Prof. Andreas Farnleitner is a Professor at TU Wien, affiliated with the Environmental Microbiology and Molecular Diagnostics Research Group. His research focuses on microbial water quality, molecular diagnostics, and environmental microbiology. Key areas include PCR-based methods for fecal pollution tracking, antimicrobial resistance in aquatic environments, and climate change impacts on waterborne pathogens. He has contributed to advancements in DNA aptamer technology for rapid water quality monitoring and employs machine learning for predictive modeling in hydrology. His work spans collaborations across Europe, addressing One Health challenges and wastewater-based epidemiology. Recent projects include studies on Vibrio cholerae in Austrian bathing waters and the biostability of drinking water resources. Prof. Farnleitner leads interdisciplinary initiatives to improve water safety and environmental management. Publications highlight his expertise in microbial source tracking, climate change effects on infection risks, and antibiotic resistance patterns. His team develops innovative diagnostic tools and models for environmental monitoring, with applications in both academic and applied settings. Current projects emphasize the integration of molecular biology and engineering to address global water quality challenges.
Christine Bauer is a Professor of Interactive Intelligent Systems at the Department of Artificial Intelligence and Human Interfaces (AIHI), University of Salzburg, Austria. She is also Co-Lead of the interdisciplinary focus area InterMediation. Music—Effect—Analysis at the inter-university organization Wissenschaft & Kunst. Her research lies at the intersection of artificial intelligence, human-computer interaction, and human-centered computing, with a strong emphasis on fairness, context-awareness, and multi-method evaluation in recommender systems. Doctoral degree in Social and Economic Sciences (Business Informatics), 2009, University of Vienna MSc in Business Informatics, 2011, TU Wien Diploma in International Business Administration, 2002, University of Vienna Study of Jazz Saxophone, Konservatorium der Stadt Wien Her research interests center on interactive intelligent systems , particularly recommender systems in music and media, with a human-centered approach. She investigates how technology can align with societal and individual needs, focusing on fairness in algorithms , culture-aware recommendation , and multi-stakeholder evaluation . Her work often integrates interdisciplinary methods and real-world impact. The recent publications highlight a consistent trend in fairness and diversity in music and news recommendation, cross-cultural user behavior , and evaluation frameworks . Her research bridges technical rigor with ethical considerations, particularly in algorithmic bias and artist equity. She has made significant contributions to understanding gender imbalance , cultural granularity , and user conformity in digital music ecosystems. Elise Richter laureate (FWF) 6 best paper awards, 5 nominations Women in RecSys Journal Paper of the Year Award (2023, 2024) 5 awards and 6 recognitions for reviewing excellence Christine Bauer has supervised over 70 theses and taught at 17 institutions worldwide. She is deeply involved in academic service, including editorial roles (AE at TOIS, TORS, JITT), conference organization (RecSys, CHI, CIKM), and mentoring initiatives (WiMIR, LEA, Allyship at CHI). She leads impactful projects such as FairRecKit and SpART: Spotlight on Artists in Recommender Systems , aiming to create more equitable and transparent recommendation technologies. She frequently engages in public outreach through keynotes, media appearances, and policy discussions. She is affiliated with the University of Salzburg as her primary institution and was previously associated with JKU Linz and the University of Klagenfurt. Her labs and research teams focus on interactive intelligent systems , music recommenders , and fair AI , often in collaboration with interdisciplinary partners in arts and social sciences.
Navid Rekab-saz is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz (JKU), Austria. He is actively involved in research and teaching, offering courses such as Natural Language Processing and Natural Language Processing with Deep Learning . He maintains regular office hours and is accessible via email and a dedicated booking system for meetings. His research focuses on natural language processing , information retrieval , fairness and bias in AI , and recommender systems , with applications in humanitarian action and ethical AI. He employs deep learning and machine learning techniques to address challenges in bias mitigation, explainability, and domain adaptation. His work often bridges technical innovation with societal impact, especially in developing inclusive and fair AI systems. The recent publications of Navid Rekab-saz reflect a strong trend in debiasing strategies , parameter-efficient learning , and evaluation of societal biases in search and recommendation systems. His research spans from foundational work on word embeddings and retrieval models to applied studies in humanitarian NLP and gender bias in user queries. He frequently collaborates with a broad network of researchers and contributes to the development of datasets and benchmarks. Scientific Awards: Best Student Paper Award at ISMIR 2022 for 'Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms' Advising and Grants: Navid Rekab-saz has advised and collaborated with numerous students and researchers, many of whom are co-authors on his publications. While specific grant details are not listed in the provided text, his extensive publication record in top-tier venues suggests active involvement in funded research projects, likely supported by national or European funding bodies. He is also engaged in interdisciplinary research, particularly at the intersection of technical AI and legal or social implications. Labs and Teams: He is a core member of the Institute of Computational Perception at JKU, where he contributes to research projects in computational linguistics and AI. He collaborates closely with the team led by Prof. Markus Schedl and participates in initiatives related to music information retrieval, fairness in AI, and humanitarian applications of NLP.
