Find Academic Supervisors

• Cyber-Physical Systems
• Computer Science
• Robotics
• +4 more

Jaroslav Klapalek is a PreDoc Researcher in the Cyber-Physical Systems department at Vienna University of Technology (TU Wien). His work focuses on formal verification of distributed timed-automata, resilient control mechanisms, and timing anomalies in automotive architectures. He is affiliated with the Embedded Systems Group (E191-01) and has contributed to research on machine learning for industrial predictive maintenance, consensus algorithms, and energy-efficient scheduling. Research Trends: Recent publications highlight expertise in formal verification of real-time constraints, security analysis in industrial CPS, and timing predictability under clock drift and network delays. Key methods include model checking, hybrid system modeling, and safety-critical protocol validation. Scientific Awards:

• Theoretical Computer Science
• Complexity Analysis
• Automated Reasoning
• +4 more

Univ.-Prof. Dr. Georg Moser is a Professor in the Department of Computer Science at the University of Innsbruck. His research focuses on theoretical computer science, complexity analysis, automated reasoning, and formal methods. He leads the Theoretical Computer Science (TCS) group, with expertise in term rewriting systems, programming language semantics, and algorithmic learning theory. Key research areas include: complexity analysis of programs via rewriting techniques, automated tools for resource analysis (e.g., ATLAS), and foundational work on proof theory and logic. His work bridges theory and practice, addressing challenges in program verification and probabilistic systems. Selected publications (2021–2025) highlight advancements in reinforcement learning, quantum program analysis, and modular rule-based systems. He teaches courses like 'Discrete Mathematics' and 'Introduction to Theoretical Computer Science'.

• X-ray Computed Tomography
• Deep Learning
• Materials Science
• +3 more

Miroslav Yosifov is an active researcher at the University of Applied Sciences Wels, specializing in X-ray Computed Tomography (X-CT), deep learning, and materials science. His work bridges industrial applications with advanced AI techniques to improve nondestructive testing and process optimization. Key projects: xCTing (MSCA-funded, 2021–2025) and PSSP (COMET-funded, 2018–2022). Research areas: X-CT, defect detection, fatigue life prediction, and AI-driven industrial automation. His recent publications focus on integrating machine learning with XCT for aerospace and aluminum components, with emerging trends in probabilistic fatigue assessment and unsupervised segmentation. Collaborations span European institutions, emphasizing Industry 4.0 process chains.

• Robotics
• Machine Learning
• Representation Learning
• +3 more

Fotios Lygerakis is a doctoral student and university assistant at the Chair of Cyber-Physical Systems, University of Leoben, Austria. He focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and robot learning through imitation and reinforcement learning. His work aims to enable robots to mimic human learning processes via abstraction and creativity, targeting tactile-rich manipulation tasks and human-robot interaction. Education: Pursuing a doctoral degree in Cyber-Physical Systems at the University of Leoben. Research Interests: Representation Learning and Visuotactile Fusion using self-supervised methods Robot Learning: Reinforcement Learning, Imitation Learning, Sim2Real Transfer Human-Robot Interaction and tactile-aware manipulation Recent Achievements: Received the Best Student Paper Award at the 2024 Ubiquitous Robotics Conference for his work on M2CURL. His research has been presented at venues like ICRA, IROS, and NYU. He is also a contributor to projects like MViTaC and CR-VAE. Engagement: Served on organizing committees for ECESCON 9 and PETRA 2021, and volunteered at TED AI Vienna 2024. Active in teaching as a Machine Learning lab assistant and through outreach initiatives like Neural Coffee Reading Group.

