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Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna

• Quantum Information Theory
• Quantum Foundations
• Quantum Tomography
• +5 more

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +3 more

Sara van de Geer is a Full Professor at the Seminar for Statistics within the Department of Mathematics at ETH Zürich since 2005. She previously held academic positions at the University of Leiden, Université Paul Sabatier (Toulouse), and others. She earned a Master's (1982) and Ph.D. (1987) in Mathematics from Leiden University. Her research focuses on high-dimensional statistics, empirical processes, and mathematical foundations of machine learning. Van de Geer has received prestigious recognitions including the Van Wijngaarden Award (2016), Knight in the Order of Orange-Nassau (2015), and membership in Leopoldina (2013). She served as President of the Bernoulli Society (2015–2017) and Chair of the Seminar for Statistics at ETH Zürich. Her contributions include landmark works on statistical learning theory and high-dimensional inference, with key publications in top journals like Annals of Statistics and SIAM/ASA Journal on Uncertainty Quantification. Her academic leadership includes organizing Saint Flour Lectures, Wald Lectures (2016), and delivering plenary lectures globally. Her research bridges theoretical statistics with applied methodologies, emphasizing rigorous mathematical frameworks for modern data analysis challenges.

• Mathematics for machine learning
• High-Dimensional statistics
• Empirical processes
• +2 more

Horacio Dante Espinosa is the James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship at Northwestern University's McCormick School of Engineering, holding the rank of Professor in Mechanical Engineering. He also directs the Theoretical and Applied Mechanics Program and the Micro and Nano Mechanics Lab. His research spans bioinspired materials, single-cell analysis, and multiscale experimentation, with a focus on nanoelectronics and energy harvesting. Espinosa earned his Ph.D. in Applied Mechanics from Brown University and has held academic roles at Purdue University and Harvard University. He leads interdisciplinary teams exploring metamaterials, cell engineering, and advanced fabrication techniques. His honors include the Prager Medal (2019) and multiple fellowships. Espinosa's lab combines experimental and computational methods, with capabilities in nanomechanical testing and biological assay development. Education: Ph.D. in Applied Mechanics (Brown University, 1992), M.Sc. in Structural Engineering (Polytechnic of Milan, 1987), Civil Engineering (Northeastern National University, Argentina, 1981) Key Roles: Director of iCET (2015–2018), Faculty Director of NUFAB (2013–2015), Visiting Professorships at Stanford and Harvard Labs: Micro and Nano Mechanics Lab focuses on biomaterials, metamaterials, and single-cell manipulation with advanced microscopy and electroporation systems Grants/Awards: NSF-CAREER Award, ONR Young Investigator Award, and leadership roles in professional societies His research bridges mechanics, materials science, and biology, with applications in healthcare technologies and advanced materials. Current projects include phononic crystal studies, Kirigami engineering, and high-throughput electroporation platforms for cell analysis.

• Bioinspired Materials
• Single-Cell Studies
• Multiscale Experimentation and Modeling
• +4 more

Dragi Kimovski is a Habilitated Assistant Professor in Distributed Systems at Klagenfurt University, Austria, focusing on Edge Computing and AI. He previously held roles at the University of Innsbruck and the University of Information Science and Technology in Macedonia. His research spans Edge/Fog/Cloud computing, multi-objective optimization, and high-performance computing. He has coordinated major projects like 6GContinuum and KärtnerFog, and led initiatives such as DataCloud and ASPIDE. His teaching includes courses on Distributed Computing, Cloud Computing, and IoT. He is the co-creator of the Carinthian Computing Continuum and maintains a blog on Edge AI World. His work emphasizes sustainable and efficient computing solutions for emerging technologies. Education: Not explicitly listed in the provided text. Research Interests: Edge Computing, Fog Computing, Cloud Computing, Multi-objective Optimization, High-Performance Computing, AI in Distributed Systems. His work addresses challenges in resource management, latency reduction, and scalability across heterogeneous environments, with applications in healthcare, IoT, and 6G networks. Projects: 6GContinuum (Coordinator): Focuses on AI services over 6G networks. KärtnerFog (Scientific Coordinator): Develops adaptive Fog infrastructures over 5G. DataCloud (WP5 Leader): Manages Big Data pipelines on the Computing Continuum. ASPIDE (Scientific Coordinator): Advances exascale programming models for data processing. Teaching: Klagenfurt University: Courses include Distributed Computing, IoT, Cloud Computing, and Advanced Programming. University of Innsbruck: Taught Advanced Parallel and Distributed Systems. University of Information Science and Technology: Courses in High-Performance Computing and Network Architectures. Labs/Teams: Co-created the Carinthian Computing Continuum, an automated SDN testbed for Edge computing research. Active in interdisciplinary teams addressing extreme data processing and sustainable computing.

