جست‌وجوی استاد و پژوهشگر

• Bioprocess Engineering
• Gene Therapy Vectors
• Protein Production
• +۴ مورد دیگر

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 .

• Statistics
• Applied Mathematics
• Environmental Science
• +۵ مورد دیگر

Bernhard Spangl is a Lecturer at the Institute for Applied Statistics and Computing , School of Spatial Planning, Landscape and Infrastructure, University of Natural Resources and Life Sciences, Vienna. With a doctorate in Technical Sciences (2008) and a degree in Technical Mathematics (2003), his work bridges statistical methodology with applications in environmental science, agriculture, and bioprocess engineering. Current research focuses on robust statistical methods for environmental data, ecotoxicology (glyphosate impacts on soil organisms), and agricultural pest dynamics under climate change. His projects include Robust Risk Estimation (2011–2016), Fusarium Contamination in Garlic (2018–2023), and Debris Flow Event Modeling (2015–2025). Publication Trends span environmental monitoring, agricultural pest behavior, and statistical modeling. Over 100 publications and 61 lectures globally (USA, Europe, Asia) highlight methodological innovations and interdisciplinary applications. Supervised Theses include Katharina Stallberger (2025), Fabian Pelzmann (2024), and projects on bioprocess control, biostatistics, and ecological resilience.

Uwe Schmockمشاهده پروفایل

استاد

• financial mathematics
• actuarial mathematics
• insurance mathematics
• +۷ مورد دیگر

Uwe Schmock is a Full Professor at the Vienna University of Technology , leading research in Financial and Actuarial Mathematics within the Institute for Statistics and Mathematical Methods in Economics. His work bridges theoretical probability with practical financial and insurance risk modeling. Research interests include large deviations theory , risk aggregation , credit risk models , and stochastic integration . He has contributed to insurance mathematics through catastrophe bond analysis (e.g., WinCAT coupons) and annuity valuation tables. His recent publications focus on U-empirical measures , Panjer's recursion , and exotic options under market constraints. Scientific awards include the Charles A. Hachemeister Prize and the David Garrick Halmstad Memorial Prize . He actively collaborates with institutions like ETH Zurich and the American Casualty Actuarial Society.

• Automated Theorem Proving
• Computer Algebra
• Symbolic Computation
• +۳ مورد دیگر

Wolfgang Windsteiger is an Associate Professor at the Research Institute for Symbolic Computation (RISC) of Johannes Kepler University (JKU), Linz, Austria. His primary research focuses on automated theorem proving, computer algebra systems, and symbolic computation, particularly within the Theorema project. He leads the redesign and implementation of Theorema 2.0, an open-source system for mathematical theory exploration. Windsteiger is also active in integrating computational tools into mathematics education, co-authoring textbooks on algorithmic methods for university students. His work spans formal methods, mathematical software development, and educational technology. Positions: Associate Professor at RISC/JKU, Trustee of Calculemus, Conference Chair of multiple events including Calculemus'2007 and MKM'2007. Contributions: Developer of Theorema 2.0, co-author of Springer's 'Algorithmische Methoden' series, and creator of educational software tools for mathematics. Research Interests: Theorema system development, automated reasoning, formal methods in economics, and computational pedagogy. He actively promotes the use of theorem provers in university and school-level mathematics education.

• Control Theory
• Robust Control
• Nonlinear Control Systems
• +۳ مورد دیگر

Richard Seeber is a Researcher at the Institute of Control and Automation (Institut für Regelungstechnik und Automation) at TU Graz. His academic titles include Privatdozent (Priv.-Doz.), Diplom-Ingenieur (Dipl.-Ing.), Doctor of Engineering (Dr.techn.), and Bachelor of Science (BSc). His work focuses on advanced control theory, nonlinear systems, and observer design. He holds a teaching authorization in system dynamics and control theory, reflecting his dual role in research and education. Research interests include robust exact differentiators, sliding mode control, observer design for linear and nonlinear systems, and applications in thermal management and energy systems. He has contributed to methodologies addressing actuator saturation, discretization challenges, and real-time control implementation. His recent work emphasizes predefined-time convergence algorithms and stability analysis under bounded noise or disturbances. Selected projects involve heater modeling for fuel cell stacks, frequency converter nonlinearity compensation, and control strategies for biomass furnaces. His contributions are often applied to industrial systems requiring precise control under uncertainty, such as gas engine power plants and transient operating conditions. He collaborates within TU Graz's Institute of Control and Automation, contributing to both theoretical advancements and practical tool development. His research portal (https://irt.tugraz.at/) hosts publications and tools related to robust control methodologies.

Daniel Jodlbauerمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Numerical Analysis
• Biomedical Imaging
• +۴ مورد دیگر

Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.

