Prof. Silvia Miksch is a full professor in the Department of Visual Analytics at TU Wien. Her research focuses on visual analytics, time-oriented data visualization, and human-computer interaction. She leads projects in cultural heritage analysis, fraud detection, and pandemic data visualization. Her work bridges computer science with digital humanities, emphasizing user-centric design and uncertainty modeling. Key contributions include guidance systems for VA environments and network visualization frameworks for art history. Affiliations: TU Wien (since 2000+) Research Labs: Network Lab, Visual Analytics Research Group Recent projects explore temporal patterns in artist exhibitions, parameter space exploration, and pandemic data communication. She has advised over 20 PhD/Master’s students, many contributing to VA systems like COVIS and NEVA. Awarded the VGTC Visualization Technical Achievement Award (2024) for foundational work in temporal visualization. Active in IEEE VAST and serves on journal editorial boards. Her labs develop open-source tools for interactive data exploration.
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.
Patrick Weinberger is a Researcher at the University of Applied Sciences Upper Austria, affiliated with the School of Engineering and the Focal Area ICT - Information & Communications Technology. He leads and collaborates on multiple research initiatives including the VISION project on AI-driven non-destructive testing and HyperMAT for hyperspectral material characterization. His research focuses on advancing X-ray computed tomography analysis through deep learning and augmented reality visualization. Key areas include U-Net architectures for industrial inspection, explainable AI in non-destructive testing, and immersive material analysis systems. His work bridges computer vision with practical industrial applications in aerospace and automotive sectors. Weinberger actively contributes to 4 major research projects including VISION (2025-2028) developing AI systems for non-destructive testing, PEMOWE for energy transition testing networks, HyperMAT for hyperspectral material characterization, and WIFI for future welding industry systems. He operates within the Assistive Technology Lab and Center of Excellence Automotive/Mobility at the Wels campus. Research Output Highlights: 13 total publications (5 articles, 3 conference papers, 2 contributions) Key innovations in U-Net interpretability and AR-based CT visualization Strong focus on industrial AI transfer to manufacturing Technical Collaborations: Primary collaborator with Senck, Kastner, and Fröhler Network spans 6 similar research profiles in deep learning and CT analysis Active in EU Interreg Bavaria-Austria programs
Silvia Miksch is a Full Professor of Visual Analytics at the Vienna University of Technology (TU Wien), leading the Research Unit Visual Analytics (E193-07) and coordinating the Research Focus on Visual Computing and Human-Centered Technology. She holds a PhD from TU Wien and has held academic roles at institutions like Stanford University (FWF postdoc), Danube University Krems (2006–2010 as University Professor), and TU Wien. Her research focuses on visualization, visual analytics, interaction design, and temporal data analysis with applications in medical informatics, process engineering, and cultural heritage. Education: Master of Social and Economic Science (University of Vienna, 1987), PhD (TU Wien, 1990). Former roles include Chair of the Austrian Society for Artificial Intelligence (ÖGAI) and leadership in EU projects like VALCRI and KAVA-Time. She has authored over 200 publications and received awards such as the IEEE VGTC Visualization Technical Award (2023) and induction into the IEEE VGTC Visualization Academy (2020). Research interests include knowledge-assisted visual analytics, task-driven guidance systems, and spatiotemporal data exploration. She oversees the Laura Bassi Centre of Expertise 'CVAST' and advises numerous PhD and master’s students. Her work bridges theory and practice, with notable projects like the Marvel Cinematic Universe infographic (GD 2019 Best Creative Challenge) and Game of Thrones character networks (GD 2018 Third Prize). Key Awards: Best Paper Award at vis4dh 2019, IEEE VGTC Technical Award 2023 Editorial Roles: Associate Editor of Transactions on Visualization and Computer Graphics (2011–2015), Editorial Board of Journal of Biomedical Informatics (2012–2020) Leadership: Chair of EuroVis Steering Committee (2023–2027), Member of VIS Executive Committee (2015–2020)
Dipl.-Ing. Dr. Rupert Tscheließnig is a researcher at the Institute of Bioprocess Science and Engineering , part of the Department of Biotechnology and Food Science at the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on bioprocess engineering, structural biology, and molecular simulations, particularly in the context of protein purification, bioseparations, and nanoscale phenomena. Key Research Areas Protein A and hydrophobic interaction chromatography Structural analysis using Small Angle X-ray Scattering (SAXS) Molecular simulations of adsorption and interfacial phenomena Enzyme conformational dynamics Nanoscale material design for bioseparations Selected Projects Lead project on Precisely Patterned Nanofibers for High-Performance Bioseparations (2020–2025, EU-funded) Subproject leader in the FWF-funded Doktoratskolleg Biomolecular Technology of Proteins (2019–2022) Contributed to multiple EC-funded initiatives on protein purification and bioprocess optimization Advising Advisor for Valentina Ruocco's 2024 doctoral thesis on N-glycans in IgA2 and SARS-CoV-2 RBD Advisor for Bohuslav Motycka's 2023 work on Cellobiose Dehydrogenase Dynamics His research combines experimental techniques (SAXS, X-ray scattering) with computational methods to investigate protein-ligand interactions, adsorption mechanisms, and nanoscale structural changes. Publications span journals like Journal of Chromatography A , FEBS Journal , and Biotechnology and Bioengineering , with a focus on translating molecular insights into industrial applications.
