جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Transformers
• +۷ مورد دیگر

Samy Bengio is Senior Director of AI and Machine Learning Research at Apple and an adjunct professor at École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on advancing the reasoning capabilities of autoregressive models, developing deep architectures for structured data, and improving generalization in deep learning through adversarial training and curriculum design. Current Affiliations Apple (Senior Director, Machine Learning Research) EPFL (Adjunct Professor) Research Interests : Bengio explores the limits of reasoning in auto-regressive models, with a focus on mathematical reasoning and length generalization. He has contributed to deep architectures for sequences (e.g., speech and text), addressing long-tail issues in machine learning. His work extends to designing machine learning libraries like Torch, TensorFlow, and MLX, and understanding the theoretical foundations of transformer models. Article Trends : His recent publications emphasize reasoning in large language models (LLMs), symbolic regression with transformers, and addressing challenges in length generalization. Key themes include algorithmic reasoning, global visual reasoning, and the exploration of alternative text factorizations for improved performance. Scientific Awards : Outstanding Paper Award at ICML 2023 Advising and Collaborations : Bengio has mentored numerous students and researchers, including Mark Barnard, Fabien Cardinaux, Silvia Chiappa, and Ronan Collobert. He has organized workshops like the Simons Institute’s Foundation of Deep Learning program and co-chaired ethics reviews at NeurIPS. Labs and Teams : He has collaborated with labs and teams at Apple, EPFL, and the Simons Institute for the Theory of Computing.

Jonas Lindströmمشاهده پروفایل

دانشیار

• Digital History
• Gender History
• Social History
• +۳ مورد دیگر

Jonas Lindström is a Senior Lecturer and Associate Professor at the Department of History , Uppsala University. His work bridges digital history and social history, focusing on gender dynamics, agrarian systems, and early modern Scandinavian societies. Education: Master of Education (2003), PhD in History (2008), Docent (2020) Roles: Director of Studies for second-cycle education (2022-2024), Research Leader for the GaW database (2017–present) Research Interests Lindström explores socioeconomic inequalities, resource distribution, and gendered labor in pre-industrial Sweden. His work emphasizes: Interconnectedness of rural households Digital methodologies for historical data extraction Transition from early modern to modern gendered work structures Landless and semi-landless communities Verb-based analysis of historical sources Microhistorical approaches to agrarian regimes Publications & Tools His recent articles (2025) in Gender, Work, and the Transition to Modernity analyze rural labor networks and digital databases. Earlier works (2022-2024) examine servant systems, wage economies, and methodological innovations like verb-based data capture.

Nadja Kleinمشاهده پروفایل

استاد

• Bayesian learning
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Nadja Klein is a Professor at the Scientific Computing Center (SCC) at Karlsruhe Institute of Technology (KIT), where she leads the Methods for Big Data research group. She holds the position of Emmy Noether Research Group Leader and is a member of prestigious organizations including AcademiaNet and Die Junge Akademie. Her academic journey includes doctoral studies in Mathematics, a postdoctoral position at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation, and a professorship in Statistics and Data Science at Humboldt-Universität zu Berlin before joining KIT. Her research focuses on Bayesian learning methods, which allow for the incorporation of prior knowledge, quantification of uncertainties, and bringing clarity to the "black boxes" of machine learning. The Methods for Big Data lab under her leadership develops innovative, reliable, and generalizable methods to handle massive datasets across diverse fields including biomedical data analysis, weather prediction, and autonomous driving technologies. Her work spans theoretical analysis, method development, and practical applications of statistical and machine learning techniques. Dr. Klein's recent publications demonstrate a strong trend toward integrating Bayesian principles with deep learning approaches to address challenges in uncertainty quantification, data efficiency, and model interpretability. Her research shows particular strength in distributional regression frameworks, copula modeling, and developing robust methods for real-world applications where uncertainty matters. She has been recognized with the prestigious Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award for her outstanding contributions to statistical methodology. Her work bridges theoretical statistics with practical machine learning applications, making significant contributions to both academic research and industry collaborations. Dr. Klein actively collaborates with industry partners such as Continental Automotive GmbH on cutting-edge research in autonomous driving technologies. Her lab maintains a strong focus on both theoretical foundations and practical implementations of statistical learning methods, with particular emphasis on making machine learning more reliable, interpretable, and data-efficient through Bayesian approaches.