Peter Kritzerمشاهده پروفایل

پژوهشگر

• High-dimensional algorithms
• Information-based complexity
• Quasi-Monte Carlo methods
• +۳ مورد دیگر

Peter Kritzer is a Senior Scientist and Scientific Coordinator at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences in Linz, Austria. He holds a Master's degree (2003) and a Doctoral degree (2005, 'Sub Auspiciis Praesidentis') from the University of Salzburg, followed by a Habilitation in Mathematics (2012) from Johannes Kepler University Linz. His research focuses on high-dimensional algorithms, quasi-Monte Carlo methods, discrepancy theory, and information-based complexity. Affiliations: RICAM, Austrian Academy of Sciences (since 2015); previously at University of Salzburg, UNSW Australia, and Johannes Kepler University Linz. His work emphasizes numerical integration, tractability analysis, and algorithm design for high-dimensional problems. Notable contributions include advancements in lattice rules, digital nets, and hybrid function spaces. He has received awards such as the Information-Based Complexity Young Researcher Award (2011) and the Kardinal Innitzer Förderungspreis (2013). His recent articles explore topics like reduced digital nets, QMC matrix-vector products, and tractability in Hilbert spaces. He actively contributes to Monte Carlo and quasi-Monte Carlo research communities, editing volumes like Monte Carlo and Quasi-Monte Carlo Methods 2022 .

• Bayesian Statistics
• Mixture Models
• Computational Statistics
• +۳ مورد دیگر

Gertraud Malsiner-Walli is an Associate Professor at the Institute of Statistics and Mathematics at WU Vienna University of Business and Economics. Her research focuses on Bayesian mixture models , model-based clustering , and computational statistics , with applications in medical imaging, neurology, and psychiatry. She has contributed to advancements in sparse finite Gaussian mixtures and telescoping sampling algorithms , enabling efficient Bayesian inference in mixture models. Her work bridges theoretical developments and practical applications, addressing challenges in identifying data clusters and handling high-dimensional datasets. Notable contributions include methodologies for effect fusion in regression models and multivariate regression analysis of mixed-type panel data. She has published in top journals such as Statistics and Computing , Advances in Data Analysis and Classification , and Bayesian Analysis . Her research also extends to medical applications, including studies on Alzheimer’s disease biomarkers and glioma diagnosis using combined imaging techniques. She has received funding from the Austrian Science Fund (FWF) and WU Projects grants. Collaborations with experts like Sylvia Frühwirth-Schnatter and Bettina Grün highlight her active role in advancing Bayesian statistical methodologies globally.

• Business Model Innovation
• Production Planning
• Supply Chain Management
• +۳ مورد دیگر

Shailesh Tripathi is a Researcher at the University of Applied Sciences Steyr within the Department of Production and Operations Management, actively contributing to the Steyr Center of Excellence for Smart Production and Josef Ressel Centre for Data-Driven Business Model Innovation. His research focuses on business model innovation through data-driven methodologies, production planning optimization, AI-integrated manufacturing systems, and supply chain analytics. Key methodologies include advanced machine learning applications, visualization tools for decision-making, and business model experimentation frameworks. Dr. Tripathi's recent work demonstrates strong interdisciplinary convergence between computer science and operations management, with publications emphasizing practical implementations in industrial settings. Research trends show increasing focus on sustainable production systems and customer-centric digital transformation. He has received the Innovation Management Best Paper Award (2023) for contributions to business model innovation frameworks. Additional recognitions include multiple Scopus-indexed publications with significant industry impact. As Co-Investigator, he secured major funding including the €1.2M Josef Ressel Centre for Data-Driven Business Model Innovation (2023-2027) and the Advanced Analytics for Production project (2017-2019). Current grants focus on AI-driven production optimization and sustainable supply chain modeling. He leads research teams at the Steyr Center of Excellence for Smart Production, collaborating with industrial partners like Siemens and Bosch on real-time production analytics and digital twin implementations.

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +۴ مورد دیگر

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).

• Algebra
• Enumerative Combinatorics
• Computer Algebra
• +۴ مورد دیگر

Manfred Buchacher is a researcher at the Institute for Algebra within the Research Institute for Symbolic Computation at Johannes Kepler University Linz (JKU). Holding a Dr. and MSc degree, he focuses on advanced algebraic computation problems with significant contributions to variable separation in polynomial ideals and algebraic curves. His research interests span Algebra , Enumerative Combinatorics , Computer Algebra , Mathematical Logic , and Algebraic Dynamics . Buchacher's work bridges theoretical mathematics with computational applications, particularly in symbolic computation methods for solving complex polynomial systems and differential equations. Buchacher's recent publications (2022-2024) demonstrate a consistent focus on variable separation techniques across multivariate and bivariate polynomial ideals, with applications to plane algebraic curves and higher-order equations. His work shows strong integration of combinatorial methods with algebraic computation, particularly evident in his development of the orbit-sum method. He currently leads the active research project Problems of Separation in Enumerative Combinatorics (2024-2026) and has supervised at least one research project. Buchacher actively participates in the academic community through conference organization and presentations, with upcoming engagements scheduled through early 2025 including the Conference on Enumerative combinatorics and effective aspects of differential equations (February 2025). His institutional affiliations include: Institute for Algebra, JKU Research Institute for Symbolic Computation, JKU

