Find Academic Supervisors

• Traffic Simulation
• Automotive/Mobility Engineering
• Surface Acoustic Wave Technology
• +5 more

Gerald Ostermayer is a Professor at the University of Applied Sciences Upper Austria, affiliated with the Research Center Hagenberg. His research focuses on automotive/mobility engineering, traffic simulation, and vehicle communication systems. He leads projects like pDrive (platooning energy efficiency) and AutoSimAR (AR applications in automotive). He has been active in multiple research areas including surface acoustic wave technology, smart grids, and augmented reality in vehicles. Key research topics include microscopic traffic simulation, vehicular visible light communication, and security protocols for vehicle platoons. His work integrates interdisciplinary approaches across computer science, electrical engineering, and transportation systems. He has organized workshops such as the 2nd Automotive Mixed Reality Applications and contributed to conferences like IEEE WCNC. Awards include the 2020 Best Paper Award for vehicle platoon verification research. Projects like InterGrid and Localisation & Coexistence highlight his expertise in smart infrastructure and vehicular networking.

• Heuristic Optimization
• Metaheuristics
• Simulation Optimization
• +3 more

Erik Pitzer is a Professor at FH Hagenberg, part of the Research Center Hagenberg. He leads the Josef Ressel Center for Heuristic Optimization and has contributed to projects like SMART UNLOAD (2021-2022) focusing on unloading bay optimization and BIOBOOST (2012-2014) addressing biofuel production via biomass. His work spans heuristic algorithms, simulation-based optimization, and fitness landscape analysis. Research interests include developing metaheuristics for complex optimization problems, distributed modeling frameworks, and dynamic problem adaptation. Notable contributions include Composable Evolutionary Computation (2025) and middleware for distributed systems (2024). Key Projects : SMART UNLOAD (Operational Excellence), BIOBOOST (Bioenergy Production), Josef Ressel Center (Heuristic Optimization) Advising : Supervised 4 works (names withheld) Labs : Josef Ressel-Zentrum für Heuristische Optimierung

• Robotics
• Human-Robot Interaction
• Mobile Robotics
• +3 more

Lukas Buchner is a Researcher at the Center of Excellence for Smart Production, Smart Automation and Robotics, University of Applied Sciences Upper Austria (FH OÖ), Wels Campus. He holds a Dipl. Ing. and BSc engineering degree with active research contributions in industrial robotics. His educational background includes: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl. Ing.) in Engineering Buchner specializes in human-robot collaboration systems, focusing on path planning algorithms that integrate human kinematics for safer industrial workflows. His work bridges mobile robotics and edge computing, particularly through multipath networking solutions for seamless robot roaming in production environments. Current research emphasizes trustworthiness in human-robot teams via trajectory optimization and real-time workflow modeling. No scientific awards are documented in the provided information. As Co-Investigator, Buchner contributes to two major projects: VRoboCoop (2024-2028): IBW/EFRE-funded research on trustful human-robot collaboration through kinematics-integrated path planning B&R Smart Factory 2020 (2017-2019): Development of machine communication protocols for smart production systems His grant activities focus on industrial automation applications with European regional development funding. He operates within the VRoboCoop research team at Research Center Wels, collaborating with robotics experts like Roland F. Froschauer and Karin Nachbagauer to advance smart factory technologies through practical human-robot integration solutions.

• Antioxidant Research
• Oxidative Stress
• Intestinal Health
• +5 more

Mara Heckmann is a researcher at the Upper Austria University of Applied Sciences, specializing in bioavailability and cytoprotective plant compounds through the Research Center Wels. Her work bridges Food Technology, Nutrition, and Medical Engineering domains. Primary affiliation: Fachhochschule Oberösterreich (Upper Austria University of Applied Sciences) Research centers: Center of Excellence Food Technology and Nutrition, TIMed Center Active projects until 2029 Research Focus: Heckmann's work centers on optimizing plant-derived compounds for health applications through: Bioavailability enhancement using natural matrices like spearmint Anti-inflammatory mechanisms via NF-κB pathway modulation Intestinal health improvement through microbiota-fermented extracts Epithelial barrier function protection against oxidative stress Article Trends: Recent publications emphasize Tormentic Acid derivatives , herbal fermentation , and high-content screening for dual-action compounds targeting oxidative and inflammatory pathways. Collaboration Network: Works with multiple co-investigators including Blank-Landeshammer, Weghuber, and Stadlbauer across interdisciplinary projects.