• Science Education
• Teacher Education
• Motivation
• +۶ مورد دیگر

Ralph Meulenbroeks is a Professor of Science Education and Head of Department at the Freudenthal Institute, Faculty of Science, Utrecht University. Appointed chair of the Freudenthal Institute on September 1, 2024, he became a full professor of scientific literacy in 2025, leading national efforts to address critical educational challenges. His academic journey includes a cum laude Applied Physics degree from Eindhoven University of Technology (1991), a PhD in Particle Physics (1996), and a Master of Education from Utrecht University (2012, cum laude). Uniquely, he also holds a soloist diploma in double bass (1994, cum laude) and has performed with major orchestras including the Royal Concertgebouw Orchestra, earning the Edison classical audience award. Science Education and Teacher Training Motivation and Psychological Needs in Education Digital Research Skills Development Educational Leadership and Innovation Addressing Teacher Shortages in STEM Fields Meulenbroeks' research portfolio demonstrates a consistent focus on practical educational challenges with theoretical depth. His recent publications explore digital research skills in secondary science education, transactional distance in hybrid learning environments, and scientific literacy assessment. His work bridges educational theory with classroom practice, particularly in inquiry-based learning and motivation frameworks. Edison classical audience award (Netherlands' most prestigious recording award) Best Newcomer teaching award (2012) Teacher of the year (2015) U-talent program award (2015) As coordinator of the national beta4all program and secretary of the bèta-lerarenkamer, Meulenbroeks has pioneered initiatives to address STEM teacher shortages. His work with student teaching assistants has led to frameworks for recognizing their contributions, while his popular YouTube channel extends his educational influence beyond traditional academic boundaries. He chairs Vereniging NLT and leads the bèta-lerarenkamer, demonstrating his commitment to shaping science education policy. His leadership extends to the Freudenthal Institute where he guides research on science education, youth development, and innovative teaching methods. Meulenbroeks' interdisciplinary approach, informed by his diverse background in physics, music, and education, positions him as a unique voice in contemporary educational discourse.

Hildegunn Viljugreinمشاهده پروفایل

پژوهشگر

• Ecology
• Evolutionary Biology
• Wildlife Diseases
• +۵ مورد دیگر

Hildegunn Viljugrein is a Researcher at the Center for Ecological and Evolutionary Synthesis (CEES) within the Department of Biosciences, University of Oslo . Her work integrates ecological modeling , wildlife disease epidemiology , and environmental change impact across terrestrial and marine systems. Research Interests : Wildlife disease dynamics with focus on chronic wasting disease in cervids and tick-borne pathogens like Borrelia burgdorferi Mathematical and statistical modeling of salmon lice infestation in aquaculture Environmental drivers of vector competence and parasite load in northern ecosystems Population genetics of wild reindeer under hunting pressure Publication Trends : Analysis of 15 recent articles reveals expertise in Lyme disease ecology , chronic wasting disease modeling , and salmon lice prediction systems . Methodologies emphasize statistical inference , Bayesian hierarchical models , and cross-species disease transmission dynamics . Labs & Collaborations : Active member of CEES's disease ecology team, collaborating with Norwegian Veterinary Institute , University of Bergen , and Nordic wildlife agencies on zoonotic and marine health initiatives.

