جست‌وجوی استاد و پژوهشگر

Jean D. Hallewell Haslwanterمشاهده پروفایل

استاد

• Assistive Technology
• Ambient Assisted Living
• Aging in Place
• +۵ مورد دیگر

Jean D. Hallewell Haslwanter serves as Professor at the University of Applied Sciences Upper Austria, Campus Wels, where she leads research in the Research Center Wels with affiliations to the Center of Excellence Automotive/Mobility and Center of Excellence for Smart Production. Her work integrates thermography, non-destructive testing, and smart automation with human-centered design principles. Education PhD in Informatics, Thesis: User-Centered Development of sensor-based Systems for Older People, TU Wien (2017) Master in Computing Sciences, Thesis: Participatory Design Methods in the Context of Human-Computer Interaction, University of Technology Sydney (1996) Bachelor in Mathematics, Stanford University (1987) Research Interests Dr. Haslwanter pioneers assistive technologies for aging populations , investigating ambient assisted living systems, aging-in-place solutions, and ageism in user experience design. Her non-destructive evaluation research applies AI to thermography and robotics for industrial inspection, while her educational technology work develops programming pedagogy for novices. These interconnected fields address critical gaps in technology accessibility for elderly users and workforce training. Publication Trends Recent publications (2022-2024) reveal dual research tracks: assistive technology studies examining computer literacy barriers among seniors and ageism in design processes, alongside programming education innovations using test-first methodologies. This demonstrates consistent focus on human-technology interaction across demographic spectrums. Scientific Awards No specific awards were documented in the source material. Advising and Grants She has supervised at least one graduate student. Major funded projects include: FLARE (2025-2027): Developing human-centered AI for high-rate non-destructive evaluation in industrial settings COIN Cooperation & Innovation (2016-2021): Creating automatic assistance systems for production workplaces Palmaster Usability Reviews (2014-2015): Designing interface solutions for storage systems Labs and Teams As core faculty in Research Center Wels, she directs the Smart Automation and Robotics group and Thermography research cluster. Her interdisciplinary teams collaborate with automotive manufacturers and healthcare technology firms on EU-funded initiatives, maintaining strong industry partnerships through the Center of Excellence Automotive/Mobility.

Josef Pichlerمشاهده پروفایل

استاد

• Software Systems
• Reverse Engineering
• Documentation Generation
• +۵ مورد دیگر

Josef Pichler serves as a Professor at the Research Center Hagenberg within the University of Applied Sciences Hagenberg, specializing in the Information & Communications Technology focal area. His academic profile demonstrates continuous research activity with publications spanning from 1998 through 2024, showing particularly productive periods in 2008-2010, 2013-2014, and consistently from 2017 onward. Dr. Pichler's research focuses on practical software engineering solutions with strong industrial applications. His primary interests include software systems , reverse engineering , documentation generation , and domain-specific languages . His work bridges theoretical computer science with practical industry needs, developing tools that address real-world software maintenance and comprehension challenges. Analysis of his recent publications reveals a clear research trajectory toward integrating artificial intelligence with traditional software engineering practices. His 2023-2024 work particularly emphasizes AI-assisted programming, semantic differencing techniques, and low-code development platforms for industrial applications like welding robot control. This demonstrates his ability to adapt to emerging technologies while maintaining focus on practical software engineering problems. Dr. Pichler has delivered numerous presentations on topics ranging from ChatGPT applications in computer science education to advanced semantic differencing approaches. His activities indicate active engagement with both academic and industry communities, particularly in the German-speaking region. While specific grant information isn't detailed in the provided text, his research output suggests involvement in projects related to software documentation, reverse engineering tools, and AI-assisted development environments. His supervision of student work (indicated by 'Supervised Work (3)') demonstrates his commitment to academic mentorship alongside his research activities. The Research Center Hagenberg, where Dr. Pichler is based, appears to be a significant hub for ICT research with strong industry connections, as evidenced by his publications on industry collaboration and practical software solutions.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Fabian Kovac is a Junior Researcher in the Data Intelligence research group at the Institute of IT Security Research, Department of Computer Science and Security, St. Pölten University of Applied Sciences. His work bridges artificial intelligence, data science, and IT security with a focus on practical implementations and theoretical foundations. His research spans critical AI domains including: Trustworthy and user-centered AI systems Attainable utility preservation for AI safety Sustainable and energy-efficient AI development Active learning methodologies Environmental applications of machine learning Analysis of his 2022-2026 publications reveals a cohesive trajectory toward making AI systems more robust, interpretable, and environmentally responsible. His work consistently addresses the tension between AI capability and safety, with recent contributions focusing on baseline methods for utility preservation and sustainability metrics. The upcoming TrustAI platform (2026) demonstrates his commitment to translating theoretical frameworks into user-centered tools. No scientific awards were documented in the available sources. Regarding academic advising and research funding, no student supervision records or specific grant details were disclosed in the provided materials. Kovac operates within the Data Intelligence research group at the Institute of IT Security Research, contributing to interdisciplinary projects that merge security principles with cutting-edge AI development across academic and industrial collaborations.