Bernd BickelView profile

Professor

• Computational Design
• Digital Fabrication
• Computer Graphics
• +7 more

Bernd Bickel is a Full Professor for Computational Design at ETH Zurich, embedded in the Design++ research center. He holds a Master's degree from ETH Zurich and a PhD from ETH Zurich under Markus Gross. Previously, he was at IST Austria (2015–2023), Disney Research, and TU Berlin as a visiting professor. His research focuses on computational design, digital fabrication, and simulation, with applications in robotics, computer vision, and material science. Key interests include physics-based simulation, geometry processing, and interdisciplinary engineering. Notable awards include the Academy of Motion Picture Technical Achievement Award (2019), SIGGRAPH's Significant New Researcher Award (2017), and the EUROGRAPHICS Best PhD Thesis (2012). He leads the Computational Design Lab at ETH Zurich and collaborates with institutions like Inria Nancy on projects such as MFX team collaborations. His work spans academic contributions (over 50 publications) and industrial applications, including the FlexMaps Pavilion (First Prize at IASS 2019) and computational tools for 3D printing. He actively mentors PhD students and oversees grants like ERC Starting Grant 'Materializable'.

Michael KohleggerView profile

Professor

• Data Engineering
• Data Science
• Machine Learning
• +7 more

Prof. Michael Kohlegger is a Professor of Data Science & Engineering at MCI | Die Unternehmerische Hochschule and holds concurrent roles as Senior Lecturer in Data Science at FH Kufstein Tirol and Information Systems at WU Wien. His academic leadership includes serving as Vice-Rector of FH Kufstein Tirol (until 2024) and Director of Studies. He focuses on data storage/analysis, holistic data processing frameworks, and industry collaboration in data-driven projects. Education: Earned a Mag. (2008) and Dr. (2013) in Business Informatics from the University of Innsbruck. Prior to academia, he worked as a Business Analyst (SOS Children's Villages International) and Research Assistant (University of Innsbruck). Research emphasizes mobility systems (e.g., Vision2Move multi-object tracking, ALMODA anomaly detection) and human-technology interaction (voice assistants usability studies). Projects also include digital tourism initiatives and SME-focused data analysis models. Scientific Contributions: Co-authored works on AR work instructions, autonomous driving validation, railway monitoring systems, and process improvement frameworks. Leadership: Spearheaded projects like Digital Tourism Expert (2018–2021) and Usability of Voice Assistants (2018–2021).

Christian PloderView profile

Lecturer

• Information Systems
• Quality Management
• Blockchain Technology
• +7 more

Christian Ploder is a Lecturer at MCI Management Center Innsbruck, focusing on Information Systems, ERP systems, and e-business. He holds a Dr. in Information Systems from the University of Innsbruck and has over 15 years of professional experience across academia and industry. Education: PhD in Information Systems, University of Innsbruck (2005-2009) Mag. in Information Systems, University of Innsbruck (2000-2005) Research interests include Quality Management Systems, Blockchain in healthcare, IoT applications in education, and gamification in learning. His work bridges theoretical frameworks with practical implementations in ERP systems, data analytics, and digital transformation. Recent articles explore blockchain for electronic health records, AR in engineering education, and machine learning for manufacturing cost estimation. These contributions highlight interdisciplinary approaches combining IT systems with organizational and educational contexts. Awarded for contributions to knowledge management frameworks and ERP integration strategies. Advised over 50 students, focusing on topics like RPA post-implementation, AR in classrooms, and cybersecurity in SMEs. Led projects on digital twin simulations, cloud-based ERP systems, and enterprise risk management. Active in SAP Academic Community and IEEE conferences, emphasizing practical teaching tools for emerging technologies.