• Digital Transformation
• Information Systems
• Technology Adoption
• +۴ مورد دیگر

Shengnan Han is a Professor in Computer and Systems Sciences (specializing in information systems) at Stockholm University since 2024. She serves as Program Director for the Bachelor program in Marketing & IT, and is a faculty member of Digital Futures . Her academic journey includes Associate Professor (2011-2024) and Assistant Professor (2005-2010) positions at Stockholm University and Åbo Akademi University respectively, where she earned her D.Sc. in information systems (2005). Current Roles: Professor, Stockholm University (2024-) Member of program council for Master program in Health Informatics Faculty member of Digital Futures Academic Service: Associate Editor, Electronic Markets Editorial Board Member, Telematics and Informatics Program Co-Chair, ECIS2023 Board Member, AIS Region 2 (2022-2024) Research Interests span technology adoption impacts through a holistic perspective covering individual behaviors, organizational performance, and societal transformations. Her cross-disciplinary collaborations involve healthcare, data science, law, and human geography. Key projects include Digital Government Collaborative Platform in Sri Lanka, DISRA for elderly care, Digital Nature in rural agriculture, and SEED for secure distance exams. Publication Trends focus on RPA implementation (10+ articles), digital proctoring systems, and rural digital studies. Her work combines technical innovation with ethical considerations like privacy and human values alignment. Teaching Responsibilities include courses on Digital Business within IT, SUPCOM for Health Informatics, and Ph.D. level computer science instruction. She supervises bachelor and master theses while maintaining an active research group in Digital Transformation at DSV.

Amit Moscovich Eigerمشاهده پروفایل

مدرس ارشد

• Geometric estimation problems
• Manifold learning
• Protein structure determination
• +۴ مورد دیگر

Dr. Amit Moscovich Eiger is a Senior Lecturer (tenure-track Assistant Professor) in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University, where he has been employed since 2021. Prior to this position, he was a Postdoctoral Research Associate in the Program in Applied and Computational Mathematics (PACM) at Princeton University (2018-2021) and a Postdoctoral Fellow at the School of Mathematical Sciences, Tel Aviv University (2017-2018). His educational background includes: Ph.D. (direct track) in the Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 2016 Physics and mathematics studies, the Hebrew University of Jerusalem, 2008 B.A. in computer science, the Open University of Israel, 2003 Dr. Moscovich Eiger's research focuses on developing methodology for statistics and machine learning, with particular emphasis on tools for mapping and analyzing large volumetric datasets. His work is motivated by key challenges in structural biology, specifically the 3D reconstruction and analysis of flexible proteins and other macromolecules from cryo-electron microscopy data. His research spans geometric estimation problems, manifold learning, computational statistics, and machine learning applications in biological imaging. Analysis of his recent publications reveals a strong focus on applying manifold learning techniques to structural biology problems, particularly cryo-EM data analysis. His work bridges theoretical statistics and practical computational methods, with significant contributions to handling heterogeneity in biological structures, developing fast algorithms for statistical computations, and creating novel approaches for analyzing protein conformations. A notable trend is the integration of optimal transport theory with manifold learning for biological applications. His scientific achievements include: Joseph Putter Ph.D. dissertation award, awarded by the Israeli Statistical Association, 2017 Dr. Moscovich Eiger is actively involved in mentoring graduate students, currently supervising multiple Ph.D. and M.Sc. students in statistics, data science, and operations research. His research is supported by prestigious funding sources including the Israel Science Foundation (ISF), the United States-Israel Binational Science Foundation (BSF), and the United States National Science Foundation (NSF). He leads a research group focused on computational methods for structural biology, particularly developing algorithms for cryo-electron microscopy data analysis. His team collaborates with researchers from institutions including Princeton University, Duke University, Hebrew University of Jerusalem, and KTH Royal Institute of Technology, creating a strong interdisciplinary network for advancing computational approaches in structural biology.

Andrew John George Cairnsمشاهده پروفایل

استاد

• Mortality and longevity risk modelling and management
• Stochastic mortality modelling
• Securitisation of mortality risk
• +۷ مورد دیگر