Josef Schichoمشاهده پروفایل

استاد

• Algebraic Geometry
• Kinematics
• Robotics
• +۴ مورد دیگر

Josef Schicho is a Full Professor at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University in Linz, Austria. His research focuses on algebraic geometry, computational mathematics, and their applications in kinematics, robotics, and geometric modeling. He leads projects exploring flexible polyhedra, mechanism design, and symbolic computation techniques for solving geometric problems. Key research interests include the study of rigid and flexible frameworks, algebraic methods in robotics, and the application of geometric algorithms to problems in computer vision and sensor networks. His work bridges theoretical mathematics with practical engineering solutions, particularly in mechanism synthesis and geometric modeling. Recent contributions include advancements in the theory of flexible polyhedra, classification of linkages with high mobility, and computational tools for analyzing robot kinematics. His publications often address problems at the intersection of algebraic geometry and engineering, such as reconstructing geometric configurations from partial data or optimizing motion planning for robotic systems. Dr. Schicho's affiliations include the RISC Faculty at JKU, where he actively contributes to the development of symbolic computation software and collaborates with international researchers in computational algebraic geometry. His office is located at Schloss Hagenberg, Room 2.6-1.

• Cyber-Physical Systems
• Smart Grids
• Swarm Intelligence
• +۵ مورد دیگر

Prof. Wilfried Elmenreich is a Professor at the Department of Networked and Embedded Systems at Alpen-Adria-Universität Klagenfurt. He serves as a Member of the Senate and Member of the Works Council for Academic Staff. His research focuses on Cyber-Physical Systems, Smart Grids, Swarm Intelligence, and Sustainability Technologies. He leads projects involving swarm robotics, decentralized energy management, and educational technology. Key research areas include: Design of swarm-based algorithms for production scheduling and robotics Development of frameworks like SwarmFabSim for bottom-up optimization Non-Intrusive Load Monitoring (NILM) using neural networks and edge computing Integration of federated learning into smart grid systems Sustainability education and pandemic-related behavioral studies His work combines theoretical contributions with practical implementations, such as the CPSwarm project for swarm system design and the Yomopie energy monitoring tool. Recent publications emphasize interdisciplinary approaches, blending robotics, machine learning, and electrical engineering to address challenges in energy systems and autonomous systems.

Horst Posseggerمشاهده پروفایل

پژوهشگر

• Computer Vision
• Autonomous Systems
• Multiple Object Tracking
• +۳ مورد دیگر

Horst Possegger is a Computer Vision researcher at the Institute of Computer Graphics and Vision at Graz University of Technology, Austria. He holds a BSc and MSc in Software Development and Business Management (2011, 2013) and a PhD in Computer Science (2018), all from Graz University of Technology. His research focuses on multiple object tracking/detection, human behavior analysis, and video analysis with applications in autonomous systems, LiDAR data processing, and robotics. Key projects include trajectory prediction, 3D object detection, and domain adaptation techniques for real-world scenarios. Recent work emphasizes cross-modal learning (e.g., vision-language models for LiDAR data), robust localization in challenging environments, and lightweight motion prediction models. He contributes to the Learning, Recognition & Surveillance (LRS) group and has published extensively in top-tier venues like IEEE/CVF CVPR and IROS. Publications span autonomous driving benchmarks, UWB-based localization, and warehouse automation. His work often bridges theoretical advancements with practical implementations in robotics and surveillance systems.

• Answer Set Programming
• Knowledge Representation and Reasoning
• Cyber-Physical Systems
• +۳ مورد دیگر