• Machine Learning
• Image Processing
• Medical Imaging
• +۳ مورد دیگر

Gerald Adam Zwettler is a researcher at the University of Applied Sciences Upper Austria (FH Hagenberg) with a focus on digital transformation in Information and Communication Technology (ICT). His work spans machine learning applications in non-destructive testing, human-robot interaction, and sensor data classification. Active in projects like FLARE (Human-Centered AI for NDT), MARIE (Mobile Robotic Assistance), and MOVE (Orthosis Modeling) Collaborates internationally on railway infrastructure analysis and medical imaging Research interests include deep learning , medical image processing , IoT sensor analysis , and AI-driven customization of orthopedic devices . Recent work explores edge computing for low-energy IoT systems and multimodal interaction frameworks for office robots. Publications emphasize applied AI in technical domains, with methodological contributions to presegmentation techniques , Levenshtein distance optimization , and human-centered robotics . Project roles include principal investigator in knowledge databases for industrial plastic manufacturing.

Thomas Ruschمشاهده پروفایل

دانشیار

• Computational Statistics
• Psychometrics
• Exploratory Data Analysis
• +۶ مورد دیگر

PD Dr. Thomas Rusch serves as Deputy Head of the Competence Center for Empirical Research Methods at the Vienna University of Economics and Business (WU Vienna), where he provides statistical consultation and supports faculty and graduate students in applying appropriate statistical methodologies. He has also taught at Harvard University (2019-2021) and FH Technikum, delivering courses in applied statistics, data analysis, computational statistics, and related fields. His educational background includes: Habilitation ("Venia Docendi") in Statistics, Vienna University of Economics and Business (2021) Doctoral studies in Social and Economic Sciences, majoring in Statistics, Vienna University of Economics and Business (2012) Master's degree in Statistics, University of Vienna (2010) Graduated with a degree in Psychology, University of Vienna (2008) Bachelor's degree in Statistics, University of Vienna (2007) Dr. Rusch's research focuses on improving various aspects of modern data analysis, with particular emphasis on discrete data analysis, data mining and statistical learning, exploratory data analysis and visualization, multivariate statistics, natural language processing, and psychometrics. His work bridges statistical methodology with applications in social and behavioral sciences, especially business research and youth mental health. He is particularly known for his contributions to multidimensional scaling, clustering algorithms, and psychometric modeling. His recent publications demonstrate a strong trend toward developing and applying advanced statistical methods to solve real-world problems. His work spans computational statistics, with a focus on R programming implementations, categorical data analysis, and applications in clinical psychology and business analytics. Many of his recent papers address challenges in data visualization, model stability, and the application of machine learning techniques to social science data. His scientific achievements have been recognized with multiple WU Awards for Outstanding Research Achievements (2017, 2018, 2019, 2021, 2022). As a statistical consultant, Dr. Rusch has advised numerous faculty members and graduate students on research methodology and data analysis. He has also been active in developing statistical software, particularly R packages for data analysis and visualization. His collaborative work extends to international projects, including research on youth mental health interventions in Kenya. He is actively involved in organizing academic events, such as the Vienna Workshop on Data and Model Visualisation, and serves as a reviewer for prominent journals in computational statistics.

Albert Shiryaevمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Stochastic Processes
• +۳ مورد دیگر

Albert Shiryaev is a leading Russian mathematician currently serving as Professor and Head of the Probability Theory Department at the Moscow State University , Faculty of Mechanics and Mathematics. He has been affiliated with the Steklov Mathematical Institute since 1957 and was a member of the Russian Academy of Sciences (Correspondent member since 1997). Research Interests: A pioneer in probability theory and mathematical statistics , his work focuses on stochastic processes , optimal stopping , sequential analysis , and financial mathematics . His publications span over 170 scientific papers and 9 monographs, including foundational texts on martingales and stochastic calculus. Scientific Awards: Markov Prize (1974) Kolmogorov Prize (1994) Humboldt Research Award (1996) Honoris Causa degrees from Albert Ludwigs University and Amsterdam University Distinguished Professor of Moscow State University (2003) Leadership Roles: Shiryaev has led the Laboratory of Statistics of Stochastic Processes at Steklov Institute (1986–2002) and currently chairs the Probability Theory Department at MSU. He has held editorial leadership in journals like Theory of Probability and its Applications and Finance and Stochastics .

• Statistics
• Probability
• Copulas
• +۲ مورد دیگر

Johanna Genest Nešlehová is a Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. She previously served as Assistant Professor at McGill University (2009-2020) and holds a PhD in Mathematics from Carl von Ossietzky University of Oldenburg. Her research spans statistics, probability theory, and financial mathematics with focus areas including multivariate analysis, dependence modeling, copulas, and statistical methods for financial applications. She serves as editor for the Canadian Journal of Statistics and Statistics and Risk Modeling. Research publications demonstrate focus on multivariate statistical methods, dependence modeling, and applications in financial mathematics. Recent work includes stochastic decomposition methods, causal inference techniques, and rank-based estimation. Carrie M. Derick Award for Graduate Supervision and Teaching CRM-SSC Prize