• Edge Computing
• Fog Computing
• Cloud Computing
• +4 more

Gilles Bourret is a Tenured Associate Professor in the Department of Chemistry and Physics at the University of Salzburg, Faculty of Natural and Life Sciences. He leads an active research group focused on nanomaterials, combining synthesis, characterization, electromagnetic simulations, and device testing. His work explores how nanoscale geometry influences functionality in complex 3D systems interacting with light. Education: M. Eng. and M.Sc. in Applied Physics and Nanotechnology, National Institute of Applied Sciences, Toulouse, France Ph.D. in Chemistry (Materials’ Chemistry), McGill University, Canada Habilitation in Materials Science, University of Salzburg His research interests center on advanced nanofabrication techniques such as three-dimensional electrochemical axial lithography (3DEAL), enabling the creation of highly uniform silicon nanowire arrays and nanostructured substrates. These are applied in photoelectrochemistry (e.g., water-splitting photocathodes), plasmonic catalysis (including gas-phase photomethanation), and Surface-Enhanced Raman Spectroscopy (SERS). His group emphasizes structure optimization through 3D electromagnetic simulations and high-resolution chemical synthesis. His publication record includes high-impact journals such as Nature Nanotechnology , Advanced Materials , Nano Letters , and JACS , with themes revolving around plasmonics, nanostructure design, catalysis, and light-matter interactions. The articles reflect a strong interdisciplinary trend combining materials chemistry, nanophotonics, and energy applications. Scientific Awards and Recognition: 6 awards/fellowships 15 invited lectures 5 interviews in mainstream media (e.g., Kronenzeitung, Die Presse, Salzburger Nachrichten) Gilles Bourret has secured total research funding of €6.3 million (as main or co-applicant), including infrastructure grants, with €468,000 directly awarded to him as principal investigator. He currently supervises four PhD students: Joshua Piaskowski, Amin Farhadi, Theresa Bartschmid, and Gebhard Sabhati (co-supervised). His lab is developing novel nanomaterials for energy conversion and sensing applications, with a strong emphasis on scalable fabrication and reproducible device performance.

• Nanomaterials
• Materials Chemistry
• Plasmonics
• +7 more

Paul Eisenberg is an Assistant Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. He holds a doctorate in Mathematics and has held positions at the University of Liverpool, Vienna University of Technology, and Technical University of Dortmund. His research focuses on probability theory and financial mathematics, particularly stochastic processes, term structure modeling, and applications to financial markets. He teaches courses in Probability, Quantitative Methods, and Business Mathematics. Research outputs concentrate on stochastic processes in finance, including term structure modeling, stochastic optimization, market microstructure, and applications to cryptocurrency markets. Recent work shows emphasis on regime-switching models and high-dimensional stochastic systems.

• Probability Theory
• Financial Mathematics
• Stochastic Processes

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +5 more

Univ.-Prof. Martin Wagner is a Professor at the Department of Economics, Alpen-Adria-Universität Klagenfurt, where he serves as Vice-Dean of the Faculty of Management, Economics and Law and Head of the Quantitative Economics Division. His research bridges theoretical and applied econometrics with environmental and macroeconomic challenges. Affiliation: Department of Economics, Faculty of Management, Economics and Law, Alpen-Adria-Universität Klagenfurt Wagner’s work focuses on econometric theory and its applications, particularly in modeling nonlinear cointegrating relationships, environmental economics, and macroeconomic forecasting. His expertise includes: Cointegrating polynomial regressions Unit root processes and stationarity High-dimensional time series analysis Environmental Kuznets curves Panel data econometrics Macroeconomic modeling Recent publications highlight his contributions to robust estimation techniques and the intersection of climate science with economic growth dynamics. Contact: martin.wagner@aau.at

• Data Science
• Econometrics
• Economics
• +5 more

Gregor Weihs is Vice-Rector for Research at the University of Innsbruck, holding a full professorship in Photonics at the Institute for Experimental Physics. He directs the Cluster of Excellence Quantum Science Austria and previously served as Austrian Science Fund (FWF) Vice-President and interim President. His academic journey includes roles at Stanford University, the University of Tokyo, and the University of Waterloo, where he held a Canada Research Chair in Quantum Photonics. Education: MSc (1994, University of Innsbruck), PhD (2000, University of Vienna) awarded 'sub auspiciis praesidentis.' He completed habilitation in Experimental Physics in 2005 at the University of Vienna. Research focuses on quantum photonics, semiconductor optics, and foundational quantum mechanics. Key projects include entangled photon pair generation from nonlinear waveguides, quantum dots, and semiconductor microcavities. He explores quantum communication, many-body interference, and hypercomplex quantum mechanics testing. Awards: Wilhelm Exner Medal, ERC Starting Grant, Canada Research Chair Leadership: Department Head of Experimental Physics (2013–2021), Faculty Council Chair, IQOQI Events Director His work bridges theory and experiment, with contributions to integrated quantum devices and precision tests of quantum foundations.