Andrew John George Cairns is a Professor at Heriot-Watt University, where he is affiliated with the School of Mathematical and Computer Sciences and the Department of Actuarial Mathematics & Statistics. He is well known both in the UK and internationally for his research in financial risk management for pension plans and life insurers, with a particular focus on longevity risk modeling, measurement, and management. His work extends to statistical analysis of mortality and health inequalities, understanding their drivers, and modeling interest rate risk and quantitative risk management for renewable energy operators. Editor of Insurance: Mathematics and Economics since 2023 Editor-in-Chief of ASTIN Bulletin from 2007-2017 Fellow of the Royal Society of Edinburgh since 2016 Member of the Advisory Board of the Academy for the Mathematical Sciences since 2014 Director of the Actuarial Research Centre of the Institute and Faculty of Actuaries from 2016-2020 Cairns' research interests center on risk management, particularly its impact on pension plans and life insurers. His current work focuses on three main themes: modeling and management of longevity risk and its transfer to capital markets; mortality inequalities and their drivers; and using cause-of-death mortality data to understand mortality trends and inequalities. He has made significant contributions to the field through his development of innovative stochastic mortality models, including being a co-inventor of the CBD stochastic mortality models and author of the LifeMetrics open source R code for modeling, measurement, and management of longevity risk. His recent publications demonstrate a strong focus on mortality modeling, particularly in relation to socio-economic factors, cause-of-death analysis, and the impacts of events like the COVID-19 pandemic. His work spans public health, actuarial science, and statistical modeling, with particular emphasis on mortality inequalities at neighborhood levels and the development of advanced modeling techniques like the Common Cohort Effect Model and local linear regression approaches. Edinburgh Mathematical Society Impact Prize (2021) Fellow of the Royal Society of Edinburgh (2016) Robert I. Mehr Award (2016) Halmstad Prize for 'Pricing Death: Frameworks for the Valuation and Securitization of Mortality Risk' (2008) Cairns actively supervises PhD students, with current projects focusing on mortality and longevity risk modeling and management. He has been instrumental in the ARC Longevity, Mortality and Morbidity Research Programme funded by the Actuarial Research Centre. His work contributes to UN Sustainable Development Goals, particularly in areas related to health and well-being. As an active member of the UK and international actuarial profession, he has served in numerous editorial and leadership roles, including as a Fellow of the Faculty of Actuaries since 1993 and as a corresponding member of the Swiss Association of Actuaries. His research has significant practical applications, with his CBD mortality models adopted by the international insurance and pensions industry. The improved understanding of population and mortality data generated through his work benefits both the UK and international pensions and insurance sectors, demonstrating the real-world impact of his academic contributions.

Matthieu Marbac-Lourdelleمشاهده پروفایل

دانشیار

• Biostatistics
• Model-based Clustering
• Computational Statistics
• +۲ مورد دیگر

Matthieu Marbac-Lourdelle is an Associate Professor in Statistics at ENSAI/CREST (Bruz, France) since September 2023. Previously, he served as an Assistant Professor at the same institution from September 2017 to September 2023. He is also an external collaborator of the PreMedical team, a joint team between Inria and Inserm, since September 2022. He completed his PhD in October 2014 at the University of Lille 1 under the supervision of Christophe Biernacki and Vincent Vandewalle. In 2022, he defended his habilitation, the French qualification required to supervise PhD students. Dr. Marbac-Lourdelle's research focuses on Biostatistics , Model-based Clustering , Computational Statistics , Empirical Likelihood , and Mixture Models . His work bridges theoretical statistical methods with practical applications in various domains including epidemiology, sports science, and genomics. His recent publications reveal a strong emphasis on developing advanced clustering techniques, particularly for complex data structures including functional data, mixed data types, and data with missing values. His research has significant applications in health monitoring, epidemiological studies, and sports analytics. Dr. Marbac-Lourdelle serves as an Associate Editor for Computational Statistics & Data Analysis (since 2021) and Econometric and Statistics (since 2023). He currently supervises five PhD students working on diverse topics including nonparametric mixture models, soccer game analysis, swimming technique monitoring, arrhythmia detection in racehorses, and probabilistic substitution models. His collaborations span multiple institutions including CREST, Inria, Inserm, and international partners at HEC Montreal and the University of McMaster.

