Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)
Karl Kunisch is University Professor at the Department of Mathematics and Scientific Computing, University of Graz , and simultaneously Scientific Director of the Radon Institute (RICAM) of the Austrian Academy of Sciences in Linz. A SIAM Fellow and recipient of the 2021 W.T. and Idalia Reid Prize, he leads the ERC Advanced Grant OCLOC and heads the research group “Optimization and Optimal Control”. Education: Dipl.-Ing. (1975), Dr. techn. (1978) and Habilitation (1980), Graz University of Technology Research Interests: His work centres on optimization and optimal control of partial differential equations , nonsmooth optimisation in function spaces , inverse problems and mathematical imaging , together with advanced numerical analysis . Current emphases are life-science applications , closed-loop control and machine-learning based feedback design. Publications Profile: With over 400 papers and two monographs, his recent output is dominated by high-impact studies on infinite-horizon optimal control , feedback stabilisation of semilinear parabolic and Navier–Stokes systems, risk-averse and data-driven control , and sparse control strategies . A clear trend is the fusion of rigorous PDE analysis with cutting-edge machine-learning techniques. Scientific Awards & Distinctions: W.T. and Idalia Reid Prize (2021) SIAM Fellow (2017) ERC Advanced Grant Horizon 2020 (2015) Alwin Walther Medaille (2008) ICM Invited Lecture, Hyderabad (2010) SIAM Outstanding Paper Prize (2006) Christian Doppler Laboratory Fellowship (1992) Fellowship of the Japanese Society for the Promotion of Science (1990) Max Kade Scholarship (1982/83) Fulbright Travel Grants (1979/80, 1985) Theodor-Körner-Fonds Research Award (1979) Pro Scienta Scholarship (1974–1977) Grants & Leadership: Principal Investigator, ERC Advanced Grant “ OCLOC – From Open to Closed Loop Control ” Scientific Director, Radon Institute (RICAM), Austrian Academy of Sciences Head of Research Group “Optimization and Optimal Control”, RICAM Co-Speaker, International Research Training Group IGDK Former member/consultant: MATHEON Scientific Advisory Board, Weierstrass Institute Scientific Advisory Board, Christian Doppler Forschungsgesellschaft Senate, DFG and INRIA evaluation boards Laboratory & Team: Prof. Kunisch currently leads the “Optimization and Optimal Control” group at RICAM, comprising post-docs, doctoral researchers and international visitors, focusing on interdisciplinary projects at the interface of PDE control, numerical optimisation and life sciences.
Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.
Anna Lackinger is a PreDoc Researcher and PhD student in the Distributed Systems Group at TU Wien's Faculty of Informatics. Holding a Diplom-Ingenieur (Master's) and BSc from TU Wien, she specializes in edge intelligence and distributed machine learning systems with applications in cloud infrastructure optimization. Education: Diplom-Ingenieur (Master of Science in Engineering), TU Wien, 2023 Bachelor of Science, TU Wien Her research centers on Edge Intelligence and Federated Learning architectures, developing communication-efficient orchestration frameworks for hierarchical learning systems. She pioneers time series prediction techniques for cloud workload forecasting, enabling proactive resource scaling while addressing challenges in heterogeneous IoT environments and communication-constrained distributed systems. Analysis of her publication record reveals consistent focus on adaptive federated learning systems that optimize communication costs while maintaining model accuracy. Her work bridges theoretical AI advancements with practical distributed systems implementations, particularly in edge-cloud continuum scenarios where resource constraints demand innovative orchestration solutions. Professional Service: Reviewer for IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Reviewer for International Web Information Systems Engineering Conference (WISE) Reviewer for ACM Transactions on Internet Technology (TOIT) Reviewer for IEEE Transactions on Services Computing (TSC) Reviewer for IEEE Internet Computing (IC) Reviewer for IEEE Open Journal of the Computer Society (OJCS) Anna co-advises bachelor's and master's theses in distributed systems and machine learning. She actively contributes to major research initiatives including AloTwin (2023–2025) on reactive federated learning orchestration, INTEND (2024–2026) developing adaptive inference agents, and TEADAL (2022–2025) focused on load-aware federated learning systems. As a core member of TU Wien's Distributed Systems Group, she collaborates on cutting-edge research in edge intelligence and IoT swarm coordination, advancing the RainCloud framework for decentralized heterogeneous IoT systems while contributing to the group's leadership in distributed AI infrastructure.