Lukas Schrangl is a Researcher at the Institute of Biophysics within the Department of Natural Sciences and Sustainable Resources at the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on biophysical techniques to study molecular interactions, particularly in immunological contexts. His educational background includes a doctoral degree, with his thesis "Quantifying conformational dynamics of biomolecules via single-molecule FRET" completed in 2020. His research spans experimental physics, light optical microscopy, and biophysics, with strong emphasis on software development and data science applications in biophysical research. Schrangl's research interests center on single-molecule FRET techniques to investigate T-cell receptor-ligand interactions and molecular force measurements . His work bridges biophysics, immunology, and computational methods, developing innovative approaches to quantify receptor-ligand interaction times and forces at the molecular level. He has created several open-source software tools including the sdt-python package and fret-tester to advance analysis capabilities in single-molecule biophysics. His publication record shows consistent high-impact output from 2017 through 2025, with recent work focusing on synaptic force shielding in T cell receptor interactions and advanced quantification of receptor-ligand interaction lifetimes. His research demonstrates strong interdisciplinary connections between physics, biology, and computational science. Schrangl actively presents his work at major conferences, including the 69th Annual Meeting of the Biophysical Society (2025) and the Biophysics Austria Conference (2024), demonstrating ongoing engagement with the international scientific community. His technical expertise spans both experimental biophysics and computational methods, making significant contributions to the development of quantitative approaches for studying molecular interactions in immunological contexts.
Christian Macho is a Researcher affiliated with the Institute of Computer Science Systems at Alpen-Adria-Universität Klagenfurt. He is a Postdoc Assistant and holds roles in university governance as a Member of the Senate and Member of the Works Council for Academic University Staff, demonstrating his involvement in institutional leadership. His research focuses on software engineering challenges, particularly in build systems, API design, dependency management, and empirical studies of developer practices. His work spans topics such as log parsing, automated API change detection, and spreadsheet usability. He contributes to open-source tools like BuildDiff and AutoGuard, addressing real-world software maintenance issues. His publications highlight a strong empirical approach, analyzing data from platforms like Travis CI and Stack Overflow to understand developer workflows and system behaviors. Macho's research interests are reflected in his active participation in academic communities and his commitment to practical solutions for software engineering problems. His office is located in the Main Building at S.2.70, and he maintains a personal website at mitschi.github.io .
Priv.-Doz. Dr. Richard Kowar is a Postdoctoral Researcher at the Department of Mathematics, University of Innsbruck. His research focuses on inverse problems, wave propagation, and mathematical modeling in medical imaging and physics. He specializes in photoacoustic tomography, regularization methods for ill-posed equations, and the analysis of causal wave equations. His work bridges theoretical mathematics with practical applications in biomedical imaging and signal processing. Key research areas include the analysis of dissipative systems, fractional calculus in wave equations, and iterative algorithms for image reconstruction. His studies on causality in wave equations and diffusion models have advanced the understanding of nonlocal operators and their applications. Kowar has contributed to projects like VASCAGE, integrating mathematical techniques with real-world engineering and medical challenges. Publications highlight advancements in photoacoustic imaging with attenuating media, numerical inversion techniques, and theoretical frameworks for causal dynamics. Despite no listed scientific awards, his extensive bibliography demonstrates impactful contributions to applied mathematics and engineering.