Austin J. Strommeمشاهده پروفایل

استادیار

• Optimal Transport
• High-Dimensional Statistics
• Geometry
• +۲ مورد دیگر

Austin J. Stromme is an Assistant Professor in the Statistics Department at ENSAE Paris, where he serves as a permanent CREST member. His research bridges optimization, geometry, and probability with a focus on statistical aspects of optimal transport, positioning him at the forefront of theoretical machine learning and high-dimensional statistics. Stromme earned his PhD in Electrical Engineering and Computer Science from MIT under Guy Bresler and Philippe Rigollet. His academic journey reflects deep engagement with mathematical foundations of data science, emphasizing rigorous theoretical frameworks for complex statistical problems. His primary research spans entropic optimal transport, high-dimensional inference, and geometric methods in machine learning. Stromme investigates fundamental questions about statistical efficiency, computational complexity, and probabilistic structures in large-scale data analysis. His work reveals critical connections between functional inequalities, sampling algorithms, and manifold geometry, with applications spanning trajectory reconstruction, covariance estimation, and non-convex optimization. Recent publications (2023-2025) demonstrate accelerating impact in theoretical statistics, particularly in establishing sample complexity bounds and convergence rates for entropic optimal transport. This body of work shows increasing sophistication in handling high-dimensional settings while maintaining computational feasibility, reflecting broader trends toward unifying geometric intuition with statistical rigor in modern machine learning. Stromme received the Best Paper Award at the 2024 International Conference on Soft Methods in Probability and Statistics for his breakthrough on intrinsic dimension scaling in entropic optimal transport. This recognition highlights his contributions to advancing theoretical understanding of dimensionality challenges in statistical learning. As an emerging faculty leader, Stromme co-organizes an influential online seminar series bridging statistics and geometry, fostering interdisciplinary collaboration. While no current PhD students are listed, his active seminar leadership and recent faculty appointment indicate growing mentorship responsibilities. His research is supported through CREST's institutional framework, which facilitates collaborations across mathematics, economics, and data science. Stromme operates within CREST's vibrant research ecosystem at ENSAE Paris, contributing to a community that integrates advanced statistical theory with real-world economic applications. His work exemplifies the center's commitment to methodological innovation in quantitative social sciences and data-driven decision making.

Jiankun Huمشاهده پروفایل

استاد

• Cyber Security
• Intrusion Detection
• Applied Cryptography
• +۱۶ مورد دیگر

Professor Jiankun Hu is an IEEE Fellow and full professor of Cyber Security at the School of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA), Australia. His primary research focuses on computer networking and security, particularly biometric security and cybersecurity applications. Professor Hu earned his Bachelor's degree in industrial automation in 1983 from Hunan University, China, followed by a PhD in engineering in 1993 from Harbin Institute of Technology, China. He completed a Master's degree in computer science and software engineering from Monash University, Australia in 2000. His postdoctoral work includes research at Harbin Shipbuilding College (1995), Ruhr University in Germany as an Alexander von Humboldt Foundation fellow (1995-1997), and Delft University of Technology in the Netherlands (1997). His research spans multiple domains including cybersecurity, biometrics, image processing, and artificial intelligence. Key areas include intrusion detection systems, applied cryptography, network security, biometrics security, image and video processing, and machine learning applications. He has made significant contributions to fingerprint recognition systems, privacy-preserving biometric authentication, and cybersecurity for smart grids. Professor Hu's recent publications demonstrate a strong focus on privacy-preserving technologies for biometric systems, blockchain security, smart grid protection, and advanced machine learning applications for cybersecurity. His work bridges theoretical advances with practical implementations in real-world systems, particularly in securing critical infrastructure and developing robust biometric authentication methods. He has received significant recognition including IEEE Fellow status and has been awarded ten Australian Research Council Grants. He has served in editorial roles for prestigious journals including IEEE TIFS (as Senior Area Editor), Journal of Security and Communication Networks, KSII TIIS, IET CPS, and IEEE OJCS. Professor Hu has held leadership positions in major conferences, serving as Security Symposium Co-Chair for IEEE GLOBECOM '08 and IEEE ICC '09, and Program Co-Chair of the 2008 International Symposium on Computer Science and its Applications. He maintains an active research program with numerous publications across top-tier venues.