Lukas Radl is a University Assistant and PhD Student at the Institute of Visual Computing, Graz University of Technology, where he works on 3D Scene Representations for View Synthesis under the supervision of Markus Steinberger. His research focuses on advancing real-time rendering techniques, particularly in Neural Radiance Fields (NeRF) and Gaussian Splatting, to bridge digital and physical world representation. Education: Master of Science in Computer Science (with distinction), Graz University of Technology, 2018-2023 Bachelor of Science in Software Engineering, Graz University of Technology, 2018-2023 Research Focus: Radl's work intersects Computer Graphics, Computer Vision, Machine Learning, and Parallel Processing. He pioneers practical implementations of Radiance Field Representations, addressing critical challenges in view consistency, anti-aliasing, and real-time performance for interactive applications. His innovations enable robust rendering in virtual reality and complex lighting scenarios through novel geometric and neural approaches. Publication Impact: Recent publications (2024-2025) demonstrate a cohesive trajectory toward production-ready radiance field systems. Key advances include sorting algorithms for view consistency (StopThePop), anti-aliasing frameworks for Gaussian Splatting (AAA-Gaussians), and VR-optimized pipelines (VRSplat). These contributions establish new standards for real-time performance while maintaining visual fidelity across diverse hardware platforms. Scientific Recognition: Dean's List (top 5% of students) at Graz University of Technology (2019, 2020) Mentorship & Service: Radl actively shapes academic discourse as a reviewer for premier venues (CGF, ICCV, TVCG) and mentors students through open projects in real-time rendering. His teaching portfolio spans exercise coordination for core visual computing courses since 2020, with current leadership in Real-Time Graphics and Computer Graphics instruction. He fosters talent through student projects advancing Gaussian Splatting implementations. Research Ecosystem: Embedded in Graz University of Technology's Institute of Visual Computing, Radl collaborates within a specialized team focused on radiance field optimization. The group maintains active pipelines for NeRF and Gaussian Splatting research, with strong industry connections evidenced by his upcoming Meta Reality Labs internship. Current projects target foveated rendering, geometric consistency, and editing capabilities for next-generation AR/VR systems.
Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks
Djordje Slijepčević serves as Deputy Research Group Leader of the Media Computing Research Group at the Institute for Creative Media/Technologies, St. Pölten University of Applied Sciences. He is affiliated with the Department of Media and Digital Technologies and holds a position involving both research leadership and academic instruction. His work bridges computer science, biomedical engineering, and clinical applications. Dr. Slijepčević's research focuses on the intersection of machine learning, computer vision, and biomechanics, with particular emphasis on clinical gait analysis. His work develops explainable AI systems that can interpret human movement patterns, particularly for rehabilitation applications and medical diagnostics. His research spans from fundamental machine learning methodologies to practical clinical implementations, with special attention to transparency and interpretability of AI systems in medical contexts. His work has significant implications for personalized rehabilitation, movement disorder diagnosis, and assistive technologies. His publication record demonstrates a clear trajectory toward increasingly sophisticated applications of machine learning in biomechanics, with a growing emphasis on explainability and clinical applicability. The research shows progression from basic gait analysis techniques to sophisticated AI-driven diagnostic tools that maintain transparency in medical decision-making processes. His work increasingly focuses on individualized approaches to movement analysis, recognizing the importance of personal gait signatures in rehabilitation contexts. Dr. Slijepčević actively contributes to multiple research projects including MODEL-CP, TrustAI, EyeQTrack, FAIRAI, deepForce, and IntelliGait 3D, which collectively aim to advance AI applications in healthcare, particularly in movement analysis and rehabilitation. His collaborative approach is evident through numerous co-authored publications with researchers from diverse disciplines including medicine, computer science, and biomechanics.