Dr. Peter Schüller is a Professor affiliated with the Department of Knowledge-Based Systems at TU Wien (Vienna University of Technology). He holds the academic title of Privatdozent (Priv.-Doz.) and has a background in Technical Engineering (Dipl.-Ing. Dr.techn. / Bakk.techn.). His roles include academic research, consulting services for intelligent automation, and partnership with Potassco Solutions. He is based at Favoritenstrasse 11, Room HG0312, and can be contacted via peter.schueller@tuwien.ac.at or contact@peterschueller.com. Education : Master's Thesis: 'Reconstructing borders of manually torn paper sheets using integer linear programming' (2008) PhD (Dr.techn.) in Informatics Habilitation (Privatdozent) qualification Research Interests : Schüller specializes in declarative problem solving through Answer Set Programming (ASP), with focus areas including hybrid knowledge integration systems, inconsistency management, and applications in robotics, traffic optimization, and industrial automation. His work bridges theoretical advances in computational logic with practical implementations in enterprise software architecture and database systems. Projects & Grants : He has led projects funded by the Austrian Research Promotion Agency (FFG), Austrian Science Fund (FWF), and Vienna Science and Technology Fund (WWTF). Key projects include: 'Dynamic knowledge-based (re)configuration of cyber-physical systems' (2017–2020) 'Integrated Evaluation of Answer Set Programs' (2015–2018) 'Inconsistency Management for Knowledge-Integration Systems' (2009–2012) Consulting & Partnerships : Provides services in enterprise software design, database optimization, GDPR compliance, and hybrid knowledge systems. Official partner of Potassco Solutions. Collaborates on initiatives like AI4EU and HumanE-AI-Net. Labs & Teams : Active in TU Wien's Knowledge-Based Systems Group. Involved in developing the DLVHEX and Hexlite solvers.

• Model-Driven Engineering
• Model Transformation
• Web Engineering
• +۳ مورد دیگر

Manuel Wimmer is a Lecturer in Business Informatics at TU Wien's Faculty of Informatics, specializing in model-driven engineering methodologies. His research develops foundations for model transformation, metamodeling, and interdisciplinary engineering. Research interests include model-driven software engineering, cyber-physical systems, web engineering, and industrial automation, with applications in smart production systems. Current work focuses on bridging IT/OT domains through standardized modeling approaches. Recent publications address quantum-edge cloud architectures, AI-enhanced modeling, and industrial security challenges. Article trends demonstrate strong focus on modeling language engineering, interoperability solutions, and quality assurance in complex systems. Leads the Christian Doppler Laboratory for Model-Integrated Smart Production and coordinates EU projects on low-code engineering platforms. Supervises doctoral research in model-driven technologies and software quality.

• Cyber-Physical Systems
• Biological Systems
• Control Systems
• +۶ مورد دیگر

Radu Grosu is a Full Professor and Head of the Cyber-Physical Systems Research Unit at TU Wien. His research focuses on modeling, analysis, and control of cyber-physical and biological systems, with applications in robotics, autonomous systems, medical imaging, and formal verification. He leads the Scuderia Segfault team for autonomous F1TENTH racing and has extensive collaborations with industry partners like TTTech Auto AG and FFG. His work integrates machine learning, control theory, and formal methods to address challenges in safety-critical systems and autonomous decision-making. Roles: Full Professor, Head of Research Unit, Faculty Council Substitute Member Affiliations: TU Wien, Scuderia Segfault, Austrian Science Fund (FWF) projects Research interests span cyber-physical systems (CPS), cardiac-cell networks, genetic regulatory networks, and AI-driven solutions for healthcare and manufacturing. He has pioneered methods in neural circuit policies, flocking control, and real-time reinforcement learning. His projects include EdgeAI for embedded systems, radiation treatment optimization in glioblastoma, and autonomous vehicle testing frameworks. Key contributions include: Developing Lagrangian reachability analysis for safety verification Advancing neuromorphic IoT architectures and sensor networks Creating tools like DeepSTL for translating requirements into specifications Grants include FFG-funded projects on autonomous driving examiners and energy-efficient neuromorphic systems. His work bridges theoretical foundations with practical implementations in CPS resilience, medical diagnostics, and industrial automation.

• Programming Languages
• Type Systems
• Object-Oriented Programming
• +۳ مورد دیگر

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Franz Puntigam is an Associate Professor at the Department of Compilers and Languages within the Faculty of Informatics at Technische Universität Wien. His research focuses on programming paradigms, type systems, object-oriented programming, and concurrent programming. He teaches courses such as Programming Paradigms, Type Systems, and Scientific Research and Writing. Dr. Puntigam’s work explores foundational aspects of programming languages, emphasizing concurrency and type system design. His research includes studies on synchronization mechanisms, contextual execution environments, and design methodologies for robotic software systems. His publications span topics like token-based synchronization, dynamic process types, and aliasing effects in object-oriented languages. He supervises numerous diploma and master’s theses on advanced topics such as concurrency in Java, type systems for configuration languages, and actor-based programming. His contributions to education and research are further reflected in his involvement with projects like migrating business software from VB to Java/Unix.

