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Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

• Electronic Design Automation (EDA)
• Computer Architecture
• Embedded Systems
• +5 more

Prof. Wolfgang Ecker is a Professor at the Technical University of Munich (TUM), affiliated with the Chair of Design Automation within the TUM School of Computation, Information and Technology . His research focuses on Electronic Design Automation (EDA), RISC-V processor architectures, and hardware-software co-design. He leads projects advancing EDA tools for embedded systems, neural network acceleration, and formal verification methodologies. Ecker's work bridges machine learning techniques with traditional EDA challenges, addressing topics like energy-efficient AI inference and automated documentation generation. His contributions span compiler optimization, FPGA implementations, and fault analysis in digital systems. Recent research highlights include contributions to the TRISTAN project for RISC-V ecosystem development, model-driven architecture frameworks, and AI-driven timing analysis. He actively collaborates on open-source EDA tools and explores Rust-based embedded systems development. Ecker’s lab emphasizes practical applications in edge computing and automotive microcontroller safety, with a strong emphasis on interdisciplinary collaboration across TUM’s CIT School. His publications (15 most recent listed) reflect a focus on EDA tool innovation, processor design, and leveraging machine learning for hardware optimization. While no specific awards are mentioned, his involvement in ERC-funded projects and leadership in international collaborations underscores his academic impact.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +3 more

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

Markus KnauffView profile

Professor

• High-level cognition
• Reasoning
• Decision-making
• +7 more

Markus Knauff is a Professor at the Justus Liebig University Giessen in the Department of Experimental Psychology and Cognitive Science. His research focuses on high-level cognition, including reasoning, decision-making, and rationality, as well as spatial cognition and mental imagery in cognitive processes. Dipl.-Psych., University of Bochum, 1991 Dr. phil., University of Freiburg, 1996 Habilitation, University of Freiburg, 2002 His work integrates cognitive science, psychology, and artificial intelligence, emphasizing mental models and belief revision. He explores how counterexamples, probabilities, and spatial reasoning influence human cognition, with applications in navigation and decision-making. While his publications span deductive reasoning, belief dynamics, and spatial mental models, his recent studies highlight probabilistic reasoning in the brain and rationality frameworks. Markus leads the Giessen virtual environment laboratory, focusing on human wayfinding and landmark salience. He contributes to interdisciplinary projects like the SPP1516 "New Frameworks of Rationality" and has mentored researchers in cognitive systems, though specific student names are not listed in the provided texts.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Automotive Engineering
• Automated Driving Systems
• Vehicle Dynamics
• +5 more

Prof. Dr. Hermann Winner is a full Professor and Head of the Institute of Automotive Engineering (FZD) at Technische Universität Darmstadt since 2002. He previously held leadership roles in predevelopment and series development at Robert Bosch GmbH (1987–2001) and served on international standards committees like ISO/TC204/WG14. Education : Diplomphysicist (1981), Westfälische Wilhelms-Universität Münster Doctorate (1987), Westfälische Wilhelms-Universität Münster (Topic: Dynamics of domain walls in metal ferromagnetic materials) Research Interests : His work focuses on automated driving systems , automotive sensor modeling , and safety validation methodologies . Key contributions include modular safety frameworks (S2I2), behavior-semantic scenery descriptions for autonomous vehicle development, and advanced driver assistance systems (PRORETA 5). He has pioneered research on lidar sensor simulation, brake wear particle emissions, and motorcycle dynamics safety. Article Trends : Recent publications (2022–2024) emphasize automated urban driving safety , semantic scenario modeling , and sensor-based localization . Earlier works (2012–2021) established foundational concepts in fail-operational architectures , collision criticality metrics , and automotive system decomposition . Scientific Awards : IEEE-ITS Institutional Award (2012) for leadership in automated driving research Labs & Projects : He leads the Institute of Automotive Engineering (FZD) at TU Darmstadt and has been integral to projects like UNICARagil (disruptive vehicle architectures), PRORETA series (collision avoidance systems), and collaborations with institutions such as the Daimler und Benz Stiftung and the Bundesanstalt für Straßenwesen.