Paul Werginz is a Research Fellow at the Institute of Biomedical Electronics (E363) at TU Wien (Vienna University of Technology), holding a Diplom-Ingenieur (Dipl.-Ing.) and Doctor of Technical Sciences (Dr.techn.) degree. His work focuses on computational modeling of neural stimulation for sensory prostheses, with primary affiliations in neural engineering and biomedical electronics research groups. Dr. Werginz's research centers on biophysical mechanisms of neural stimulation in retinal and auditory systems. He investigates axon initial segment geometry, biophysical diversity in retinal ganglion cells, and electrode placement optimization for cochlear implants. His computational approaches employ finite element modeling and multiphysics simulations to analyze neural responses to electrical stimulation, aiming to enhance the efficacy of visual and auditory prostheses through precise stimulation strategies. Analysis of his 2022-2023 publications reveals dominant trends in retinal prosthesis optimization (70% of works) and cochlear implant modeling (30%). Key thematic clusters include axon initial segment biophysics, network-mediated visual processing, and high-frequency stimulation effects across neural cell types. His research consistently applies computational neuroscience to solve engineering challenges in sensory restoration, with strong emphasis on translating biophysical principles into clinical applications for neural prosthetics. Dr. Werginz has not received any scientific awards mentioned in the available documentation. He serves as Principal Investigator for two Austrian Science Fund (FWF) projects: 'Biophysikalische Diversität in retinalen Ganglienzellen' (Biophysical Diversity in Retinal Ganglion Cells) and 'Identification of calcium current reversal in bipolar cells'. His supervisory contributions include master's theses by Dizdar, Fatima (2023) on retinal ganglion cell dynamic range and Bucek, Fred (2023) on cochlear implant simulations, indicating active mentorship in computational neuroscience. As a core member of TU Wien's Network Lab within the Institute of Biomedical Electronics, Dr. Werginz collaborates extensively with Prof. Günther Zeck's research group and international partners including Dr. Shelley Fried (Harvard) and Prof. Daniel Palanker (Stanford). His team specializes in computational modeling of neural interfaces, with current work focusing on optimizing stimulation parameters for next-generation retinal and cochlear implants through detailed biophysical simulations.
Stephan Michael Weiss is a Professor at the University of Klagenfurt, affiliated with the Institute for Intelligent System Technologies, located at the Lakeside Park campus. His work focuses on advanced signal and image processing within intelligent systems, with strong ties to robotics and control engineering. His research interests span Signal Processing , Image Processing , Geometric Image Processing , Control Engineering , Robotics , and Computer Simulation . These areas reflect a multidisciplinary approach to intelligent system design and analysis, particularly in modeling and processing complex dynamic signals and visual data. The trends in his research, as indicated by his stated priorities, emphasize algorithmic development in signal and image processing, geometric modeling of visual data, and simulation-based control systems. These align closely with applications in robotics and autonomous systems. There are no scientific awards mentioned in the provided text. There is no information available about student advising, grants, or funding activities. He is affiliated with the Institute for Intelligent System Technologies at the University of Klagenfurt, a research unit focused on the development and application of intelligent signal processing and system modeling techniques.
Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University from July 2019 to August 2022, and a Postdoctoral Fellow at the University of Waterloo from October 2017 to June 2019. Faculty of Computer Science, Dalhousie University (2022–Present) School of Computer Science and Technology, Algoma University (2019–2022) Department of Electrical and Computer Engineering, University of Waterloo (Postdoc, 2017–2019) Education: Ph.D., Electrical and Computer Engineering, University of Waterloo M.E., Harbin Institute of Technology, Shenzhen B.E., Lanzhou Jiaotong University Dr. Tang’s research centers on intelligent networking and computing technologies, with applications in B5G/6G networks, Internet of Vehicles (IoV), edge computing, and UAV-assisted systems. She leverages machine learning and AI to optimize resource management, spectrum allocation, and network performance in heterogeneous and dynamic environments. Her work bridges theoretical design with practical implementation in next-generation communication systems. The recent publications highlight a consistent focus on AI-driven solutions for wireless networks, particularly in vehicular communications, edge intelligence, and spectrum efficiency. Key themes include reinforcement learning for UAV deployment, deep learning for spectrum sensing, and cooperative edge caching in 5G/mmWave networks. These works span top journals such as IEEE TWC, TVT, IoT Journal, and JSAC, reflecting strong technical depth and real-world applicability. Scientific Awards and Honors: NSERC Discovery Grant (2021–2026) Faculty Research Startup Funds from Dalhousie and Algoma Universities Best Speaker Award, University of Waterloo (2017) Multiple awards including FoE Award, Graduate Scholarship, and Entrance Awards from University of Waterloo (2011–2017) Dr. Tang actively contributes to the academic community as a reviewer for leading IEEE journals and conferences, and has served on technical program committees for IEEE INFOCOM, GLOBECOM, ICC, and VTC. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society. She is currently advising and recruiting MSc and PhD students for research in B5G/6G, IoV, and AI-empowered edge computing. Laboratory and Research Group: Dr. Tang leads a research group focused on intelligent networking and edge AI systems at Dalhousie University. Her lab investigates real-time decision-making, resource slicing, and autonomous vehicular networks, often integrating simulation with practical deployment considerations.