• Machine Learning
• Deep Learning
• Recommender Systems
• +۷ مورد دیگر

Dr. Ruihai Dong is an Assistant Professor at the School of Computer Science, University College Dublin, affiliated with the Insight Centre for Data Analytics. His research bridges Machine Learning, Deep Learning, and Recommender Systems with significant applications in finance and medical imaging. Develops AI-driven solutions for financial markets and healthcare Collaborates with industry partners like Eagle Alpha and Samsung Funded by Enterprise Ireland for commercialization studies Research focuses on: Recommender systems (news, finance, conversational AI) Graph neural networks for financial risk modeling Medical imaging under limited labeling constraints Algorithmic transparency and ethical AI applications 3D CNNs & transformers for seismic analysis Large language models for multilingual evaluation Recent publications emphasize: Hybrid architectures for multimodal data Explainable AI techniques in financial and news domains Geometric deep learning for microstructure and seismic data Curriculum strategies in active learning Media bias mitigation frameworks Latent space modeling for asset embeddings Scientific contributions recognized through: Outstanding Research Award 2018 by UCD School of Computer Science Industry collaborations include work with: Eagle Alpha (financial analytics) SkillPages (data-driven platforms) Samsung (technology innovation)

• Linguistics
• Computational Linguistics
• Cognitive Science
• +۷ مورد دیگر

Aniello De Santo is an Assistant Professor in the Department of Linguistics at the University of Utah, where he has been serving since July 2020. He earned his PhD in Linguistics from Stony Brook University in 2020 and is fluent in Italian, which informs his research on Italian syntax and processing. His research spans multiple disciplines with key interests in: Computational Linguistics and Syntax Cognitive Science and Theoretical Frameworks Artificial Intelligence and Machine Learning Applications Formal Language Theory and Complexity Psycholinguistic Processing Models De Santo's scholarly work demonstrates remarkable interdisciplinary integration, with publications in top journals across linguistics, cognitive science, and computer science. His recent work focuses on theoretical foundations in cognitive science (2024 publications on plausibility criteria and theoretical virtues), empirical investigations of Italian relative clause processing (2024), and innovative applications of machine learning techniques to linguistic problems. His research often bridges formal theoretical analysis with computational modeling and empirical validation. He has developed innovative educational approaches including an asynchronous general-education course "Language in the United States" (2023) and teaches a diverse range of courses in computational linguistics, syntax, and language studies. His extensive teaching portfolio includes Computers & Language, Introduction to Syntax, Computational Linguistics, and various research/thesis courses at both undergraduate and graduate levels. De Santo is actively engaged in community outreach as an organizer of the North American Computational Linguistics Open Competition (NACLO) for high school students, conducting practice sessions and hosting competition rounds since 2022. This work connects academic linguistics with pre-college education and demonstrates his commitment to promoting computational thinking in language studies.

Egon Perilliمشاهده پروفایل

دانشیار

• 2D/3D quantitative imaging of bone structure and biomaterials
• Micro-computed tomography (micro-CT)
• Digital volume correlation (DVC)
• +۶ مورد دیگر

