جست‌وجوی استاد و پژوهشگر

• Climate Science
• Ecohydrology
• Machine Learning
• +۳ مورد دیگر

Dr. Shijie Jiang is a Project Leader at the Max Planck Institute for Biogeochemistry , leading the Machine Learning for Hydrological and Earth Systems group within the Department of Biogeochemical Integration. His research focuses on integrating hybrid AI methods with domain knowledge to study climate-water-ecosystem interactions under anthropogenic and climatic pressures. Key areas include ecohydrological processes, explainable machine learning applications in earth systems, and climate extremes predictability. Education: Ph.D. in Environmental Science (2017–2021), National University of Singapore M.Sc. in Environmental Science (2013–2016), Beijing Normal University B.Sc. in Environmental Engineering (2009–2013), Wuhan University Research Interests: Dr. Jiang explores interactions between climate, water, and ecosystems using AI-driven frameworks. Specific foci include: Physics-AI hybrid models for ecohydrology and climate processes Machine learning interpretability in earth system science Predictability of climate extremes and their impacts on water cycles Global hydrological drought mechanisms and compounding climatic drivers Labs/Teams: His work is anchored in the Biogeochemical Integration Department, collaborating with the ELLIS Unit Jena. Research outputs emphasize bridging data-driven discovery with traditional geophysical modeling. Grants/Advising: No specific grants or advisees listed in current records. Activities focus on leading interdisciplinary projects and postdoctoral mentorship within his group.

Joachim Denzlerمشاهده پروفایل

استاد

• Computer Vision
• Dynamical Systems
• Explainable AI
• +۳ مورد دیگر

Joachim Denzler is Professor of Computer Vision at the University of Jena, Director of the Michael Stifel Center Jena for Data-driven Science, and leads the Institute of Data Science at the German Aerospace Center (DLR). His research develops machine learning methods for analyzing complex dynamical systems, with applications in earth sciences, medicine, and psychology. Key focuses include fine-grained object classification, active learning, and explainable AI for time-series data. He chairs the Thuringia Center for Learning Systems and Robotics, translating AI research into industrial applications. Current projects address climate change and biodiversity monitoring through computer vision approaches. With over 500 publications and extensive peer review experience for top conferences/journals, his group of 15 PhD students receives funding from DFG, BMBF, EU, and industry partners.

Moritz Böhleمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Moritz Böhle is a researcher at the Max Planck Institute for Informatics , affiliated with the Computer Vision and Machine Learning department. He completed his PhD in 2024 at Saarland University, titled Towards Designing Inherently Interpretable Deep Neural Networks for Image Classification . His work focuses on interpretable machine learning, particularly in developing architectures like B-cos Networks to align models for faithful explanations. He has contributed to foundational research in model interpretability, including transforming pre-trained models (B-cosification), knowledge distillation with explanations, and automated concept discovery. His research spans computer vision tasks, vision transformers, and convolutional neural networks, with a strong emphasis on aligning model components for transparency. He has co-authored papers in top venues like ICLR, NeurIPS, CVPR, and ECCV, addressing challenges in post-hoc explanations, alignment mechanisms, and systematic evaluation of attribution methods. His work bridges theoretical advancements and practical applications in explainable AI. Key contributions include B-cos alignment for CNNs and transformers, explanation-enhanced knowledge distillation, and frameworks for task-agnostic concept discovery. These efforts aim to make AI systems more transparent and trustworthy while maintaining competitive performance.

Arjun Chandrasekaranمشاهده پروفایل

پژوهشگر

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Arjun Chandrasekaran holds dual roles as a Guest Scientist/Postdoctoral Researcher at the Max Planck Institute for Intelligent Systems (Tübingen, Germany) and Head of AI at CONXAI Technologies (Munich, Germany). His research bridges computer vision, natural language processing, and cognitive science, focusing on multimodal machine learning for understanding human actions, interactions, and behavior in 3D environments. Key areas include human-AI team dynamics, embodied AI, and humor/narrative modeling. Education: PhD in Computer Science (Georgia Tech, advised by Devi Parikh) Postdoc under Michael Black at MPI-IS Research Highlights: Developed the BABEL dataset for 3D human behavior analysis Explored Fermi problems as AI reasoning benchmarks Pioneered studies on humor perception in visual contexts Advocated for explainable AI through human-centric evaluation Publications emphasize interdisciplinary approaches, combining vision-language tasks with cognitive insights. Current work at CONXAI applies AI to transform the Architecture, Engineering, and Construction (AEC) industry through strategic AI solutions and team leadership.

