جست‌وجوی استاد و پژوهشگر

Leif Bonordenمشاهده پروفایل

پژوهشگر

• Interfaces in Modular Software Systems
• API Evolution and Deprecation
• API Specification and Documentation
• +۳ مورد دیگر

Leif Bonorden is a Research Associate and Doctoral Student at the Department of Informatics, Faculty of Mathematics, Computer Science and Natural Sciences, University of Hamburg. He works within the Software Engineering and Construction Methods research group led by Professors Matthias Riebisch and André van Hoorn. His academic background includes a B.Sc. and M.Sc. in Mathematics from Technische Universität Berlin (2010-2018), with thesis work in Probability Theory and Functional Analysis. Since 2019, he has been pursuing doctoral research at the University of Hamburg while also studying for an M.A. in Higher Education. 2010-2018: Student, B.Sc. and M.Sc. Mathematics, Technische Universität Berlin 2012-2015: Student Teaching Assistant, Software Engineering group, TU Berlin 2015-2017: Assistant Researcher, FZI Forschungszentrum Informatik 2019-present: Research Associate & Doctoral Student, University of Hamburg 2020-present: M.A. Higher Education student, University of Hamburg Bonorden's research centers on API evolution and deprecation processes, with particular focus on how deprecated web APIs can be detected through tracing techniques. His work bridges theoretical software engineering with practical applications in API design and documentation. He has developed expertise in systematic mapping studies of API deprecation practices and their impact on software maintenance. His publication record demonstrates consistent focus on API-related research, with multiple systematic studies and empirical analyses. He has also contributed significantly to software reengineering knowledge through the SREBOK initiative and explored innovative educational approaches for teaching software engineering concepts through research-based learning. Active member of Software Engineering and Construction Methods research group Contributor to German Informatics Society's software reengineering special interest group Member of ACM, GI (Gesellschaft für Informatik), and DMV professional organizations Bonorden serves on multiple university committees including the Committee for Teaching and Studies, Building Committee, Faculty Council, and various examination boards for computer science programs. His teaching portfolio includes Software Design, Introduction to Software Engineering, and Bachelor Seminars in Software Engineering at both University of Hamburg and TU Berlin.

Florian Kernمشاهده پروفایل

پژوهشگر

• Extended Reality (XR)
• Virtual Reality (VR)
• Augmented Reality (AR)
• +۵ مورد دیگر

Florian Kern is a PhD candidate in Human-Computer Interaction at the University of Würzburg, supervised by Prof. Marc Erich Latoschik. He holds an M.Sc. in Computer Science focusing on HCI (2018). His research centers on Extended Reality (XR), particularly text input methods, surface alignment for handwriting/sketching, and VR applications in rehabilitation. Key projects include the Off-The-Shelf Stylus framework for XR interaction and the Reality Stack I/O modular framework for cross-platform XR development. Research Interests: XR Interaction: Handwriting/sketching in VR/AR, physical-virtual surface alignment. Rehabilitation Technology: VR-based gait training (e.g., Homecoming application for MS/stroke patients). Framework Design: Cross-platform tools like Reality Stack I/O, modular animation pipelines. User Experience: Evaluating input techniques, sketching behavior, and usability in immersive systems. Publications emphasize technical innovations (e.g., stylus calibration, surface refinement) and interdisciplinary applications (e.g., healthcare VR). Collaborations span HCI, psychology, and engineering departments.

Sarath Menonمشاهده پروفایل

پژوهشگر

• Materials Science
• Machine Learning
• Computational Physics
• +۴ مورد دیگر

Sarath Menon is a computational materials scientist at Ruhr-University Bochum and Max-Planck-Institut für Eisenforschung GmbH. His work focuses on atomistic simulations, machine learning interatomic potentials, and thermodynamic property calculations. He contributes to open-source software like pyiron and pace. Doctor of Engineering, Mechanical Engineering (2021) Master of Science, Materials Science and Simulation (2018) Bachelor of Technology, Mechanical Engineering (2012) His research centers on developing machine learning potentials for thermodynamic modeling, with applications in phase diagrams and nucleation studies. He employs methods like transition path sampling and hyperdynamics. Menon teaches Python programming, electronic structure methods, and atomistic simulation techniques. He has organized workshops on reproducible workflows and quantum mechanics in solid-state physics. Key software contributions include: pyscal : Structural analysis tool for atomic environments pace : High-performance Atomic Cluster Expansion implementation calphy : Free energy calculation library atomRDF : Ontology-based structure manipulation

Ralf Hielscherمشاهده پروفایل

استاد

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +۷ مورد دیگر

Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.