• Automated Software Verification
• Formal Methods
• Model Checking
• +7 more

Georg Weissenbacher is a Full Professor in the Institute of Logic and Computation at TU Wien, leading the Formal Methods in Systems Engineering group. His research focuses on automated software verification, formal methods, and concurrency bug analysis. He holds a DPhil from Oxford and has held positions at Princeton and ETH Zurich. Education: TU Wien (since 2010), Postdoctoral Researcher at Princeton University (2010–2012), PhD from Oxford University (2008–2010), Research Assistant at ETH Zürich (2005–2010), Master's at TU Graz (until 2003). Research Interests: Automated software verification, formal methods, model checking, concurrency bug detection, logic and automated reasoning, symbolic simulation, and test case generation. His work addresses challenges like Heisenbugs in concurrent systems and robustness verification of neural networks. Grants & Projects: Lead projects such as AutoTest (Vienna Science Fund), LogiCS-Scholarships (2017–2025), and contributions to the National Research Network 'Rigorous Systems Engineering' (FWF). Active in the FWF-funded doctoral college 'Automated Reasoning' (2025). Advising: Supervised over 20 PhD and master's students, including notable works on Heisenbugs, mutation testing, and neural network verification. Current advisees include Thomas Hader and Sarah Sallinger. Labs & Teams: FORSYTE Group at TU Wien, collaborating on formal methods and automated reasoning. Engaged in international conferences like CAV and FMCAD as program chair/co-chair.

Benedikt LorchView profile

Researcher

• Steganography
• Multimedia Forensics
• Machine Learning
• +3 more

Dr. Benedikt Lorch is a researcher currently affiliated with the Security and Privacy Lab at the University of Innsbruck, Austria. His professional journey includes roles as a Postdoctoral Fellow (2023–2024) and Research Assistant (2022–2023) at the same institution, preceded by research and academic roles at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, including a Ph.D. in Computer Science (2018–2023) focused on cybercrime and forensic computing. He has also held visiting researcher positions at Dartmouth College (USA) and Imperial College London (UK). His research interests center on steganography, steganalysis, multimedia forensics, and machine learning applications in digital security. Notable work includes analyzing JPEG image directionality for security implications, improving steganalysis techniques using leaked cover thumbnails, and developing reliable machine learning models for forensic image analysis. His contributions address challenges in digital forensics, such as detecting image tampering, authenticating multimedia content, and enhancing degraded license plate recognition. Key academic awards include the ASQF award for outstanding studies (2019) and a DAAD scholarship for international research (2017). His work spans projects like UNCOVER and emphasizes compliance with regulations like the Artificial Intelligence Act in forensic analysis. Beyond technical contributions, Lorch has explored medical imaging applications, including motion artifact detection in MRI and heart rate estimation using wearable sensors. His research trends reflect a focus on leveraging probabilistic methods and deep learning to enhance forensic reliability, with a particular emphasis on JPEG-based steganalysis and digital image security. Collaborations with institutions like the Pattern Recognition Lab and Biomedical Image Analysis Group highlight interdisciplinary engagement in computer science and medical imaging.

Cem ErsoyView profile

Professor

• Computer networks
• Pervasive computing
• Edge computing
• +1 more

Cem Ersoy is a Professor at Bogazici University 's Department of Computer Engineering, with a career spanning over 30 years. He has held visiting positions at the University of Trento, Italy, and previously served as Department Head at Bogazici University multiple times. Education : Ph.D. in Electrical Engineering (Polytechnic University, 1992), M.S. in Electrical Engineering (Bogazici University, 1986), B.S. in Electrical Engineering (Bogazici University, 1984) His research focuses on computer networks , pervasive computing , edge computing , and wireless networks . Recent work highlights AI integration in air computing, stress monitoring with wearables, and edge network optimization. Articles span applications of multi-agent DRL, probabilistic tracking, and network slicing. Scientific awards : Elected to the Academy of Sciences of Turkey (2022) Bogazici University Research Excellence Awards (Junior 2007, Senior 2014) He has supervised 24 PhD and 58 MS theses. As principal investigator, he led 35 funded research projects by agencies such as the EU, TUBITAK, and Bogazici University Research Fund. His Google Scholar profile shows 11,856 citations, h-index 43, and i10-index 130.

Wenzel FriedemannView profile

Professor

• Seismic Early Warning Systems
• Hazard and Risk Assessment
• Risk Management in Urban Areas
• +3 more

Prof. Wenzel Friedemann is a Full Professor of Geophysics and Director of the Geophysical Institute at Karlsruhe Institute of Technology (KIT) since 1994. His expertise spans seismic early warning systems, hazard assessment methodologies, and risk management in urban environments. He coordinates the Center for Disaster Management and Risk Reduction Technology (CEDIM). Key contributions include developing PreSEIS neural network-based early warning systems and probabilistic hazard models for regions like Romania. Awards include membership in Academia Europaea (1999) and an Excellence Award (2001). Research focuses on intermediate-depth earthquakes (e.g., Vrancea zone), site response analysis, and urban seismic vulnerability. Notable projects include Bucharest's earthquake early warning systems and Amurian plate stress field studies. His work integrates tectonophysics, real-time monitoring, and policy-driven risk reduction strategies.