Yixin Zouمشاهده پروفایل

استادیار

• Human-Centered Security
• Privacy Studies
• Cross-Cultural Security Research
• +۳ مورد دیگر

Dr. Yixin Zou is a tenure-track Assistant Professor and Group Leader of the Human-Centered Security and Privacy group at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. They are also a Principal Investigator at Ruhr University Bochum's CASA Cluster of Excellence. Their research focuses on making technologies safer and equitable for underserved communities, including accessibility challenges for deaf individuals and older adults, cross-cultural security practices, and responsible AI development. They lead interdisciplinary collaborations with legal scholars and social scientists to translate research into real-world impacts. Zou holds a Ph.D. in Information from the University of Michigan (advised by Florian Schaub), recognized with an ACM SIGCHI Outstanding Dissertation Award. Their work is funded by MPI-SP, DFG, Google, and Meta. Notable grants include a Google Academic Research Award on safeguarding emerging adults from short-form video scams and a Meta grant studying digital privacy expectations. Research interests include differential access to security tools, cross-cultural digital privacy norms, and designing interventions to address algorithmic discrimination. They prioritize global perspectives, contrasting WEIRD (Western, Educated, Industrialized, Rich, Democratic) and non-WEIRD populations. Recent projects examine online fraud prevention among Chinese seniors and trauma-informed computing approaches. Zou has received multiple awards, including Best Paper Honorable Mentions (ACM CHI 2025/2020/2019) and a Distinguished Paper Award (SOUPS 2018). Their group's work bridges academia and practice through partnerships with companies, NGOs, and policymakers. Current initiatives focus on explainable hardware (XHW) and mitigating image-based sexual abuse on online platforms. Students and collaborators include researchers from computer science, law, and social sciences. Former members have moved to industry and academia roles. Zou advises via the MPI-SP doctoral programs and offers internships through the CS@Max Planck program.

Eleni Straitouriمشاهده پروفایل

استاد

• Machine Learning
• Causal Inference
• Large Language Models
• +۴ مورد دیگر

Eleni Straitouri is a Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, where she leads research at the intersection of artificial intelligence safety and human-centered decision systems. Her work bridges theoretical machine learning with practical applications in high-stakes environments requiring rigorous uncertainty quantification and causal reasoning. Her primary research focuses on causal inference in large language models , counterfactual prediction sets , and human-AI complementarity . She develops methods to control algorithmic harm through counterfactual reasoning, enhances model evaluation via conformal prediction, and designs decision support tools that leverage human expertise. Current projects address critical challenges in LLM safety, including bias detection through coupled token generation and harm mitigation in medical/operational decision systems. Analysis of her 13 publications (2021-2025) reveals an evolving trajectory from reinforcement learning foundations toward cutting-edge LLM safety frameworks. Her work consistently integrates causal methods with human factors, producing practical tools for reliable AI deployment across healthcare and operational domains. Recent NeurIPS/ICML publications demonstrate increasing influence in trustworthy AI research. Best Paper Award, Workshop on AI and HCI (ICML), 2023 Spotlight Paper (top 3.5%), ICML 2024 Dr. Straitouri maintains active collaborations with Manuel Gomez-Rodriguez's research group, evidenced by co-authorship on 100% of her recent publications. While specific grant funding isn't detailed in available materials, her work aligns with MPI-SWS's focus on software systems for societal benefit. No information about academic advising or laboratory infrastructure was found in the provided text.

• Formal Methods
• Software Architecture
• Self-Adaptive Systems
• +۵ مورد دیگر

Patrizia Scandurra is a Professor affiliated with the University of Bergamo, Italy. Her research focuses on formal methods, software architecture, self-adaptive systems, and model-driven engineering. She has contributed extensively to the development of rigorous system design frameworks like ASMETA and has led work on resilience engineering in cyber-physical systems. Scandurra has co-authored numerous papers in top venues such as ECSA, ABZ, and IEEE Transactions, and has served as editor for conference proceedings including ECSA 2024 and ABZ 2024. Her work emphasizes practical applications of formal methods in safety-critical systems, IoT, and medical devices, with a recent focus on explainable AI and trustworthiness in autonomous systems. Areas: Formal Methods, Self-Adaptation, Cyber-Physical Systems Tools: ASMETA, HYPpOTesT Toolkit Key Projects: MVM-Adapt, RAMSES, IPSOS emergency response system Her research spans theoretical advancements and practical implementations, often bridging gaps between model-driven approaches and real-world system deployment. Current trends include addressing uncertainty in self-adaptive systems, trust analysis for medical devices, andexplainability in robotics.