• Production Management
• Supply Chain Management
• Logistics
• +۷ مورد دیگر

Stephan Zelewski is a full Professor at the Faculty of Business and Economics of the University of Duisburg-Essen since 1998 and head of the Institute for Production and Industrial Information Management. He studied business administration and economics at the Universities of Münster and Cologne (1977-1981), earning two diplomas with distinction. Key Research Areas : Production Management, Logistics, Industrial Information Systems, Semantic Knowledge Management, AI/Operations Research applications in business, and Game Theory-based fair distribution models. Academic Leadership : Dean of Studies (1993-1996), founder of the Ruhr Campus Academy (2001-2002), and chair of multiple research projects including KI-LiveS (AI brainware development) and OrGoLo (Semantic Knowledge Management in logistics). Recent Articles focus on AI-based knowledge reuse systems, e-mobility sustainability analysis, and semantic modeling tools. His work integrates Artificial Intelligence with Operations Research and Game Theory across logistics and production domains. Scientific Recognition : Multiple scholarships (Konrad Adenauer, Hoechst, Fritz Honsel Foundations) Cologne University Prize (1992) Leadership in academic associations (VHB, Wissenschaftliche Kommission Wissenschaftstheorie)

• Collective Intelligence
• Collaboration & Group Processes
• Network Analysis
• +۲ مورد دیگر

Nikolas Zöller is a research scientist at the Max Planck Institute for Human Development 's Adaptive Rationality department. His work focuses on collective intelligence, human-AI collaboration, and computational modeling of social systems. He combines methods from physics, computer science, and psychology to study decision-making processes in both natural and artificial collectives. Ph.D. candidate at Constructor University Bremen (2020-2024) Research Associate at Potsdam University of Applied Sciences (2015-2022) Master of Science in Physics from Freie Universität Berlin (2014) Bachelor of Science in Physics from Freie Universität Berlin (2010) His research spans: Collective intelligence frameworks Human-AI collaborative diagnostics Agent-based modeling of social networks Technological co-diffusion patterns Thermodynamic optimization in physical systems Recent publications highlight his work on: Diagnostic accuracy in human-AI collectives GitHub collaboration topology Affect control theory in group dynamics Co-diffusion of complementary technologies Scientific recognition includes: European Press Prize (2022) as part of a team Springer Best Masters Award (2014)

Shweta Suranمشاهده پروفایل

پژوهشگر ارشد

• Collective Intelligence
• Collective Behaviour
• Citizen Science
• +۱۰ مورد دیگر

Shweta Suran is a Postdoctoral Fellow at the Max Planck Institute for Human Development (Berlin, Germany) with affiliations as an External Researcher at Tallinn University of Technology (School of Information Technologies, Department of Software Science) and IT University of Copenhagen (Collective Intelligence Research Group). She previously held roles at TalTech, The Open University (UK), and Krishna Engineering College. Doctor of Philosophy (2022) in Computer Science, Tallinn University of Technology Research Master's Degree (2014) in Knowledge Engineering, SRM Institute of Science and Technology Her research focuses on Collective Intelligence, Collective Behaviour, Social Network Analysis, Data Mining, and Digital Image Processing. She explores how individuals interact with online content, the impact of conversational affordances on deliberation quality, and correlations between social media preferences, media choices, and socio-economic/political factors. Her recent publications include work on a digital collaborative platform for the silver economy (2023), frameworks for collective intelligence (2020), pathfinding in medical imaging (2014), and AI applications in spatial data infrastructure (2019-2021). She co-supervised Sijo Arakkal Peious (2019 Master's thesis). Marie Skłodowska-Curie Postdoctoral Fellowship (2024) Seal of Excellence from European Commission (2023) SAP Fellowship Award (2016) Gold Medal at SRM University (2014) She contributed to projects like the EU Green Deal-aligned Waterway Safety Management program (2020-2021) and cost-effective 3D spatial data infrastructure (2019-2021), with funding totaling €1,996,000. Her work bridges ICT, collective behavior, and societal challenges.