Guodong WangView profile

Researcher

• Machine Learning
• Industrial Data Processing
• Cyber-Physical Systems
• +5 more

Dr. Guodong Wang is a researcher affiliated with the Forschungsbereich Cyber-Physical Systems at TU Wien. His work focuses on machine learning applications in industrial data processing, fault diagnosis, and health monitoring systems. He has collaborated extensively with Prof. Radu Grosu and other researchers on projects involving neural networks, Bayesian methods, and sensor data analysis. Education: PhD in Technical Sciences (Dr.techn.) MSc in Engineering His research interests bridge theoretical machine learning with practical industrial challenges, including: Development of generative neural network models for quality prediction in manufacturing Fault prognostics for semiconductor and marine machinery systems Integration of Bayesian networks and evidential reasoning for multi-sensor data fusion Key contributions include: Proposing the ZIZO algorithm for Echo-State Networks optimization Advancing wear condition prediction in milling tools Creating machine learning frameworks for cyber-physical systems diagnostics His work often addresses real-world applications in manufacturing, marine engineering, and medical diagnostics.

Ezio BartocciView profile

Professor

• Cyber-Physical Systems
• Formal Methods
• Runtime Verification
• +5 more

Univ. Prof. Dr. Ezio Bartocci is a Professor at TU Wien, leading the Forschungsbereich Cyber-Physical Systems . His research focuses on formal methods, runtime verification, and probabilistic systems in Cyber-Physical Systems (CPS). He leads projects like 'Distribution Recovery for Invariant Generation of Probabilistic' and 'Trustworthy IoT for CPS'. Key interests include specification mining, probabilistic hyperproperties, and developing tools like MoonLight for spatio-temporal monitoring. Recent work explores reinforcement learning ethics, neural network verification, and adaptive testing frameworks. His contributions span conferences such as HSCC and RV, with notable publications on parameter synthesis, fault localization in CPS, and moment-based analysis of probabilistic loops. Research Interests : Cyber-Physical Systems (CPS) design and validation Formal specification and verification techniques Probabilistic systems and hyperproperties Runtime monitoring and adaptive testing Neural networks and ethical AI Labs/Teams : Active in TU Wien's Cyber-Physical Systems research unit, collaborating with industry and academia on CPS security and autonomous systems.

Cornelia FernerView profile

Lecturer

• Machine Learning
• Natural Language Processing
• Data Science
• +3 more

Cornelia Ferner serves as a Lecturer in the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences, based at Campus Urstein. Her office is located in room Urstein-430, and she can be reached via email at cornelia.ferner@fh-salzburg.ac.at or by telephone at +43-50-2211-1329. Education: Bachelor of Science (BSc) Her research expertise lies at the intersection of artificial intelligence and real-world applications, with primary focus areas including machine learning (particularly deep learning and generative models), natural language processing (topic modeling, sentiment analysis), and data science methodologies. She applies these techniques to energy systems analysis, social media mining for refugee movement tracking, and process optimization in business contexts, demonstrating strong interdisciplinary capabilities across technical and societal domains. Analysis of her publication trends (2017-2022) reveals consistent methodological innovation in neural networks and probabilistic modeling, applied to energy data (smart grids, tariff structures) and social dynamics (event detection, refugee movements). Her work bridges theoretical AI advancements with practical European industry challenges, particularly in digital transformation of energy markets and social media analytics. No scientific awards were mentioned in available sources. Information regarding graduate student supervision or specific research funding was not documented in current materials, though her multi-case studies indicate collaborative industry engagement. Her publications suggest involvement in cross-institutional energy sector projects and social impact research initiatives. Details about laboratory facilities or dedicated research teams were not available in provided documentation.

