorphism, and RoboEthics. She holds a PhD in Psychology from Technische Universität Berlin (2014) and a Diplom in Psychology from the same institution (2009). PhD in Psychology (2009-2014), Technische Universität Berlin Diplom in Psychology (2002-2009), Technische Universität Berlin Her research explores the impact of anthropomorphic robot design on trust and attention, flexible automation concepts, and ethical implications of human-robot collaboration. Recent work examines social loafing with robots, taxonomy development for interaction models, and the role of system transparency in trust recovery. Linda’s publications emphasize automation bias, human error identification, and empirical validation of human-robot interaction paradigms. She has contributed to journals like ACM Transactions on Human-Robot Interaction and Science Robotics , with a 2021 meta-analysis on anthropomorphism in robotics. HFES Europe Chapter Best Paper Award She has served as an ad-hoc reviewer for journals like IEEE Transactions on Human-Machine Systems and is a member of organizations including the Human Factors and Ergonomics Society Europe Chapter and the German Association of University Professors and Lecturers (DHV).

• 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.

• Epistemology
• Philosophy of Science
• Philosophical Logic
• +۷ مورد دیگر

Wolfgang Spohn is a Senior Professor at the University of Tübingen (since 2019) and Principal Investigator of the Cluster of Excellence 'Machine Learning: New Perspectives for Science' . He previously served as Professor (C4) at the University of Konstanz (1996-2018) and as a Senior Lecturer at the University of Bielefeld (1991-1996). His academic career spans multiple prestigious roles including Academia Europaea member (2015) Frege-Preis awardee (2015) Lakatos Award winner (2012) His research focuses on epistemology , philosophy of science , philosophical logic , ontology , and decision/game theory . He developed ranking theory as a foundational framework for belief dynamics and epistemic reasoning. His work bridges formal epistemology with practical applications in legal theory, economic rationality, and artificial intelligence. Recent publications highlight his contributions to Rationality frameworks for law and science Indexical utility theory and time preferences Ranking-theoretic approaches to conditionals and norms Epistemological foundations of induction Scientific honors include Lakatos Award (2012) Frege-Preis (2015) Heisenberg-Stipendium (1985) He has led major research initiatives such as the DFG Forschergruppe on counterfactuals (2011-2019) and the Reinhart-Koselleck project on reflexive decision theory (2020-present).

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

• Optimization under uncertainty
• Robust optimization
• Scenario generation
• +۸ مورد دیگر

Christina Busing is a Full Professor for Combinatorial Optimization at RWTH Aachen University, a position she has held since 2021. Previously, from 2016 to 2021, she served as a Junior Professor for Robust Planning in Medical Care at the same institution. She leads the Teaching and Research Group on Combinatorial Optimization, contributing significantly to the academic community through her research and teaching activities. Her educational background includes Mathematics studies at WWU Münster, Universidad Comlutense de Madrid, and the Technical University of Berlin. Her doctoral work was completed under the supervision of Prof. Möhring, focusing on Recoverable Robustness in Combinatorial Optimization. She has also held postdoctoral positions at institutions in Aachen, Lancaster, and Vienna. Professor Busing's research spans multiple areas of optimization theory and application. Her work focuses on optimization under uncertainty, robust optimization, scenario generation, combinatorial optimization, complexity theory, optimality criteria, and both exact and heuristic algorithms. She has made significant contributions to applying these theoretical frameworks to practical problems in healthcare, energy systems, and transportation networks. Her interdisciplinary approach bridges theoretical computer science with real-world operational challenges. Her extensive publication record demonstrates a consistent focus on robust combinatorial optimization, with recent work emphasizing applications in healthcare systems, particularly in patient-to-room assignment, primary care scheduling, and pharmacy services. She has developed novel methodologies for handling uncertainty in optimization problems, including recycling valid inequalities and designing consistent decision frameworks for two-stage optimization problems. RWTH Aachen Brigitte Gilles Award for contributions to the advancement of women in science (2022) RWTH Aachen FAMOS Award for excellent family-friendly leadership (2019) RWTH Aachen university-wide Best Teaching Award (2018) TU Berlin Best Diploma Excellence Award in Mathematics (2008) German National Scholarship (Cusanuswerk) (2003-2007) Professor Busing serves on multiple program committees including EURO (2019), INOC (2018), and ESA (2017). She is also involved with the UnRAVeL Graduate College since 2018. Her teaching portfolio includes courses on Graph and Network Optimization, Mathematical Heuristics for Discrete Optimization Problems, and Combinatorial Optimization. She has developed problem-based learning approaches for heuristic methods in decision problems across mathematics, computer science, and industrial engineering. Her research group actively collaborates on interdisciplinary projects addressing operational issues in healthcare, energy systems, and transportation networks, developing mathematically optimized solutions for complex real-world challenges.