• Software Engineering
• Requirements Engineering
• Formal Methods
• +۳ مورد دیگر

Steve Riddle is a Research Professor with extensive contributions to software engineering, systems of systems (SoS) modeling, and requirements engineering. His work spans formal methods, traceability frameworks, and safety-critical system design across institutions and projects. Active in software traceability and requirements engineering Key contributor to SoS architectural modeling Collaborator in formal methods education initiatives His research focuses on traceability in software systems , contract-based analysis of SoS , and protective wrapping for off-the-shelf components . He explores how project metrics predict change-proneness and develops frameworks for competency-based computing education. Recent publications analyze team-based capstone projects in computing education and instructor perspectives on software engineering pedagogy. His work bridges theoretical approaches (like VDM++) with industry-aligned problem-solving .

Tobias Lasserمشاهده پروفایل

استاد مدعو

• Computational Imaging
• Inverse Problems in Tomographic Reconstruction
• X-ray Phase-Contrast and Dark-field Imaging
• +۲ مورد دیگر

Prof. Dr. Tobias Lasser is an Adjunct Professor at the Technical University of Munich (TUM) since 2024, leading the Computational Imaging and Inverse Problems research group. He holds affiliations with the TUM School of Computation, Information and Technology and the Munich Institute of Biomedical Engineering. His academic career includes a PhD (2011) and habilitation (2017) in Computer Science from TUM, along with prior roles as a Postdoctoral Fellow and Akademischer Rat at TUM's Chair for Computer Aided Medical Procedures. His research focuses on computational imaging , inverse problems in tomography , and clinical decision support systems . Key areas include X-ray phase-contrast/dark-field imaging, light field microscopy, and multi-modal medical data analysis. Notable contributions include advancements in sparse-view CT reconstruction, artifact-free deconvolution techniques, and AI-driven diagnostic tools. Recent work emphasizes integrating deep learning with traditional imaging modalities, such as encoder-decoder architectures for anomaly detection and attention-based models for skin lesion classification. His team also explores robotic sample holders for advanced CT setups and open-source frameworks like elsa for tomographic reconstruction. Educations: Diplom-Informatiker (2006), Diplom-Mathematiker (2008), Dr. rer. nat. (2011, summa cum laude), Habilitation (2017) Awards: IEEE editorial award (2023), Best Poster (2021), Supervisory Excellence (2021), Teaching Award (2021) Labs/Teams: Munich Institute of Biomedical Engineering, Computational Imaging Group (TUM)

Christoph Hennerspergerمشاهده پروفایل

پژوهشگر ارشد

• Medical Image Computing
• Computer Vision
• Medical Robotics
• +۵ مورد دیگر

Dr. Christoph Hennersperger is a Co-Founder and CTO of OneProjects, an Irish-German MedTech startup focused on cardiac imaging and data-driven healthcare solutions. He is affiliated as a senior research scientist and lecturer at the Chair of Computer Science Applications in Medicine (Prof. Navab) at Technical University of Munich (TUM). His research integrates medical device development, computational sonography, and robotics in surgical applications. He has directed the MedInnovate fellowship program and led the EU Horizon2020 EDEN2020 project. Education: Electrical Engineering (Information Technology) from TUM (2006–2011). Professional History includes roles as a Research Fellow at Trinity College Dublin (2016–2019), Research Manager at Klinikum Rechts der Isar (2016–2018), and Fellow at BioInnovate Ireland (2015–2016). He has supervised over 20 MSc/BSc theses on topics like Ultrasound-Guided Interventions and Surgical Robotics. Teaching: He has lectured since 2014 on courses including Computer Aided Medical Procedures , Medical Augmented Reality , and MedInnovate . Research focuses on 3D ultrasound imaging, robotic interventions, and AI-driven medical solutions. Current projects include RoBildOR (robotics for multimodal imaging) and SUPRA (real-time ultrasound processing). Key innovations include the SegThy Dataset and Leg-3D-US Dataset for medical imaging, and developments in 3D ICE imaging for cardiac interventions. His work bridges hardware, software, and clinical needs, emphasizing collaborative team-driven healthcare innovation.