Minsu Kimمشاهده پروفایل

پژوهشگر

• Environmental Systems Science
• Biological Soil Crusts
• Soil Microbiology
• +۴ مورد دیگر

Minsu Kim is a University Assistant with Doctorate at the Institute of Biology, University of Graz (Austria). His research focuses on environmental systems science, particularly biological soil crusts (biocrusts), microbial ecology, and atmospheric interactions in dryland ecosystems. He has held roles including SNSF PostDoc.Mobility Fellow and Postdoctoral Researcher at Empa (Switzerland), with a PhD from ETH Zürich (2015). Affiliations: Institute of Biology, University of Graz; previously at Empa, ETH Zürich, and Seoul National University (SNU). Key Projects: Led studies on biocrust CO₂ uptake mechanisms, soil nitrogen cycling, and air pollution modeling using satellite data. Key Skills: Agent-based modeling, computational biogeochemistry, remote sensing downscaling, and microbial community dynamics analysis. Research interests include biocrust dynamics, microscale soil-atmosphere interactions, nitrogen cycling, and high-resolution air pollution mapping. He developed models like the Desert Biocrust Model (DBM) to explain nocturnal CO₂ uptake in drylands and contributed to HONO emission mechanisms in soil systems. His work bridges environmental engineering, microbiology, and atmospheric science, with applications to climate modeling and pollution mitigation. Notable outputs include peer-reviewed articles in journals like Science of the Total Environment and Biogeosciences .

• Wireless Communications
• Millimeter-Wave Networks
• 5G/6G Networking
• +۵ مورد دیگر

Jörg Widmer is a leading researcher at IMDEA Networks Institute in Madrid, Spain, specializing in wireless communications, mobile networking, and next-generation network technologies. With over 297 publications spanning from 2000 to the present, his work has significantly contributed to the fields of millimeter-wave communications, 5G/6G networking, and integrated sensing and communication systems. Widmer's research interests span wireless communications, millimeter-wave networks, 5G/6G networking, mobile systems, integrated sensing and communication (ISAC), and AI/ML applications for networking. His recent work (2023-2025) demonstrates a strong focus on 5G performance analysis, explainable AI for network forecasting, millimeter-wave technologies, and the integration of sensing capabilities with communication systems. He has pioneered research in directional communications, channel modeling, and network optimization for next-generation wireless systems. His extensive publication record shows a clear evolution from fundamental wireless networking research toward more complex integrated systems that combine communication with sensing capabilities. Recent publications reveal a strong emphasis on making AI applications in networking more transparent and explainable, while also addressing practical deployment challenges in operational 5G networks. The breadth of his work spans theoretical modeling, experimental validation, and practical system implementation. Widmer has collaborated extensively with researchers across Europe and internationally, particularly with Claudio Fiandrino (34 co-authored papers), Adrian Loch (30 papers), Jesus Omar Lacruz (21 papers), and Joan Palacios Beltran (20 papers). His work appears in top-tier venues including IEEE INFOCOM, MobiCom, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Communications.

Daniel Neiderمشاهده پروفایل

استاد

• Secure Machine Learning
• Artificial Intelligence Security
• Explainability of Intelligent Systems
• +۳ مورد دیگر

**Daniel Neider** is a Professor of Security and Explainability of Learning Systems at Carl von Ossietzky University of Oldenburg. His research focuses on secure machine learning, the safety and reliability of AI systems, and their explainability. He holds a PhD in Computer Science from RWTH Aachen University (2014) and a Habilitation in theoretical computer science from TU Kaiserslautern (2022). Prior to his professorship, he was a Research Group Leader at the Max Planck Institute for Software Systems (2017–2022) and a Postdoc at RWTH Aachen and the University of Illinois at Urbana-Champaign (2014–2017). His work bridges formal methods and machine learning, emphasizing verification of neural networks, temporal logic-based explainability, and robust reinforcement learning. Notable contributions include neuro-symbolic verification techniques, algorithms for learning temporal properties, and frameworks for secure AI deployment. Research interests span formal verification, interpretable AI, and the theoretical foundations of machine learning. He leads projects in the Research Center ‘Trustworthy Data Science and Security’ within the Ruhr University Alliance. Recent publications address challenges in anomaly detection, temporal query processing, and ethical AI applications.

• Business Intelligence
• Analytics
• Physiological Computing
• +۳ مورد دیگر

Mario Nadj is an Assistant Professor of Business & Information Systems Engineering at the Department of Business and Economics, Technical University of Dortmund since September 2022. His research focuses on Business Intelligence & Analytics, Physiological Computing, and Interactive & Explainable Machine Learning. Education: Bachelor of Science in Business Information Systems (2011), Otto-Friedrich-University Bamberg Study abroad at Institute of Technology Tallaght Dublin (2010-2011) Master of Science in Finance & Information Management (2014), Goethe University Frankfurt Research Interests: Dr. Nadj explores how physiological data and interactive systems can enhance workplace performance and decision-making. His work bridges human factors with computational methods, emphasizing explainable AI and human-in-the-loop systems. Recent studies investigate flow states in knowledge work, IT-mediated interruptions, and adaptive labeling systems. Awards: No awards explicitly mentioned. Advising & Grants: No formal advisees listed. Previously led projects such as “Smart Process Apps on Big Data” (SAP SE, 2014-2017) and headed research areas like Intelligent Enterprise Systems at Karlsruhe Institute of Technology. Labs/Teams: Involved in neuroinformation systems research, evidenced by tools like the ‘brownieR’ package for EEG data analysis.