Roman KernView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Data Science
• +7 more

Assoc. Prof. Roman Kern is affiliated with the Institute of Machine Learning and Neural Computation at Graz University of Technology and serves as Chief Scientific Officer at Know-Center Research GmbH , a competence center for Big Data analytics. His research bridges natural language processing, data science, and machine learning with a focus on causal inference and discovery, applied to scientific publication mining, intelligent transportation systems, and smart production. Research Interests : Causal inference, NLP, data science, trustworthy AI, knowledge discovery, and interdisciplinary applications. Projects : VanillaFlow (sustainable electrolytes for batteries), REWAI (causal discovery in textile manufacturing), and EPN-2024-RI (planetary science infrastructure). Teaching : Courses include Knowledge Discovery, Natural Language Processing, and Introduction to Scientific Working at TU Graz. His recent publications analyze causal relationships in semiconductor manufacturing, differential privacy, and LLM applications in healthcare and environmental monitoring. Roman Kern actively mentors students through thesis topics on causality, LLMs, and data privacy frameworks, while leading applied research projects with industry partners.

Samuele TosattoView profile

Assistant Professor

• Reinforcement Learning
• Robotics
• Machine Learning
• +6 more

Samuele Tosatto is an Assistant Professor in the Computer Science Department at the University of Innsbruck and affiliated with the Digital Science Center. His research focuses on enabling robots to learn in real-world environments through reinforcement learning and abstraction techniques, addressing challenges in scalability and efficiency. Ph.D. in Computer Science from Technical University of Darmstadt (2020) M.Sc. and B.Sc. in Software Engineering from Polytechnic University of Milan His work emphasizes theoretical aspects of reinforcement learning, particularly policy gradient estimation and temporal-difference methods. Recent publications highlight innovations in data-driven teleoperation, movement primitives, and off-policy optimization, aligning with his goal of bridging simulation-to-reality gaps. He teaches courses including Introduction to Robotics, Optimization and Numerical Computation, Machine Learning, and Data Analysis II. His research is disseminated via platforms like Google Scholar and samueletosatto.com.

• Human-Centered Computing
• Digital Thinking Pedagogy
• Crowdsourcing Mechanics
• +4 more

Lisa Posch, a Research Fellow at the Institute of Human-Centred Computing at Graz University of Technology, focuses on digital thinking pedagogy, crowdsourcing mechanics, and social media analytics. Her work bridges computational methods with sociotechnical systems. Education: Dipl.-Ing. (Master of Science), Dr.techn. (Doctorate in Technical Sciences), BSc Her research spans Human-Centered Computing , Natural Language Processing , and Digital Humanities , with publications on crowdworker motivation, social media monitoring, and metadata modeling. She has contributed to advancements in probabilistic ontology mapping and multilingual topic modeling. Her recent publications (2013-2023) demonstrate expertise in social media analytics , digital labor dynamics , and semantic data systems . She actively engages in educational initiatives through the Teaching Digital Thinking Project (TDT).

Vladimir KolmogorovView profile

Professor

• Discrete Optimization
• Combinatorial Optimization
• Global Optimization
• +3 more

Vladimir Kolmogorov is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Kolmogorov Group focused on Discrete Optimization. He previously held positions as Assistant Professor at IST Austria (2011–2014), Lecturer at University College London (2005–2011), and Associate Researcher at Microsoft Research (2003–2005). His research spans algorithm development for graphical models, combinatorial optimization, and applications in computer vision. Education: M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, and Ph.D. in Computer Science from Cornell University (2003). Research interests include complexity classifications, algorithm design for discrete optimization problems (e.g., max-flow, min-cost matching), and applications in computer vision. Notable contributions include the 'Boykov-Kolmogorov' max-flow algorithm and 'Blossom V' for minimum cost perfect matching. Honors include the ERC Consolidator Grant (2014–2019), Koenderink Prize (2012), and best paper awards at CVPR and ECCV. His work on optimization algorithms and theoretical complexity has significantly impacted computer vision and combinatorial optimization fields. Advising and grants: Supervised multiple PhD students and postdocs, with current advisees including Martin Dvořák and Pavel Arkhipov. His team explores inference in graphical models, combinatorial optimization, and discrete optimization theory.