Katharina Hoedt is a University Researcher and Assistant at the Institute of Computational Perception at Johannes Kepler University Linz (JKU), with a Vienna-based research presence. She holds a PhD in Computer Science from JKU (2020) and has been involved in research roles since 2016, including at the Austrian Research Institute for Artificial Intelligence (OFAI). Her work focuses on adversarial machine learning, model interpretability, and neural network robustness, particularly within music information retrieval domains. Education: PhD in Computer Science (2019–2020), DI (Diploma) in Computer Science (2013–2016), Bachelor of Science in Informatics (JKU Linz). She has taught courses on Machine Learning and Pattern Classification, and Artificial Intelligence at JKU. Research Interests: Adversarial examples and robustness, interpretable machine learning, neural network inner workings, and applications in music classification. Her publications explore adversarial attacks, explanation validity, and model defense strategies in audio and music contexts. Labs/Teams: Active member of the Institute of Computational Perception, collaborating on interdisciplinary projects combining AI with musicology and signal processing.
Peter Filzmoser is a Professor at the Institute for Statistics and Mathematical Economics (E105) of Vienna University of Technology, leading the Computational Statistics Research Area (E105-06) and affiliated with the Network Lab. His research focuses on: Compositional Data Analysis Robust Statistics Outlier Detection Machine Learning for High-Dimensional Data with applications in geochemistry, mobility data, tribology, and sustainable development. Recent publications (2023) demonstrate significant advances in explainable outlier detection using Shapley values, robust techniques for compositional data analysis, and applications in forecasting heterogeneous time series. His work extends compositional data analysis through graph signal processing and develops novel robust methodologies for real-world problems. Professor Filzmoser has advised over 15 Master's and PhD students from 2021-2023. Key research projects he leads include: Automotive Intelligence for/at Connected Shared Mobility CSTAT: Blind Source Separation Generalized relative data and Robustness in Bayes spaces
Alireza Furutanpey is a University Assistant (PreDoc Researcher) at TU Wien's Distributed Systems Group within the Institute of Information Systems Engineering, holding a Master's degree with distinction. His academic role combines research in distributed systems with teaching responsibilities for core computer science courses. His educational background includes: Bachelor of Science (BSc) Master of Science (Dipl.-Ing.) with distinction Furutanpey's research centers on Edge Computing and Edge Intelligence, specializing in Distributed Inference, Neural Data Compression, and AI-Systems integration. He pioneers techniques for neural feature compression in satellite/edge environments and develops frameworks for federated learning orchestration under communication constraints. His work bridges theoretical AI with practical system implementation, focusing on resource-constrained scenarios where bandwidth and computational efficiency are critical. Analysis of his 15+ publications reveals dominant trends in neural compression for distributed systems (60%), federated learning optimization (25%), and serverless edge frameworks (15%). Key contributions include solving satellite downlink bottlenecks through feature compression and enabling adaptive inference in heterogeneous edge networks, with methodologies increasingly incorporating generative modeling and robustness against adversarial attacks. Scientific Awards: None documented in available sources. He actively supervises master's theses, guiding students on adversarial machine learning, neural compression, and image retrieval systems. His research is supported through major projects: AloTwin (2023-2025) focusing on edge intelligence, INTEND (2024-2026) on industrial IoT, and TEADAL (2022-2025) on federated learning. He serves as a reviewer for 15+ IEEE/ACM venues including IEEE Transactions on Mobile Computing and ICDCS. Furutanpey operates within TU Wien's Distributed Systems Group, which specializes in edge-cloud continuum research. The team develops tools like faas-sim for serverless edge simulation and explores quantum-classical hybrid architectures, maintaining strong industry collaborations in industrial IoT and satellite communications.
Zhang Jun is a Full Professor of Physics and Mathematics and Co-director of the Applied Math Lab at the Courant Institute, New York University (NYU), USA. He also serves as Co-director of the NYU-ECNU Joint Physics Research Institute in Shanghai, China, and holds an Affiliated Professorship at NYU Shanghai. His research focuses on experimental fluid physics, particularly fluid-structure interactions in biological and geophysical contexts, including bio-locomotion, flapping wings, and continental dynamics. Zhang has authored over 290 invited talks and peer-reviewed papers in journals like Nature and Physical Review Letters. He received the 2017 APS Fellow award for pioneering work in fluid-structure interactions. Beyond academia, he is a freelance illustrator with plans to publish a book of sketches. Education: PhD in Physics (1994), Niels Bohr Institute, University of Copenhagen PhD Candidate (1990-1991), Hebrew University of Jerusalem BSc in Physics (1985), Wuhan University Research Interests: Zhang’s work bridges physics, biology, and geophysics, exploring phenomena like flapping wing aerodynamics, animal locomotion, and Earth’s core-mantle interactions. His experiments often use novel fluid dynamics setups to model natural systems. Awards: APS Fellow (2017) Milton Van Dyke Award (2014) Antarctica Service Medal (2015) Labs & Teams: Co-directs the Courant Institute’s Applied Math Lab, specializing in fluid dynamics experiments. Collaborates with institutions globally, including NYU Shanghai and Aix-Marseille University.