Josef Eitzinger is a full Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Institute of Meteorology and Climatology. His research bridges agricultural meteorology, climate change impacts, and sustainable farming systems. He holds a Dr.nat.techn. degree and completed postdoctoral work at Colorado State University. His research focuses on: Agricultural meteorology and microclimatology Climate change impacts on crop production and water resources Drought monitoring and forecasting systems Agrivoltaics and renewable energy integration in agriculture Recent publications (2023-2025) emphasize climate risk modeling, soil moisture dynamics, agrivoltaic design, and sustainable land management. Trends show strong integration of remote sensing, machine learning, and cross-disciplinary approaches to address agricultural resilience. Awards & Honors: Austrian Sustainability Award 2018 WMO Award as RA VI expert team leader (2014) Klimaschutzpreis (2002) Pöttinger Preis (2001) He leads 67+ projects including EU initiatives like CropShift (climate-driven crop shifts) and Machine Learning ET Estimation . His team develops tools like the Agricultural Risk Information System (ARIS) for real-time agrometeorological forecasting. At BOKU's Institute of Meteorology and Climatology, he oversees micrometeorological field studies and collaborates with European research networks on climate adaptation strategies.
Jürgen Huber is a Full Professor in Finance and Head of the Department of Banking and Finance at the University of Innsbruck. He has been a central figure in experimental finance since 1998, founding the Society for Experimental Finance and leading international collaborations with institutions like Stockholm School of Economics and VU Amsterdam. Education: PhD in Political Sciences (2001) and Business Administration (Habilitation, 2007), both from University of Innsbruck. Research Interests: His work spans Experimental Economics , Behavioral Finance , and Meta Science , focusing on market microstructure, replicability of scientific research, and communication behavior. Recent projects explore: Climate change policy through CO2 taxation and climate dividends . Digital finance implications of cryptocurrencies and NFTs . Communication differences (face-to-face vs. online) and their impact on decision-making. Scientific objectivity in peer review via the Nobel and Novice project. Article Trends: His 2024 publications in Nature Human Behaviour and PNAS emphasize replicability crises, climate economics, and communication behavior. Earlier work in Management Science and Journal of Economic Behavior and Organization analyzes market microstructure, financial transaction taxes, and crowd-sourced research validation. Scientific Awards: Voted Professor of the Year by students (2019-2023), with multiple FWF and OeNB grants totaling over €1 million. Received the Pater Johannes Schasching SJ-Preis (2016) and Forschungspreis der Stiftung Südtiroler Sparkasse (2015). Teaching: Integrates research into interactive lectures, including lab-based simulations for stock market experiments. Taught at universities in Austria, Germany, USA, Thailand, Vietnam, and Indonesia.
Dr. Nikolina Ban is an Assistant Professor at the Department of Atmospheric and Cryospheric Sciences (ACINN) at the University of Innsbruck, Austria. Her research focuses on high-resolution climate modeling, convection-permitting models, and mountain climate dynamics. She leads projects such as 'Mountain Climate at the Kilometre-Scale Resolution' and collaborates with international teams like TEAMx-UIBK and the CORDEX-FPSCONV-Team. Key areas include extreme rainfall projections, hail/lightning diagnostics, and the impact of climate change on Alpine regions. Expertise: Regional climate modeling, convection-permitting approaches, mountain climate variability. Key Projects: Third Pole climate simulations, Alpine hydrological studies, and national climate scenario development in Austria. Her work addresses challenges in resolving fine-scale climate processes over complex terrains. Recent studies emphasize improving projections of precipitation extremes and understanding model uncertainties. Collaborations span institutions globally, including the Swiss Federal Institute of Technology (ETH Zurich) and the University of California, Los Angeles (UCLA). Publications highlight advancements in ensemble-based climate simulations and the added value of kilometer-scale models for capturing temperature and wind patterns in alpine regions. Ongoing research includes evaluating dynamical downscaling techniques for reliable regional climate change assessments.