Phillip KronthalerView profile

Lecturer

• Control Engineering
• Mechatronics
• Robotics
• +4 more

Phillip Kronthaler is affiliated with UMIT TIROL and MCI | The Entrepreneurial School, where he teaches advanced control engineering courses. He holds a Dr. techn. (PhD) in Engineering Sciences from UMIT TIROL and has extensive experience as a Research Assistant in mechatronics and electrical engineering. His work includes roles as a systems engineer and hardware developer, bridging academic research with industrial applications. Education: Dr. techn., Doctoral Programme Engineering Sciences, UMIT TIROL, 2014–2022 Dipl.-Ing., Mechatronics, UMIT TIROL & LFU-Innsbruck, 2012–2014 BSc Mechatronics, UMIT TIROL & LFU-Innsbruck, 2009–2012 Research focuses on model-based control systems, optimization of robotic processes, and adaptive impedance control in industrial settings. His work emphasizes practical applications in robotics, automation, and precision engineering. Award highlights include a Teaching Award (1st Place), Best Paper Award at VDI Fachtagung Mechatronik, and certifications in academic teaching. His publications span conferences such as IFAC Symposium on Mechatronic Systems and IEEE CASE, addressing topics like trajectory planning and vibration reduction. He has advised no listed students but contributes to academic teaching and research teams at UMIT TIROL and MCI. His research often involves collaborations on high-precision robotic systems and semiconductor industry automation.

• Finance
• Healthcare Systems
• Evolutionary Algorithms
• +2 more

Ronald-Karl Hochreiter is a Privatdozent (Priv.-Doz.) and researcher affiliated with the Institute of Security Research. Holding a doctorate (Dr.), he has maintained active research output from 2010 to 2021 across multiple disciplines including finance, healthcare systems, and computational social science. His research demonstrates significant interdisciplinary breadth: in finance, he investigates network centrality in interbank markets and Markov chain-based credit risk modeling; in healthcare, he analyzes rheumatology specialist supply through nationwide surveys; and in computer science, he develops evolutionary algorithms to mitigate negative opinion spread in social networks. His scientometrics work further contributes to journal meta-ranking methodologies using statistical estimators. Publication trends reveal sustained engagement with real-world economic and social challenges, with recent work (2020-2021) emphasizing financial stability systems and healthcare resource allocation, while earlier contributions focus on risk modeling and academic evaluation frameworks. Collaborative patterns show consistent co-authorship across institutional and disciplinary boundaries. Scientific Awards: No scientific awards are documented in the provided text Advising and Grants: No information regarding student supervision, grants, or funding sources is available in the source material Labs and Teams: The Institute of Security Research affiliation is noted, but specific laboratory structures, research teams, or collaborative groups are not described

Antonia GolabView profile

Researcher

• Energy System Analysis
• Spatial Modeling
• Transport Infrastructure
• +2 more

Antonia Golab is a University Assistant and PhD Candidate at the Energy Economics Group (EEG) of Vienna University of Technology , specializing in energy system analysis with high spatial resolution. Her work focuses on modeling energy and transport infrastructure, particularly for electric vehicle charging and decarbonization strategies. M.Sc. in Geodesy and Geoinformation (TU Wien, 2021) Her research integrates spatial modeling and transport infrastructure to address challenges in renewable energy integration and electric vehicle deployment . She contributes to projects like iDesignRES (2023–2027), which emphasizes renewable energy system design. Antonia's publications explore spatio-temporal charging models, freight transport decarbonization, and grid flexibility through commercial EV fleets. Her GitHub repositories ( StressTestFastChargingInfr , HighCharge ) demonstrate her commitment to open-source energy modeling tools.

• Recommender Systems
• User Modeling
• Generative AI
• +4 more

Thomas Elmar Kolb is a PreDoc Researcher at Technische Universität Wien , affiliated with the Data Science group (E194-04) and the Curriculum Commission for Business Informatics . His research focuses on Recommender Systems , User Modeling , and Generative AI , emphasizing fairness, bias, and cross-domain applications. Recent research trends in his work include: Advancing fairness metrics in news recommendation systems Integrating Large Language Models (LLMs) for cross-domain recommendation Developing Austrian language polarity resources (ALPIN Dictionary) Exploring the interplay between trust and serendipity in user experiences Analyzing user roles in online forums for behavioral modeling Supervisions include diploma theses on group fairness, podcast recommendation systems, and populism detection. Key projects involve the 2022-2028 CDL-RecSys initiative and the 2020-2021 DYSEN project on dynamic sentiment analysis.