Dipl.-Ing. Hanna Zeitfogel is a researcher at the Institute of Hydrology and Water Management, part of the Department of Landscape, Water and Infrastructure at the University of Natural Resources and Life Sciences, Vienna (BOKU). Her work focuses on hydrological systems, groundwater recharge, and soil-water interactions in the Austrian context. Her primary research interests include hydrology, groundwater recharge assessment, water resource management, soil physics, and the application of machine learning techniques to environmental data analysis. Her work demonstrates a strong emphasis on regional water assessment and hydrological modeling at various spatial scales. Her publication record shows a consistent focus on Austrian water systems, with particular attention to groundwater recharge modeling, water balance components, and soil hydraulic properties. Her recent work increasingly incorporates machine learning approaches for predicting soil properties and understanding regional hydrological patterns. She has been actively involved in significant research projects including 'Austrian wide infiltration capacities' (2020-2021) and 'Variability of Groundwater Recharge and its Implication for Sustainable Land Use in Austria' (2019-2023), collaborating with Professor Karsten Schulz and other researchers. Dr. Zeitfogel regularly presents her research at major international conferences, particularly the European Geosciences Union General Assembly, demonstrating her active participation in the global hydrology research community.
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:
Andreas Steininger is an Associate Professor in the Department of Embedded Computing Systems at TU Wien. His primary research focuses on fault-tolerant computing, asynchronous logic, and dependable systems. He leads the Embedded Computing Systems group and serves as Director of the Curriculum Commission for Computer Engineering. His work emphasizes resilient hardware architectures, radiation effects in microelectronics, and timing domain interfacing. He has contributed to numerous projects with industry partners like Intel and TTTech Auto AG, addressing challenges in trustworthy autonomous systems and robust distributed algorithms. Key research interests include asynchronous circuits, clockless processors, and error detection mechanisms. He has over 50 publications in top-tier conferences and journals, including IEEE Transactions and ASYNC. Notable contributions include methodologies for mitigating single-event transients in QDI logic and fault-tolerant clock generation schemes. He teaches advanced courses in digital design, computer engineering, and scientific research methods. His projects span radiation-hardened ASIC design, metastability analysis in FPGAs, and secure IoT architectures. He actively collaborates with automotive and aerospace industries to develop solutions for embedded systems reliability. Recent work explores fault resilience in neural networks and energy-efficient asynchronous microprocessors.
Pascal Schöttle is a Professor for IT Security and Machine Learning at the Department of Digital Business & Software Engineering at MCI - The Entrepreneurial University in Innsbruck. He previously held roles including Associate Professor (2020-2022), University Lecturer (2018-2020), and Post-Doc positions at the University of Innsbruck and University of Münster. His academic journey includes a PhD in Computer Science from the University of Münster (2014), and prior studies in IT Security and Mathematical Engineering. Research focuses on adversarial machine learning, IoT security, blockchain applications, and game-theoretic approaches to cybersecurity. Notable projects include leading the Josef Ressel Center for Security Analysis of IoT Devices (2023-present) and managing the SMiLE project on encrypted machine learning (2021-2023). Awards include the IEEE Best Student Paper (2012) and CAST IT Security Runner-Up (2008). Education: PhD (Computer Science, 2014), MSc (IT Security, 2011), BSc (Mathematical Engineering, 2008) Teaching: Courses include IT Security, Machine Learning, Algorithms & Data Structures, and Blockchain Grants: FFG (2021-2023), FWF (2019-2023), University of Innsbruck (2017-2019) Labs/Teams: Research Center CDG, Josef Ressel IoT Security Group