Sabine Seidel is a full Professor at the Institute of Crop Production, Department of Agrarwissenschaften, University of Natural Resources and Life Sciences, Vienna (BOKU). Her research integrates plant modeling, sustainable agriculture, and digital farming to enhance climate-resilient and resource-efficient crop systems. Her research interests focus on the development and testing of innovative, diverse (organic) cultivation systems, particularly mixed cropping and intercropping. She investigates ecosystem services such as yield and greenhouse gas emissions through measurements and modeling. Her work emphasizes the interactions between genotype, environment, and management (G×E×M), especially concerning water, nitrogen, and root dynamics. She also explores root growth responses to nutrient deficiency and drought stress, and leads initiatives in digital farming, including AI tools for pollinator detection and digital twin development in agriculture. Analysis of her recent publications (2024–2025) reveals a strong trend in interdisciplinary research combining field experiments with advanced modeling. Her work spans agroecosystem modeling, intercropping systems (especially wheat and faba bean), soil-crop interactions, and the application of AI and machine learning in agriculture. She frequently contributes to multi-model studies and calibration protocols, emphasizing model accuracy and validation. Her research is highly collaborative, involving teams across Germany and Austria. Root:shoot ratio under conservation tillage Phenotypic plasticity in winter wheat Resource acquisition in intercropping Digital crop growth simulation using GANs Soil carbon sequestration and organic matter dynamics She has been actively involved in the PhenoRob Cluster of Excellence as a junior research group leader (2020–2025), focusing on optimizing plant mixtures through field experiments and modeling. Prior to this, she conducted postdoctoral research on subsoil management at the University of Bonn. Her work bridges ecology, plant science, soil science, and digital technologies. She earned her doctorate on plant modeling and irrigation from the Technical University of Dresden and studied agricultural sciences at the Technical University of Munich. She is based in Vienna and maintains an active presence in knowledge transfer, with media contributions in print and online outlets discussing sustainable farming practices.
Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.
Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.
Enrique Sentana is a Professor of Economics at CEMFI (Centro de Estudios Monetarios y Financieros) in Madrid, Spain. He is also a Research Fellow at the CEPR Financial Economics Programme and a Senior Research Associate at the LSE Financial Markets Group. His academic career spans prestigious institutions including the London School of Economics and the University of Alicante. Degrees: PhD in Economics (LSE, 1991), MSc in Econometrics and Mathematical Economics (LSE, 1987), Licenciado en Ciencias Económicas y Empresariales (University of Alicante, 1985) Dr. Sentana specializes in Econometrics , with a focus on Asset Pricing , Financial Economics , and VIX Derivatives . His methodological contributions include work on ARCH models, indirect estimation, and identification issues in econometrics, advancing volatility modeling and financial risk assessment. His research trends highlight innovations in empirical asset pricing , nonlinear time series , and financial market linkages . Notable achievements include the Rey Jaime I Prize in Economics (2014) , Fellowships at the Econometric Society and Journal of Econometrics , and prestigious prizes from the University of London and LSE. Scientific Awards: Rey Jaime I Prize in Economics (2014) Fellow of the Econometric Society (2012) Fellow of the Journal of Econometrics (2010) Sayers Prize, University of London (1992) Ely Devons Prize, London School of Economics (1987) Dr. Sentana has advised 10 PhD students at CEMFI and held editorial roles including Managing Editor of the Review of Economic Studies and Co-Editor of the Journal of Financial Econometrics . He has also served as Executive Vice-President of the Econometric Society and Treasurer of its European Standing Committee.
Rudolf Ramler is an External Lecturer at TU Wien's Faculty of Informatics, Department of Information Systems Engineering. He specializes in software testing methodologies, automated testing frameworks, and software quality assurance. His research focuses on improving testing practices for legacy systems, industrial automation software, and defect prediction in software projects. He teaches the Software Testing course (VU 188.280) in 2025S. His work spans empirical investigations, tool-supported testing, and systematic literature reviews. Ramler has contributed to projects like CDL-SQI (2018–2024), exploring practical approaches for testing industrial automation systems. Research interests include test code readability, automated testing strategies, and value-driven testing frameworks. His publications address challenges in retrofitting tests for legacy code, comparing manual and automated testing efficacy, and developing context-specific defect prediction models. He has collaborated with industry partners to apply academic research to real-world software engineering problems.