• Quantum Photonics
• Quantum Information
• Semiconductor Optics
• +3 more

Joachim Hermisson is a Professor of Mathematics and Biology at the University of Vienna, where he leads the Mathematics and Biosciences Group (MaBS). He is a principal investigator in the SFB Polygenic Adaptation and the founder of the evolVienna network, fostering collaboration in evolutionary biology across Vienna. His academic journey spans physics, philosophy, and evolutionary theory, reflecting a deeply interdisciplinary approach. Education: PhD in Theoretical Physics, University of Tübingen (1999); Habilitation in Evolutionary Biology, LMU Munich (2006) Appointments: Professor at University of Vienna (since 2012); Vienna Science Chair (2007–2012); Emmy Noether Group Leader, LMU Munich (2002–2007) Joachim Hermisson’s research lies at the intersection of theoretical population genetics and mathematical biology. He develops models to understand adaptation, speciation, and the role of polygenic traits under natural selection. His work integrates molecular and phenotypic approaches, focusing on coalescent theory, epistasis, robustness, and evolvability. He is particularly interested in how complex genetic architectures evolve and influence evolutionary trajectories. His recent publications reflect a strong emphasis on polygenic adaptation beyond classical selective sweeps, hybridization, introgression, and the genomic signatures of selection. The articles span mathematical modeling, stochastic processes, and genomic data analysis, demonstrating a consistent focus on theoretical innovation with empirical relevance. Marcus W. Feldman Prize in Theoretical Population Biology (2018) WWTF Vienna Science Chair (2006) Emmy Noether Fellowships (DFG, 2000–2007) Graduate Program Award, VolkswagenStiftung (2006) Doctoral Prize, University of Tübingen (1999) Hermisson has advised numerous PhD and Master’s students, many of whom have gone on to independent research careers at institutions such as MPI Cologne, University of Edinburgh, and University of Vienna. He has secured major grants from FWF, DFG, EU Marie Curie, and ERC programs. He serves as Associate Editor for Genetics and Evolution , and has been a referee for leading journals and funding agencies including Nature, Science, PNAS, and the ERC. He leads the MaBS group, a vibrant research team focused on mathematical modeling in evolutionary biology. The group includes postdocs, PhD students, and collaborators working on polygenic adaptation, speciation, and eco-evolutionary dynamics. The lab participates in the SFB Polygenic Adaptation and the Vienna Graduate School for Population Genetics, contributing to a strong training environment for early-career scientists.

• Theoretical Population Genetics
• Mathematical Biology
• Molecular Evolution
• +13 more

Emmerich Kneringer is an Associate Professor at the Institute for Astro- and Particle Physics, Faculty of Mathematics, Computer Science and Physics, University of Innsbruck. He is a key member of the Experimental Particle Physics research group and actively contributes to the ATLAS collaboration at CERN. His research focuses on experimental high-energy physics, particularly Higgs boson physics, top quark physics, electroweak interactions, and searches for physics beyond the Standard Model, including supersymmetry, dark matter, and exotic particles. He also works on advanced data analysis methods such as neural simulation-based inference and machine learning applications in particle physics. Dr. Kneringer's recent publications (2023–2025) show a strong trend in precision measurements (e.g., Higgs and top properties), combination of search results, and innovative analysis techniques. His work spans detector performance, cross-section measurements, and searches for new phenomena in proton-proton and heavy-ion collisions. He is actively involved in public outreach, delivering lectures on astronomy, cosmic radiation, and particle physics to schools and the public, including events like Masterclasses and the Long Night of Research. Experimental Particle Physics Higgs and Top Quark Physics Machine Learning in Physics Beyond Standard Model Searches Heavy-Ion Physics Dr. Kneringer has no listed scientific awards in the provided text. He advises no named students in the material. His research is conducted within the ATLAS collaboration, a large international team at CERN, and he contributes to both physics analysis and detector performance studies. He also participates in educational initiatives, including public lectures and training programs.

• Experimental Particle Physics
• High-Energy Physics
• Higgs Boson Physics
• +10 more

Ursula Laa is an Assistant Professor at the Institute of Statistics , University of Natural Resources and Life Sciences, Vienna (BOKU) since 2020. Previously, she was a Postdoctoral Researcher at the Department of Econometrics & Business Statistics and School of Physics & Astronomy , Monash University (2017–2020), and earned her doctorate in particle physics at the University of Grenoble-Alpes , France (2014–2017). Her academic journey began with physics studies at the University of Vienna (2007–2014). Research Interests: She specializes in statistics, with a focus on data visualization, machine learning, computational statistics, and environmental statistics. Her work bridges high-dimensional data analysis with applications in particle physics and environmental studies, particularly in climate change and hydrology. Scientific Contributions: Ursula has actively contributed to projects involving climate change impacts on hydrological systems interactive visualization tools (lionfish, cubble, tourr) statistical modeling for environmental and particle physics data Her recent publications emphasize tour algorithms, projection pursuit, and spatiotemporal indexing, reflecting a blend of data science and domain-specific applications. Awards & Recognition: She has been a keynote speaker at DAGStat 2025 active participant in international conferences (EGU, useR!, Joint Statistical Meetings)

• Statistics
• Data Science
• Machine Learning
• +4 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Nikolaus Umlauf is an Associate Professor at the Department of Statistics, University of Innsbruck. He specializes in Bayesian distributional regression, structured additive models, and spatiotemporal analysis, with applications in public health, climate science, and real estate valuation.

• Statistics
• Machine Learning
• Environmental Data Analysis
• +3 more