جست‌وجوی استاد و پژوهشگر

Christopher M. Jonesمشاهده پروفایل

پژوهشگر

• Intensive-longitudinal methods
• Behavior Change (interventions)
• Social and health inequalities
• +۴ مورد دیگر

Dr. rer. nat. Christopher M. Jones is a Senior Scientist in the Department of Cardiovascular and Metabolic Disease Prevention at the Medical Faculty Mannheim of Heidelberg University . Holding a PhD in Clinical Psychology from the University of Bremen, his work bridges behavioral science, digital health, and social determinants of health behavior. Current focus areas include intensive longitudinal methodology for real-time behavioral analysis. Investigates social and contextual factors influencing intention-behavior translation. Examines socioeconomic drivers of health disparities and misinformation dissemination on social media. Recently expanded into climate and health research, particularly air pollution protective behaviors. His methodological expertise spans experimental designs and smartphone-based ecological momentary assessments. Publications include studies on: COVID-19 nonpharmaceutical adherence dynamics. Smoking behavior regulation over time. Misinformation sharing in digital health contexts. Behavioral determinants of antibiotic prescribing patterns. Contact: christopher.jones@medma.uni-heidelberg.de | Phone: +49 621 383-71792

Benjamin de Haasمشاهده پروفایل

پژوهشگر

• Visual Perception
• Face Processing
• Individual Differences
• +۳ مورد دیگر

Dr. Benjamin de Haas is a vision scientist and faculty member at Justus Liebig University Giessen , Germany, within the Department of Psychology and Sports Science . He currently leads the ERC-funded Indivisual project and co-leads project C9 Factors influencing categorical face processing within the Collaborative Research Centre CRC/TRR 135. He is also a principal investigator in the NeurOscientific Workflow Assistance (NOWA) infrastructure project, dedicated to open, reproducible neuroscience. Research Focus Dr. de Haas pursues two intertwined questions: How do early and late stages of visual processing interact—from the initial registration of slanted edges to the recognition of faces? How and why do our perceptions differ from one person to the next? To answer these questions his group combines psychophysics, high-resolution eye-tracking, functional and quantitative MRI, and computational modelling, with a strong emphasis on face perception, individual differences, and naturalistic viewing conditions. Publications Overview Across more than 20 publications since 2016, Dr. de Haas has advanced understanding of individual differences in face processing, gaze control, and visual salience. His work repeatedly appears in Journal of Vision , Nature Communications , PNAS , and NeuroImage , highlighting a sustained focus on eye-movement behaviour, cortical representations of faces and scenes, and methodological best practices in neuroimaging. Current Supervision & Team Dr. de Haas currently supervises two PhD students: Elaheh Akbarifathkouhi Hilal Nizamoglu Together with Dr. Katharina Dobs (co-project leader) and affiliated post-docs and research technicians, the group forms the Indivisual laboratory at Giessen. Contact & Resources Email: Benjamin.de-Haas@psychol.uni-giessen.de Department of Psychology and Sports Science Otto-Behaghel-Str. 10F, 35394 Gießen, Germany

Jie M. Zhangمشاهده پروفایل

استادیار

• Software Testing
• AI for Software Engineering
• Software Engineering for AI
• +۵ مورد دیگر

Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.

Juergen Groeneveldمشاهده پروفایل

پژوهشگر

• Ecological Modelling
• Agent-Based Modelling
• Biodiversity Conservation
• +۵ مورد دیگر

Jürgen Groeneveld is a researcher at the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. His work focuses on agent-based modelling of ecological and socio-environmental systems, with a particular emphasis on pollinator conservation, climate change impacts, and biodiversity digital twins. He contributes to the BioDT Project and collaborates on initiatives like the BEEHAVE and BEE-STEWARD models for honeybee and bumblebee dynamics. His research integrates computational methods with ecological theory to address challenges in sustainable land use and environmental policy. Key projects include modeling land use transformations, analyzing the effects of pesticides on pollinators, and exploring risk-sharing mechanisms for smallholder farmers. Groeneveld has published extensively on topics such as metapopulation dynamics, forest fragmentation, and the application of standardized protocols (e.g., ODD) for simulation models. His work bridges theoretical ecology with practical applications, aiming to inform conservation strategies and climate adaptation policies. He participates in interdisciplinary collaborations across the UFZ’s research units, including the 'Smart Models / Monitoring' group. His contributions span publications in journals like Ecological Modelling, Ecosphere, and Nature, reflecting his expertise in both ecological and computational methodologies.

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

Jann Weinandمشاهده پروفایل

استاد پژوهشی

• Energy System Optimization
• Renewable Energy Resource Assessment
• Complexity Management
• +۴ مورد دیگر

Dr. Jann Michael Weinand is the head of the Integrated Scenarios department at the Institute of Climate and Energy Systems (ICE-2) within Forschungszentrum Jülich GmbH. He leads a team of 30 scientists, PhD students, and master students focusing on energy system analysis, complexity management, and AI integration. His work addresses regional and international energy systems, emphasizing renewable energy resource assessment and techno-economic feasibility. Dr. Weinand holds a Dr.-Ing. from the Karlsruhe Institute of Technology (2020) and a Mechanical Engineering and Business Administration degree from RWTH Aachen University (2016). His research spans energy autonomy, renewable resource optimization, and the socio-technical challenges of energy transitions. Key research areas include energy system modeling, geothermal and wind energy potential, and data-driven methodologies. He coordinates interdisciplinary projects with academic and industrial partners, contributing to high-impact journals like Nature Energy and Joule. His team develops open-source tools (e.g., ETHOS workflows) for reproducible energy assessments and advocates for spatially disaggregated energy planning. Publications highlight trade-offs in energy system design, AI risks, and land-use conflicts for renewables. He emphasizes integrating social, technical, and environmental factors into energy policy frameworks.