• Natural Language Processing
• Healthcare Informatics
• Machine Learning
• +۳ مورد دیگر

Dr. Max Maurer is an Adjunct Professor affiliated with the Data Science and Text-based Information Systems (DATEXIS) group. His research focuses on advancing Natural Language Processing (NLP) with applications in healthcare, including clinical outcome prediction, assertion detection in medical records, and contextualized document retrieval for clinical decision support. He also explores foundational NLP topics like entity linking, topic segmentation, and transformer network explainability. He has contributed to projects such as INDREX (in-database text mining), TASTY (interactive entity linking), and IDEL (neural entity linkage). His work extends to healthcare technology, including the Smart-MD system for paragraph retrieval and communication tools for nephrology. Dr. Maurer has been involved in teaching, with records of master’s thesis supervision spanning multiple academic terms since 2013. His applied research includes media bias analysis, hate speech detection (NOHATE), and OLAP information systems (GoOLAP.info).

Patrick Mäderمشاهده پروفایل

استاد

• Secure Software Development
• Reliable Software Systems
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Patrick Mäder is a University Professor and head of the Group of Data-intensive Systems and Visualization at TU Ilmenau's Department of Computer Science and Automation. He previously served as an Assistant Professor in Software Engineering for Safety-Critical Systems. His career includes doctoral research on software traceability (earning the Thuringian STIFT Prize), a Lise Meitner Fellowship at Johannes Kepler University Linz, and research stays at DePaul University Chicago. Bachelor/Master studies at TU Ilmenau PhD in 2005 on software traceability Postdoc at Johannes Kepler University Linz (2010-2012) Research focuses on secure/reliable software systems, scalable machine learning, explainable AI, computational biology/ecology, and interdisciplinary projects like the award-winning Flora Incognita plant identification app. He has coordinated numerous third-party funded projects in software engineering and machine learning, expanding his team to 18 researchers by 2020. Awards include the 2020 Thuringian Research Prize for applied research and the STIFT Prize for his dissertation on software traceability.

• Neuroimaging
• Positron Emission Tomography (PET)
• Magnetic Resonance Imaging (MRI)
• +۵ مورد دیگر

Dr. Simone Beer is a Physicist at the Institute of Neuroscience and Medicine (INM-2) at Forschungszentrum Jülich , focusing on advanced neuroimaging and AI-driven methodologies. Her work integrates PET, MRI, and machine learning to study sleep-circadian rhythms, neurodegeneration, and brain aging. Key projects include the Awake PET initiative for non-anesthetized animal studies and the SleepLess Project exploring sleep deprivation effects. She teaches Experimental and Translational Neuroimaging at Heinrich-Heine-University (HHU) Düsseldorf, emphasizing radiation protection and PET techniques. Research interests span: Development of hybrid PET/MRI phantoms Quantitative PET methods for awake animal models Explainable AI analysis of multi-omics data linking genetics to neurodegeneration Circadian rhythm impacts on adenosine receptor availability Her recent work highlights the relationship between sleep deprivation and accelerated brain aging, as well as sex differences in adenosine receptor availability. Beer also pioneers low-dose PET/MRI image enhancement via deep learning and investigates hypoxia effects on cerebral perfusion using [F-18]CPFPX PET. Laboratory affiliations include the Plant Imaging with PET (PhenoPET) initiative and development of 3D-printed phantoms for hybrid imaging. Her interdisciplinary approach bridges physics, neuroscience, and clinical medicine.

Hannes Freyمشاهده پروفایل

استاد

• Wireless Networked Systems
• Mobile Robotics
• Sensor Networks
• +۲ مورد دیگر

Hannes Frey is Professor and Head of the Computer Networks Research Group at the University of Koblenz and Landau. His research focuses on the controllability of complex dynamically networked systems including sensor networks, mobile autonomous robot teams, and connected vehicles. The group investigates fundamental theoretical questions and system-level practical issues, with emphasis on transferring theoretical results into practice through prototype implementations. Research interests include: Connectivity and percolation in wireless networked systems Local topology control under graph structure assumptions Cooperative distributed control for wirelessly networked mobile robots Reliable ultra-low latency communication for autonomous platforms Software Defined Networking for IoT systems The group maintains the UniKoRN laboratory for practical research in wireless communication and mobile distributed robotics, supporting research internships and student projects.