• Formal Methods
• Program Analysis
• Verification
• +۹ مورد دیگر

Florian Zuleger is an Associate Professor at TU Wien's Department of Formal Methods in Systems Engineering (E192-04). He has a 100% research focus and serves as Curriculum Coordinator for the Master’s program in Verification and Automated Reasoning. His work emphasizes automated methods for termination analysis, resource-bound estimation, verification of programs with dynamic data structures, parameterized systems, and automated feedback for introductory programming tasks. He has led projects funded by the European Commission (2024–2027), Austrian Science Fund (FWF), Amazon Research Awards, and Vienna Science and Technology Fund. Research Interests: Automated Program Verification Formal Methods for Concurrent Systems Separation Logic and Decision Procedures Resource and Complexity Analysis Parameterized Model Checking Inductive Logic in Verification Grants & Advising: Zuleger’s recent grants explore verification of safety-critical applications and automated cost analysis. He has advised numerous students on topics ranging from verified data structures to fault-tolerant algorithms. His projects include collaboration with Amazon, the European Commission, and FWF. Labs/Teams: Involved with the LogiCS Research Group, focusing on logical methods in computer science, and contributes to tool development (e.g., ATLAS, SpecBMC, SL-COMP competitions).

• Computational Social Choice
• Algorithmic Game Theory
• Formal Methods
• +۶ مورد دیگر

Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.

Chee Yapمشاهده پروفایل

استاد

• Computer algebra
• Symbolic-numeric computation
• Robotics
• +۵ مورد دیگر

Chee Yap is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, where he has been a faculty member since 1979. He previously held positions at the University of Southern California and has served as a Visiting Professor at several prestigious institutions including Oxford University, Seoul National University, and the Chinese Academy of Sciences. Dr. Yap received his BS in Computer Science & Mathematics (Double Major) from MIT in 1975 and his PhD in Computer Science from Yale University in 1980. His academic journey began at the Boys' Wing, Royal Military College in Malaysia. His research spans several areas in theoretical computer science and computational mathematics, with primary focus on Computer algebra and symbolic-numeric computation , Robotics , Visualization , Theory of algorithms , Exact computation , Complexity theory , and Computational geometry . His work bridges theoretical foundations with practical implementations, exemplified by his development of the Core Library for exact computation and the G-Vis System for visualization. Dr. Yap's publication record shows a consistent focus on robust geometric computation, algorithmic motion planning, and exact numerical methods. His recent work (2021-2026) demonstrates continued innovation in parameter estimation for differential equations, subdivision models, path planning algorithms, and root clustering techniques. His research maintains a strong connection between theoretical computer science and practical applications in robotics and scientific computing. Among his notable honors are the CAS President's International Visiting Scientist Fellowship (2018), KIAS Scholar award (2006-2009), Lester R. Ford Award (1990), and Fulbright Scholar Award (1988). He has also received multiple ACM Recognition of Service Awards for his conference leadership. Dr. Yap has been continuously funded by the National Science Foundation since 1984, securing approximately 20 grants valued at about $5 million. He has mentored 17 PhD students and 4 postdocs throughout his career. His editorial service includes long tenures on the boards of SIAM Journal of Computing (1985-2002), Journal of Computer and System Sciences (1986-2014), and Journal of Symbolic Computation (1988-2004), among others. He is the principal architect of the Core Library (http://cs.nyu.edu/exact) and co-architect of the G-Vis System (http://cs.nyu.edu/visual), and holds two visualization patents. His leadership in the academic community is evident through his roles as General Chair of the 42nd ISSAC conference and various program committee positions for major conferences including FOCS, ISSAC, SoCG, and WAFR.

• Computer Vision
• Autonomous Driving
• 3D Reconstruction
• +۵ مورد دیگر

Ao.Univ.Prof. Margrit Gelautz is an Associate Professor at TU Wien's Computer Vision research department (E193-01), affiliated with the Institute for Software Technology and Interactive Systems (E188). Her work focuses on computer vision applications in autonomous driving, robotics, and 3D reconstruction. She leads projects on driver monitoring systems, vulnerable road user detection, and human-robot interaction. Recent research includes 3D bounding box prediction, synthetic data-driven pose estimation, and real-time sensor systems. She advises over 20 graduate students and collaborates on automotive platforms integrating vision-based safety systems. Her contributions span sensor fusion, stereo matching algorithms, and interactive video tools. Key projects include multimodal sensor-lighting systems for traffic safety, 3D scene completion, and intelligent workflow design for low-cost 3D film production. Her work emphasizes practical applications like in-cabin monitoring systems and adaptive lighting control. She develops semi-automatic annotation tools for traffic datasets and explores nonverbal communication in human-robot interaction using platforms like Pepper humanoid robots.

Alexey Ignatievمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +۳ مورد دیگر

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.