• Artificial Intelligence
• Theoretical Computer Science
• Automated Reasoning
• +3 more

Martin Kronegger serves as a Senior Lecturer in the Department of Automation Systems at Vienna University of Technology (TU Wien), where he teaches courses including Fundamentals of Digital Systems, Computer Engineering Projects, and Scientific Projects in Computer Science for the 2025W and 2026S semesters. His academic profile demonstrates active engagement in both teaching and research within the Faculty of Electrical Engineering and Information Technology. Dr. Kronegger's research centers on theoretical foundations of artificial intelligence with emphasis on parameterized complexity, automated planning, and answer set programming. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning, as evidenced by publications in premier venues like Artificial Intelligence journal and AAAI conferences. Key research themes include: Backdoor techniques for planning problems Parameterized complexity analysis of AI problems QBF solving for conformant planning SAT-based verification methods for software models His publication landscape reveals consistent contributions to parameterized complexity theory applied to planning and logic programming from 2011-2019, with significant work on multiparametric analysis of answer set programming and SAT-based approaches to planning problems. The research trajectory shows deepening theoretical contributions while maintaining connections to practical AI applications. Dr. Kronegger has supervised at least one diploma thesis on planning solvers and has participated in major research projects including FAIR (2013–2018), START (2014–2022), and X-TRACT (2014–2018). His project work demonstrates sustained collaboration with research groups at TU Wien focused on computational logic and automated reasoning. Based in the Automation Systems research group (E191-03) at TU Wien's Treitlstraße campus, he maintains active research collaborations through projects like the START program and contributes to the international answer set programming community as evidenced by involvement in the Fourth Answer Set Programming Competition.

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +4 more

Georg Langs is a PartTime Lecturer at the Faculty of Informatics, Vienna University of Technology , specializing in Medical Image Analysis , Machine Learning , and Computer Vision applications in Radiology and Neuroscience . His research bridges Biomedical Engineering and Pattern Recognition to advance diagnostics and predictive modeling in healthcare. His work focuses on spatiotemporal disease modeling (e.g., bone lesion progression in multiple myeloma), fetal brain connectivity via in-utero fMRI, and automated image segmentation using deep learning and probabilistic frameworks. He has developed cloud-based benchmarking systems for medical image analysis and contributed to functional connectivity mapping in pediatric and fetal neuroscience. Key article trends include AI-driven radiology , dynamic lesion prediction , and reproducibility in fetal imaging . His collaborations span institutions in Austria, the UK, and China. He mentors students in deep learning , longitudinal image analysis , and medical AI through diploma theses.

• Artificial Intelligence
• Explainable AI
• Logic
• +5 more

Xinghan Liu is a PostDoc Researcher at the Knowledge-Based Systems group within Vienna University of Technology. Their work bridges Artificial Intelligence , Logic , and Environmental Engineering , as evidenced by their publications in both computer science and hydrology journals. Contact: xinghan.liu@tuwien.ac.at . Research Interests: Explainable AI and logic-based classifier modeling Counterfactual and intuitionistic conditional logics Legal case-based reasoning in computational frameworks Hydrodynamic and environmental adaptation to infrastructure projects Recent Work Trends: Recent articles focus on classifiers (2021–2024), explainable AI (2023–2024), and legal reasoning (2022–2023). A 2023 hydrology publication highlights interdisciplinary research on river systems affected by damming.

• Machine Learning
• Digital Preservation
• Privacy-Preserving Data Analysis
• +6 more

Rudolf Mayer is an Lecturer at the School of Logic and Computation and School of Information Systems Engineering at Vienna University of Technology . He is affiliated with the university through his roles in teaching and research, focusing on Machine Learning , Digital Preservation , and Privacy-Preserving Data Analysis . His work spans both academic research and applied projects, with a strong emphasis on collaborative and secure data management . His research interests include Machine Learning , Privacy-Preserving Techniques , Digital Preservation , and Music Information Retrieval . Over his career, Mayer has contributed to Developing frameworks for digital preservation of scientific and business processes Advancing privacy-preserving data analysis through platforms like WellFort Exploring adversarial machine learning and model stealing attacks Integrating semantic web technologies into data management systems His publications highlight trends in collaborative cybersecurity , model robustness , and digital archiving with applications in federated learning, self-organizing maps, and music analysis. Mayer has supervised 15+ graduate students , covering topics such as adversarial attacks , model watermarking , and privacy-preserving anomaly detection . Notable projects he has been involved in include the NEWSROOM initiative for cybersecurity automation, the Research Studio Digital Memory Engineering , and contributions to the PLANETS project for long-term digital access.