Katharina Eggenspergerمشاهده پروفایل

پژوهشگر

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +۱ مورد دیگر

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

• Computational Neuroscience
• Visual System
• Sensory Coding
• +۷ مورد دیگر

Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.

• Process Mining
• Business Process Management
• Stochastic Processes
• +۴ مورد دیگر

Dr. Sander Leemans is a Professor at RWTH Aachen University leading the Business Process Management Foundations and Engineering research group. His work focuses on advancing process mining theory and practice with emphasis on stochastic modeling and conformance verification. Leemans' research centers on process mining, business process management, and stochastic process modeling. He investigates conformance checking techniques for probabilistic models, process discovery algorithms, and the integration of exogenous data into process analysis. His work bridges theoretical foundations with practical applications in healthcare, robotic process automation, and inter-organizational systems. Recent publications reveal a concentrated research trajectory in stochastic conformance checking, where Leemans develops methods for matching observed traces to stochastic process models using alignment techniques, entropy metrics, and partial-order reasoning. He also pioneers object-centric process mining frameworks and explores silent transitions in labeled Petri nets, significantly enhancing the precision and applicability of process mining in real-world scenarios. The Business Process Management Foundations and Engineering group under Leemans' leadership drives innovation in process mining through rigorous theoretical development and open-source tooling, maintaining RWTH Aachen's position at the forefront of business process intelligence research.

Jürgen Citoمشاهده پروفایل

دانشیار

• Software Engineering
• Explainable AI
• Performance Engineering
• +۳ مورد دیگر

Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.

Arpan Gujaratiمشاهده پروفایل

عضو هیئت علمی

• Real-Time Systems
• Distributed Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.

• Computational Chemistry
• Computer-Aided Drug Design
• Cheminformatics
• +۱۲ مورد دیگر

Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.

Urs Gasserمشاهده پروفایل

استاد

• AI Governance
• Digital Policy
• Quantum Technology Ethics
• +۳ مورد دیگر

Prof. Dr. Urs Gasser is Professor of Public Policy, Governance and Innovative Technology at the Technische Universität München (TUM) , where he serves as Dean of the TUM School of Social Sciences and Technology and Rector of the Hochschule für Politik (HfP). His research examines the interplay between emerging technologies and societal structures, focusing on regulatory frameworks for AI, quantum technologies, and extended reality. Academic Affiliations: TUM, Harvard Law School (Visiting Professor), Berkman Klein Center for Internet & Society (Executive Director, former) Policy Roles: Chairs Thailand's International Policy Advisory Panel on AI Governance, advises OECD/UNICEF/UNESCO, contributes to World Economic Forum and US National Academies of Sciences Working Group on Generative AI Gasser leads initiatives including the TUM Think Tank , Generative AI Task Force , Quantum Social Lab , and Friedrich Schiedel Fellowship . His recent publications address AI governance blueprints (2025), quantum ethics (2024), and digital policy challenges across global jurisdictions. Emerging trends in his research emphasize: Responsible innovation for quantum technologies Ethical guardrails in AI-driven decision-making Comparative regulatory frameworks (EU vs US) Generative AI's impact on education and scientific integrity Global governance models for digital privacy Interoperability in digital ecosystems

Florian Hausenمشاهده پروفایل

استاد

• Electrochemistry
• Nanotechnology
• Materials Science
• +۵ مورد دیگر

Florian Hausen is a Professor for Applied Interface Electrochemistry at RWTH Aachen University and leads the scanning probe microscopy focused group at the Fundamentals of Electrochemistry (IET-1) department within Forschungszentrum Jülich . His work bridges electrochemistry, nanotechnology, and materials science, with a focus on energy storage systems. Education : Studied Chemistry at the University of Bonn PhD in Physics from Saarland University Hausen's research interests center on in-situ/operando scanning probe techniques to study interfaces in energy materials, including solid-state batteries , proton exchange membrane water electrolyzers , and ionic liquids . His group explores: Correlative microscopy (AFM, SEM, EPR) for multi-scale analysis Mechanical properties under electrochemical load Tribology of materials in battery systems Interphases in lithium, zinc, and silicon-based energy technologies The article trends reflect his expertise in: Nanoscale electrochemical characterization of battery and electrolyzer components Material degradation under dynamic cycling and electrochemical stress Correlation of electrical, mechanical, and chemical properties in energy systems Technique development for operando studies and data reproducibility His work contributes to understanding and improving: Lithium plating and SEI formation Nanomechanical stability of electrolyzer anodes and cathodes Carbon nanofiber properties for battery electrodes Doping effects in ceramic solid electrolytes