Ana LucicView profile

Assistant Professor

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

Claudia KlüppelbergView profile

Professor

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +3 more

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

Fabian GansView profile

Researcher

• Earth System Science
• Biogeochemistry
• Climate Extremes
• +7 more

Fabian Gans is a Researcher at the Max Planck Institute for Biogeochemistry, affiliated with the Department Biogeochemical Integration led by Prof. Dr. M. Reichstein. He leads the Scalable Spatiotemporal Data Structures and Analytics (SSDSA) research group and is actively involved in the Empirical Inference of the Earth System group under Dr. Miguel D. Mahecha, as well as the Energy and Earth System group under Dr. A. Kleidon. His work is central to advancing data-driven methodologies in Earth system science. His research focuses on Earth system dynamics, particularly through the development and application of Earth System Data Cubes (ESDCs), which integrate multivariate spatiotemporal datasets for robust analysis. He employs machine learning, remote sensing, and hybrid modeling to study carbon and water fluxes, climate extremes, and ecosystem responses. His work bridges observational data with modeling frameworks to improve understanding of biosphere-atmosphere interactions. The 15 most recent publications highlight a strong trend toward data integration, scalability, and the use of artificial intelligence in Earth sciences. Key themes include the FLUXCOM framework for upscaling carbon fluxes, the development of Earth System Data Cubes, analysis of compound climate extremes, and hybrid modeling approaches. His research consistently emphasizes open science, reproducibility, and the need for integrated data platforms to tackle global environmental challenges. Scientific Awards: No awards listed in the provided text. Advising and Grants: No formal advisees or students are listed. No specific grants or funding sources are mentioned, though his involvement in large collaborative projects like FLUXCOM and Earth System Data Cubes suggests participation in significant research initiatives. Labs and Teams: Fabian Gans leads the Scalable Spatiotemporal Data Structures and Analytics (SSDSA) group and is a key member of the Empirical Inference of the Earth System team. He is also involved in the DeepESDL platform, an open collaborative environment for Earth system research, indicating leadership in developing research infrastructure and fostering interdisciplinary collaboration.

• Data Stream Management
• Real-time Data Analytics
• Data Quality
• +7 more

Sandra Geisler is a Junior Professor for Data Stream Management and Analysis at the Department of Computer Science, RWTH Aachen University, a position she has held since September 2021. She is also the leader of the Digital Health Spaces group at the Fraunhofer Institute for Applied Information Technology (FIT) in St. Augustin, reflecting her dual expertise in academic research and applied digital health solutions. Bachelor/Master: Diploma in Computer Science, RWTH Aachen University (2008) PhD: Doctoral degree in Computer Science, RWTH Aachen University (2016) Her research focuses on data stream systems, real-time analytics, data quality, and their applications in digital health and industrial processes. She has made significant contributions to ontology-based data quality management, edge computing for stream processing, and FAIR data principles. Recent work explores the integration of large language models into data management workflows and the development of privacy-preserving platforms for industrial data exchange. Her recent publications demonstrate a strong trend in distributed and edge-based stream processing, interdisciplinary applications in healthcare and supply chains, and the use of AI for data discoverability and quality. Topics include in-network computing, simulation of edge queries, self-tonometry for glaucoma, and cross-company data sharing with privacy awareness. She has served as Associate Editor for the Data & Knowledge Engineering Journal (Elsevier), Public Relation Chair for QDB Workshop (VLDB 2016), and Workshop Chair for IMMoA and HIMoA workshops. She has also edited a special issue on Information Management in Mobile Applications in the Pervasive and Mobile Computing Journal. Geisler has supervised multiple theses on topics including LLM-based ontology integration, edge anomaly detection, and data ecosystem modeling. She has been actively involved in research grants and projects related to industrial data processing, digital health, and sustainable production. She teaches courses such as Data Stream Management and Analysis and Data Ecosystems Lab. She leads the Digital Health Spaces research group at Fraunhofer FIT, focusing on innovative solutions for health data management and patient-centric digital tools. Her work bridges computer science, healthcare, and industrial applications, promoting secure, efficient, and intelligent data ecosystems.

• Psychological Research Methods
• Quantitative Psychology
• Statistical Inference
• +4 more

Anna Nikolei is a research assistant at the University of Kassel in the department of Psychological Research Methods since February 2025. She holds an M.Sc. in Psychology and a B.Sc. in both Mathematics and Psychology from the University of Münster. Education: M.Sc. Psychology (2024), B.Sc. Mathematics (2024), B.Sc. Psychology (2021) Contact: anna.nikolei@uni-kassel.de | +49 561 804-1873 | Room 1303, Dutch Street 36-38, Kassel Her research focuses on statistical inference after model selection and temporal dynamics in experimental longitudinal data . She actively contributes to methodological advancements in multilevel modeling and post-selective inference. Anna's recent publications examine mixed-effects models for time-varying effects and simulation-based validation of inferential techniques in applied linear modeling. Her work bridges methodological rigor with practical applications in psychological experimentation.