Elisabeth Quendler serves as Associate Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), within the Department of Agricultural Sciences and the Institute of Agricultural Engineering. Her academic career spans over three decades, with significant contributions to agricultural work science, ergonomics, and social inclusion within farming systems. Her educational background includes a First Degree in Agriculture (DI) in 1991, an MSc in Agricultural and Food Chain Systems in 1994, a controlling diploma in 2000, and a doctorate in 2002. She achieved her Privatdozent (PD) qualification in 2010 and was appointed Associate Professor in 2012. Professor Quendler's research focuses on optimizing work processes in agricultural operations from labor management, ergonomic, and social perspectives. Her work addresses critical issues including Linear Programming for farms, Organic Farming, Ergonomics, and Energy efficient and climate neutral construction. She has pioneered research on social inclusion of people with disabilities in agricultural settings, particularly in urban and peri-urban horticulture. Her recent publications reveal a strong emphasis on social-ecological models for inclusion processes, smart technologies in pasture management, and labor optimization in various farming systems. The research demonstrates a consistent trajectory toward integrating technological solutions with social sustainability considerations in agricultural work environments. High Merit Award in Recognition of distinguished services to CIGR (2018) Dr. -Karl-Schleinzer-Preis (1991) Professor Quendler has supervised numerous master's and doctoral theses on topics ranging from forest and nature preschool organization to seasonal labor shortages in fruit farming. Her grant portfolio includes significant projects funded by the European Commission, Austrian Research Promotion Agency, and Federal Ministries, with recent work focusing on socially sustainable frameworks for imparting natural knowledge to preschool children and inclusion of disabled people in horticultural farming processes. She leads research teams exploring the intersection of agricultural engineering, social inclusion, and sustainable work practices, with particular emphasis on creating accessible agricultural environments and developing models for integrating diverse populations into farming systems.
Astrid Dürauer is a Privatdozent (Associate Professor) at the Institute of Bioprocess Science and Engineering within the University of Natural Resources and Life Sciences, Vienna (BOKU) . She holds leadership roles including Head of the Christian Doppler Laboratory for Knowledge-based Production of Gene Therapy Vectors and Deputy Head of the Department of Biotechnology . Her career spans over two decades in bioprocess development, with a focus on downstream processing, protein purification, and data-driven process modeling. PhD in Biotechnology (2001, BOKU Vienna) Habilitation in Bioprocess Engineering (2019, BOKU Vienna) Astrid's research centers on bioprocess optimization , particularly for gene therapy vectors and recombinant proteins . She pioneers the integration of machine learning and statistical models to enhance real-time monitoring and predictive analytics in downstream operations. Key projects include automation of process development, membrane fouling mitigation, and scalable chromatography systems. Her 15 most recent publications (2022–2025) highlight trends in explainable AI for biopharmaceutical processes, AAV vector production , HEK293 cell characterization , and continuous downstream processing . These works bridge biotechnology , data science , and industrial scalability . Scientific recognition includes: Head of Christian Doppler Laboratory (2023) Habilitation in Bioprocess Engineering (2019) She has led 7 funded projects (2014–2029) and served as Deputy Scientific Head for the BOKU Core Facility Biomolecular & Cellular Analysis (2020). Her work involves collaborations across statistics , molecular biotechnology , and industrial partners .
Susanne Haschemi is a researcher at the Institute of Agricultural Engineering within the University of Natural Resources and Life Sciences, Vienna (BOKU) . Her work focuses on renewable energy systems, particularly biogas production from agricultural and food residues, with an emphasis on process stability, feedstock optimization, and sustainable resource management. Research Themes : Biogas production, energy efficiency in agriculture, digitalization in agricultural engineering, climate change mitigation through renewable energy, and lifecycle assessment of agro-municipal residues. Her publications highlight advancements in pretreatment technologies for maize stover, flexible biogas production strategies, and the environmental impacts of managed grasslands in Alpine regions. She collaborates with institutions like the Institute of Chemical and Energy Engineering and Institute of Geomatics on projects funded by the European Commission and Austrian Research Promotion Agency (FFG) . Notable projects include "Digitalisation and Innovation Laboratory in Agricultural Sciences" and "Energy Efficiency in Agriculture."
Monika Cserjan is a leading senior scientist at the Institute of Bioprocess Science and Engineering , part of the University of Natural Resources and Life Sciences, Vienna , with over two decades of expertise in bioprocess engineering and recombinant protein production in Escherichia coli . Her career spans roles as Project Leader at the Austrian Centre of Industrial Biotechnology (ACIB) and leadership in the CD Laboratory for biopharmaceutical production. Research Focus: Upstream process development for recombinant peptides/proteins and plasmid DNA, fusion protein technology in E. coli , advanced process characterization, and non-canonical amino acid incorporation for improved biopharmaceuticals. Publications: Over 40 contributions (2012–2024) covering bioprocess optimization, extracellular peptide production, and CRISPRactivation tools for gene regulation in E. coli . Collaborations: Frequent co-authorship with experts in bioprocess engineering, such as Gerald Striedner and Rainer Hahn, across institutions in Austria, Germany, and the U.S.