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +۶ مورد دیگر

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

Andreas Zieglerمشاهده پروفایل

استاد

• System Software
• Linux Configuration
• Binary Tailoring
• +۶ مورد دیگر

Andreas Ziegler is a Professor at the Department of Computer Science (INF) of Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on system software optimization , particularly in Linux kernel configurability and binary tailoring . He leads the Chair of Computer Science 4 (System Software) and develops open-source tools for minimizing software stacks while preserving functionality. University: Friedrich-Alexander University Erlangen-Nürnberg School: College of Engineering Department: Department of Computer Science Rank: Professor Research Interests: Ziegler investigates methods to automate software stack tailoring. His work spans: Configuration Interface Utilization (e.g., Linux kernel modules) Binary-Level Code Removal (ELF file manipulation without source access) Maintenance Impact Quantification (AST hashing for change detection) Publication Trends: Recent works emphasize attack surface reduction and scalable configuration testing , while earlier studies focus on feature modeling and compilation redundancy . Tools developed include GitHub-hosted open-source solutions . Advising: Supervised multiple Master’s theses on topics like header analysis for dead code detection and dynamic variability management in Linux systems.

Sara Nazariمشاهده پروفایل

پژوهشگر

• Groundwater Modeling
• Integrated Water Resources Management
• Sustainability Analysis
• +۵ مورد دیگر

Sara Nazari is a Researcher at the Leibniz Center for Tropical Marine Research (ZMT) , affiliated with the Working Group: Submarine Groundwater Discharge . She integrates interdisciplinary methodologies to address climate change, land use, and socio-economic impacts on water resources. Education: MSc in Water Resources Management and Engineering, Isfahan University of Technology (2015–2018) Her research emphasizes: Groundwater modeling Integrated and adaptive water resources management Sustainability analysis Big-Data analysis Conflict resolution theories Her publications focus on global groundwater recharge trends, computational models, and conflict resolution frameworks. She developed open-source tools for hydrological assessments and contributed datasets on global recharge patterns. She is based at Fahrenheitstr. 6, Bremen, Germany, and can be reached at sara.nazari@leibniz-zmt.de .

• Cybersecurity
• Dependable Computing
• Performance Evaluation
• +۷ مورد دیگر

William H. Sanders is a Professor at the University of Illinois at Urbana-Champaign with over 35 years of research experience in cybersecurity, dependable computing, and performance evaluation. His work bridges theoretical modeling with practical security applications for critical infrastructure systems, particularly power grids and industrial control systems. Dr. Sanders' research focuses on developing quantitative methods for security assessment, with emphasis on cyber-physical systems. He is best known for his leadership in developing the Möbius modeling framework, a comprehensive tool for performance and dependability evaluation. His work spans security metrics, intrusion detection, and resilience mechanisms for smart grid infrastructure. His publication record shows consistent scholarly output through 2023, with recent work focusing on security argumentation frameworks, advanced metering infrastructure security, and cyber-physical state estimation. The research demonstrates a clear progression from foundational modeling techniques to applied security solutions for critical infrastructure protection. Dr. Sanders has collaborated extensively with researchers across multiple institutions, with frequent co-authors including Michel Cukier, Tod Courtney, and Kaustubh Joshi. His work appears regularly in top security and dependability venues including IEEE Transactions on Dependable and Secure Computing, DSN, and QEST. His research has practical implications for securing national critical infrastructure, particularly the electrical power grid. Recent projects focus on detecting electricity theft, securing advanced metering infrastructure, and developing game-theoretic approaches to intrusion response. He has made significant contributions to establishing security metrics as a rigorous scientific discipline within cybersecurity.

• Quantum Software Engineering
• Cyber-Physical Systems
• Digital Twin Technologies
• +۳ مورد دیگر

Dr. Shaukat Ali is a leading researcher at Simula Research Laboratory (Certus Software V&V Center, Norway), with a PhD from the University of Oslo . His work bridges quantum software engineering and cyber-physical systems (CPS) validation, focusing on digital twins, autonomous vehicles, and healthcare IoT applications. Collaborates with institutions like South Dakota State University, COMSATS University, and Mohammad Ali Jinnah University Co-developed tools: QuCAT (Quantum combinatorial testing), DeepScenario (autonomous driving datasets), and EvoCLINICAL (medical digital twin evolution) Research spans quantum software testing (IBM quantum computers), uncertainty-aware validation of medical devices, and search-based optimization in CPS. His 2025 work introduces quantum circuit mutants, uncertainty-wise test oracles, and foundational models for digital twin creation. Publications in journals like ACM Transactions on Software Engineering and conferences including ICSE , GECCO , and IEEE Quantum Software reflect his interdisciplinary approach. Current projects address quantum software robustness, LLM-driven scenario realism, and safe adaptation in robotics.