Zhang Yi-Cheng is a Full Professor of Theoretical Physics at the University of Fribourg, Switzerland, since 1992. His academic career includes visiting professorships at Nordita (Denmark) and INFN (Italy), and postdoctoral research at Brookhaven National Lab (USA). He specializes in interdisciplinary fields such as Econophysics , Statistical physics , and Complex network sciences , focusing on applications in financial markets, social systems, and global trade networks. His research explores topics like market dynamics, network structures, and algorithmic ranking systems. Notable awards include the 2011 Honorary Director of the Complexity Sciences Research Center and recognition as a 2011 Chinese '1000 Talents' awardee . His work bridges physics-based methodologies with socio-economic systems, addressing challenges in information-driven economies and networked societies. Zhang has contributed to influential studies on ranking algorithms, percolation theory in networks, and the interplay between economic complexity and trade. His interdisciplinary approach has led to advancements in understanding systemic risks, market inefficiencies, and the role of information in shaping global economic interactions.
Univ.-Prof. Dipl.-Ing. Dr.sc.ETH Michael Hartmann is a full professor of Power Electronics at the Electric Drives and Power Electronic Systems Institute of Graz University of Technology (TU Graz), leading the Power Electronic Systems research group since 2021. He holds academic degrees from Vienna University of Technology and ETH Zurich. Education: B.Sc. and M.Sc. (both with honors) in Electrical Engineering, Vienna University of Technology (2005–2006) Ph.D. in Power Electronics, ETH Zurich (2011) Research Interests: Three-phase power conversion, high-frequency switched converters, and MV connected systems Optimal design of compact power converters using novel semiconductor technologies Converter modeling, monitoring, and lifetime prediction Awards: IEEE Transaction Prize Paper Award (2012) 2nd TIA Award (2023) Google Little Box Challenge 2nd Place (2016) Grants & Industry Collaboration: Developed industrial power converter systems at Schneider Electric (2011–2021) Active in standardization groups for low-frequency EMC and energy efficiency Labs/Teams: Heads the Power Electronic Systems research group at TU Graz, focusing on cutting-edge converter technologies.
Tobias Ofner-Graff is a researcher at the Institute of Forest Growth within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based at Peter-Jordan-Straße 82, 1190 Wien, his work focuses on advanced forest monitoring technologies. His research interests include: LiDAR and remote sensing applications in forestry Automated forest inventory systems Forest regeneration quantification Airborne Laser Scanning (ALS) data analysis Sustainable forest harvesting planning Recent project contributions include: Leading lidar-based forest monitoring systems development Developing spatial forest growth models Implementing digital inventory workflows His publications demonstrate expertise in: Quantifying forest resources through 3D point clouds Advanced timber stack measurement techniques ALS data integration for forest modeling Mobile laser scanning applications Forest climate adaptation strategies
Jennifer Culbertson is an Associate Professor at the University of Edinburgh, where she has been a faculty member since 2014 when she took up a Chancellor's Fellowship, later being promoted to Reader (equivalent to Associate Professor) in 2018. She is a founding member of the Centre for Language Evolution at Edinburgh and maintains an active research program in experimental linguistics and cognitive science. Dr. Culbertson obtained her PhD in Cognitive Science from Johns Hopkins University in 2010, with her dissertation receiving the Robert J. Glushko Prize for Outstanding Dissertations in Cognitive Science in 2011. Her primary research investigates how languages are shaped by learning and use, with particular focus on grammatical structures (like word order) and morphological categories (gender and person systems). She employs experimental and computational methods to explore how typological universals arise from properties of the human cognitive system. Her work bridges theoretical linguistics, cognitive science, and language evolution, examining the cognitive constraints that influence linguistic patterns across diverse languages and learning contexts. She frequently uses artificial language learning paradigms to isolate specific cognitive mechanisms underlying language structure. Analysis of her recent publications reveals a consistent focus on cognitive biases in linguistic structure, particularly in word order and morphological systems. Her research increasingly incorporates cross-population studies (including work with autistic learners) and developmental perspectives to understand how language patterns emerge across different populations and throughout the lifespan. She examines how simplicity principles, communicative efficiency, and statistical learning shape linguistic structure. Dr. Culbertson's scientific contributions have been recognized with several prestigious awards: Robert J. Glushko Prize for Outstanding Dissertations in Cognitive Science (2011) ERC Starting Grant Young Academy of Europe membership (2019) Her research has been supported by over €2 million in funding from the ESRC and ERC. While specific advisees aren't mentioned in the provided text, her active publication record with numerous co-authors suggests she mentors graduate students and early-career researchers. Her work has significant implications for understanding language universals, language acquisition, and the cognitive foundations of linguistic structure. As a founding member of the Centre for Language Evolution at Edinburgh, Dr. Culbertson collaborates with an interdisciplinary team investigating the biological, cognitive, and cultural foundations of human language. Her research often intersects with psychology, cognitive science, and computational linguistics, contributing to a comprehensive understanding of language as both a cognitive and social phenomenon.