Richard TraunmüllerView profile

Professor

• Political Science
• Empirical Democracy Research
• Social Cohesion
• +6 more

Prof. Dr. Richard Traunmüller is a Professor of Political Science and Empirical Democracy Research at the University of Mannheim. He serves as Scientific Director of the German Internet Panel (GIP) and is affiliated with the Research Institute Social Cohesion (RISC) at Goethe University Frankfurt. His research spans multidimensional social change, immigration policy, wartime sexual violence, and quantitative methodology. School of Social Sciences, University of Mannheim Goethe University Frankfurt University of Essex University of Bern University of Konstanz His research focuses on: Democratic challenges from social structural reconfiguration Religious diversity and social cohesion Free speech regulation in digital democracies Long-term effects of wartime sexual violence Recent work analyzes: Causal impacts of immigration policies Contextual speech norms Gendered consequences of conflict trauma Bayesian approaches to sensitive topics Scientific awards include: BEST PAPER AWARD (CES Immigration Research Network, 2017) BEST ARTICLE AWARD (APSA Migration & Citizenship, 2017) He teaches courses on: Bayesian Statistics Data Visualization Free Speech & Censorship Political Methodology Conflict Studies

Matthias HagenView profile

Professor

• Information Retrieval
• Natural Language Processing
• Web Data Mining
• +3 more

Matthias Hagen is Professor of Databases and Information Systems at Friedrich-Schiller-Universität Jena. His research focuses on information retrieval (query understanding, conversational search, comparative questions, known-item search, user simulation), natural language processing (clickbait, argumentation), and web data mining. He earned his Ph.D. from Friedrich-Schiller-Universität Jena with a thesis on algorithmic complexity, and previously led research groups at Bauhaus-Universität Weimar and Martin-Luther-Universität Halle-Wittenberg. His current work develops novel methods for retrieval-augmented generation evaluation, neural information retrieval efficiency, and user-centered search systems. Recent publications examine crowdsourcing for RAG evaluation, LLM-based relevance assessment, corpus subsampling techniques, and child-friendly web search evaluation frameworks. He contributes to open web search initiatives and develops tools like the TIREx Tracker for experimental reproducibility in IR research. Dr. Hagen serves on program committees for major conferences including SIGIR, ECIR, and ACL. His research group participates in competitive evaluations such as TREC, CLEF, and Touché. Recent projects explore axiomatic approaches to retrieval, argumentation systems, and the impact of search result quality on decision-making.

• Robotics
• Cognitive Systems
• Bio-inspired Robotics
• +4 more

Constantin Uhde is a PhD candidate at the Institute for Cognitive Systems (ICS) of the Technical University of Munich (TUM). His research focuses on integrating cognitive approaches with robotic planning to achieve robust, unsupervised task execution. He holds a Master's degree in Robotics, Cognition, and Intelligence from TUM and a Bachelor's in Mechatronics from Friedrich-Alexander University Erlangen. Research Interests include bio-inspired robotics, human-robot interaction, virtual reality applications in robotics, and path planning using multimodal sensor data. His work bridges theoretical cognitive science with practical robotic systems, emphasizing causal reasoning and transfer learning. Uhde teaches as a lecturer for the Advanced Seminar & Practical Projects in Cognitive Systems and has published in top-tier conferences such as IEEE CASE, IROS, and ICRA. His contributions range from visuomotor learning frameworks to benchmark datasets for symbolic grounding in robotics. He is part of the ICS lab team developing systems for humanoid robotics and neuroengineering applications, contributing to platforms like the Compact Omni-directional Mobile Platform and EEG Laboratory setups.

Fabian SchmidtView profile

Professor

• Cosmology
• Dark Matter
• Dark Energy
• +3 more

Fabian Schmidt is a Professor (Forschungsgruppenleiter) at the Max Planck Institute for Astrophysics (MPA) in Garching, Germany. He specializes in cosmology, focusing on dark matter, dark energy, and the evolution of cosmic structures. His research explores the universe's composition, history, and the mechanisms behind primordial density fluctuations. Education: Diplom (equivalent to MSc) in cosmic gamma rays from Humboldt University Berlin, PhD (2005-2009) at the University of Chicago under advisors Scott Dodelson and Wayne Hu. Held fellowships at Caltech (Moore Fellow) and Princeton (NASA CXC Einstein Fellow) before joining MPA in 2013. Research interests include cosmological observables in general relativity, baryon acoustic oscillations (BAO) reconstruction, and field-level inference techniques. His group is funded by the Excellence Cluster Origins and MPA resources, with ongoing work on large-scale structure, gravitational lensing, and dark matter physics. Notable contributions span theoretical cosmology, computational methods for galaxy clustering analysis, and Bayesian forward modeling. His team advises PhD students and postdocs in areas like EFT-based BAO reconstruction, simulation-based inference, and dark matter dynamics. Outreach: Active in public talks (Lange Nacht der Wissenschaften) and collaborations with science journalists. Co-author of Modern Cosmology textbook (2nd ed. 2020, 3rd ed. 2025), addressing topics like inflation and quantum vacuum cosmology.