• Econometrics
• Nonparametric Estimation
• Causal Inference
• +۴ مورد دیگر

Christoph Breunig is a Professor in the Department of Economics at the University of Bonn. His work bridges theoretical econometrics with empirical applications, focusing on nonparametric methods, instrumental variable modeling, and causal inference. His research addresses challenges in high-dimensional data, missingness mechanisms, and treatment effect estimation. University: University of Bonn Department: Economics Academic Rank: Professor Email: cbreunig@uni-bonn.de Research Trends: Nonparametric and semiparametric estimation techniques Applications of instrumental variables in causal inference Handling missing data and measurement error High-dimensional statistical models with economic applications Specification testing in complex regression frameworks Connections between microeconomic theory and empirical methods

• Mathematical Optimization
• Nonsmooth Optimization
• Multiobjective Optimization
• +۴ مورد دیگر

Bennet Gebken is a researcher at the Department of Mathematics , Technical University of Munich , affiliated with the Chair of Mathematical Optimization led by Prof. Ulbrich. His work focuses on nonsmooth and multiobjective optimization, particularly in PDE-constrained problems and numerical continuation methods. Research Interests: Bennet specializes in nonsmooth optimization, multiobjective optimization, and regularization techniques. His recent work explores convergence analysis in nonsmooth settings, second-order gradient sampling, and inverse optimization methods for data-driven decision criteria. Publications: His research spans topics like PDE-constrained multiobjective optimization, L1 penalty terms, and the hierarchical structure of Pareto critical sets. Key trends include reduced-order modeling, computational efficiency, and the interplay between optimization and machine learning. Contact: bennet.gebken@tum.de . Based at Boltzmannstr. 3, Garching b. München, Germany.

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +۱۲ مورد دیگر

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۸ مورد دیگر

Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs

• Human-Computer Interaction
• User Interface Design
• 3D Modeling Software
• +۵ مورد دیگر

Benjamin J. Lafreniere is a researcher affiliated with the University of Waterloo, Ontario, Canada. His work spans Human-Computer Interaction (HCI) , 3D Design Software , and Extended Reality (XR) . He focuses on improving user experiences through innovative input techniques, error mediation, and adaptive interfaces.

• Algorithms
• Computational Geometry
• Wireless Communication
• +۴ مورد دیگر

Stefan Funke is a researcher at the University of Stuttgart, Germany, with a focus on algorithms and computational geometry. His work spans wireless communication, route planning, and trajectory analysis. Research Interests: Algorithms, Computational Geometry, Wireless Communication, Route Planning, Trajectory Segmentation His recent publications (2024-2025) explore topics like 3D epithelial cell dynamics, graph radius computation, and polyline simplification, emphasizing scalability and efficiency. Earlier works (2017-2019) investigate contraction hierarchies, energy-efficient routing, and trajectory storage systems. Stefan collaborates frequently with Sabine Storandt, Claudius Proissl, and Tobias Rupp. He applies geometric methods to problems in wireless networks, road systems, and data structures, with a recurring emphasis on optimization and robustness.

Zhijing Jinمشاهده پروفایل

استادیار

• Natural Language Processing
• Causality
• Artificial Intelligence
• +۵ مورد دیگر

Currently a postdoctoral researcher at the Max Planck Institute for Empirical Inference under Bernhard Schölkopf, Zhijing Jin will soon join the University of Toronto as an Assistant Professor. Her work bridges Natural Language Processing , causality , and ethical AI to address challenges in large language model behavior and societal impact. Research interests include causal reasoning in language models , moral decision-making in AI agents , multi-agent collaboration , and responsible deployment of NLP technologies . She focuses on understanding how models handle ethical dilemmas, political biases, and cooperative dynamics while improving their robustness and alignment. Her publications explore collaborative LLM evaluation, ethical reasoning frameworks, and causal modeling for trustworthy AI. Key trends include causality-based NLP , LLM alignment , and socially beneficial language technology . Current projects investigate multi-agent debate systems , political bias detection , and responsible AI practices .

Andrea Kaimمشاهده پروفایل

پژوهشگر

• Landscape ecology
• Ecosystem services
• Biodiversity and trade-offs
• +۳ مورد دیگر

Dr. Andrea Kaim is the Head of the Junior Research Group AgriScape at the Helmholtz Centre for Environmental Research (UFZ) since 2022. She holds a PhD from the UFZ (2015–2022) and has extensive experience in landscape ecology, ecosystem services, and spatial conservation planning. From 2019–2022, she worked as a Research Scientist in the Professorship of Ecological Services at the University of Bayreuth. Her current research focuses on modeling farmer decisions in alpine grassland management using agent-based models, integrating biophysical, economic, and policy data. She has collaborated on projects like SusAlps and AgriScape, addressing climate change and policy impacts on land-use decisions. Education includes a German Diploma in Business Mathematics (2008–2015) from Universität Leipzig, with additional studies in Mathematics and Economics at the Universidad Complutense de Madrid (2012) and a traineeship at The University of Queensland’s Centre for Biodiversity and Conservation Science (2013–2014). Her research interests span landscape ecology, spatial conservation planning, environmental economics, and mathematical optimization. Key publications address agent-based models for nitrogen management, ecosystem service trade-offs in alpine regions, and multi-objective land-use allocation techniques. Dr. Kaim’s work bridges ecological modeling with policy analysis, emphasizing stakeholder preferences and sustainable resource management.