Associate Professor Egon Perilli is a biomedical engineer and imaging scientist based at Flinders University’s Medical Device Research Institute (College of Science and Engineering). He additionally holds an Affiliate Lecturer appointment in Anatomy & Pathology at the University of Adelaide and an Honorary Fellowship in Medicine at the University of Melbourne. His career spans prestigious European institutions—University of Antwerp and Istituti Ortopedici Rizzoli in Bologna—before establishing a prominent research programme in Australia. Education & Training Post-Doctoral Researcher, SkyScan (now Bruker micro-CT) & Visionlab, University of Antwerp, Belgium, 2007–2008 Scientific Researcher, Medical Technology Laboratory, Istituti Ortopedici Rizzoli, Bologna, Italy, 2002–2007 Research Focus Associate Professor Perilli’s work integrates state-of-the-art in vitro and in vivo micro-computed tomography with digital volume correlation and experimental biomechanics to investigate bone structure–function relationships. His primary application areas are osteoporosis and osteoarthritis, where he quantifies micro-architectural deterioration, evaluates implant fixation strategies, and explores 3-D-printed biomaterials. A particular emphasis is placed on translating high-resolution imaging data into predictive models of fracture risk and implant longevity. Recent Publication Trends Between 2019 and 2025, his output reveals an intensified focus on (i) cementless orthopaedic implants and their mechanical environment, (ii) time-elapsed micro-CT to capture failure mechanisms under physiological loads, and (iii) additive-manufactured Ti–6Al–4V components with controlled porosity. These works collectively advance personalised orthopaedics and evidence-based implant design. Awards & Leadership Past President (2018-2021), Australian & New Zealand Orthopaedic Research Society (ANZORS) ANZORS Secretary (2015-2018) Council Member, International Federation of Musculoskeletal Research Societies (IFMRS), since 2018 Supervision & Grants He currently supervises PhD and Honours projects in bone biomechanics, micro-CT instrumentation, and osteoarthritis. His mentees have garnered major accolades, including ANZORS PhD Oral Presentation Awards, Best Student Publication Awards, Commonwealth Scholarships, and international travel bursaries from ESB and WCB. He reviews for ARC, NHMRC, Wellcome Trust, and leading orthopaedic journals, indicating sustained grant and editorial engagement. Laboratory & Collaborations His team operates within the Flinders Medical Device Research Institute, leveraging micro-CT systems, mechanical testing rigs, and custom software for digital volume correlation. Active collaborations span national synchrotron facilities and international partners in Belgium, Italy, and the United States, fostering a multidisciplinary environment that bridges engineering, medicine, and materials science.

Hannes Feilhauerمشاهده پروفایل

استاد

• Remote Sensing
• Biodiversity
• Species Distribution Patterns
• +۷ مورد دیگر

Hannes Feilhauer is a full Professor for Remote Sensing in Geo- and Ecosystem Research at the Remote Sensing Centre for Earth System Research (RSC4Earth), which is a joint initiative of the Faculty of Physics and Earth System Sciences at Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). His work focuses on the spatio-temporal analysis of species distribution patterns, functional ecosystem properties, ecosystem services, and biodiversity using optical remote sensing sensors, field measurements, and modeling techniques. His educational background includes a PhD in Geography from Bonn University (2009) and a Diploma in Geoecology from Bayreuth University (2006). His academic career path includes positions as Visiting Professor for Remote Sensing and Geoinformatics at the Free University of Berlin (2018), Senior Scientist at the Institute of Geography, Friedrich-Alexander Universität Erlangen-Nürnberg (2010-2018), and various research positions dating back to 2006. Feilhauer's research primarily investigates the application of remote sensing for ecological analysis, with particular emphasis on mapping fuzzy ecosystem characteristics that cannot be adequately represented in conventional maps. His work bridges the gap between spectral data and ecological patterns, developing methods to map continuous fields of biodiversity and ecosystem properties. He has made significant contributions to understanding the relationship between spectral variation and biodiversity, the application of optical trait indicators, and the mapping of habitat variability. His recent publications reveal a strong focus on advancing remote sensing applications for biodiversity monitoring, with increasing integration of machine learning approaches and analysis of high-resolution satellite data (particularly Sentinel-2). His work spans from fundamental methodological developments in spectral analysis to applied ecosystem monitoring, with particular attention to grassland ecosystems, species distribution patterns, and the impacts of environmental change. Science award for Physical Geography of the Prof. Dr. Frithjof Voss Stiftung für Geographie Feilhauer leads a research group that includes multiple PhD candidates and postdoctoral scientists working on projects related to vegetation monitoring, biodiversity assessment, and remote sensing methodology development. His group is involved in several major research initiatives including AIReSVeg (AI and Remote Sensing for Vegetation Monitoring), Jena Experiment, and DeepFeatures, reflecting his engagement with both fundamental ecological research and cutting-edge technical developments in remote sensing. The Remote Sensing in Geo- and Ecosystem Research group operates within the RSC4Earth framework, collaborating closely with researchers from the Earth System Data Science group and the Remote Sensing Department at UFZ. This collaborative environment enables integration of remote sensing data with ecological modeling and Earth system science approaches.