جستجوی استاد راهنمای دانشگاهی

• Geotechnical Engineering
• Slope Stability
• Earthquake Engineering
• +۷ مورد دیگر

Ernesto Ausilio is an Associate Professor in the Department of Civil Engineering at the University of Calabria, Italy. He is actively engaged in teaching and research, with a strong focus on geotechnical engineering and seismic risk assessment. His academic work spans numerical modeling, landslide analysis, soil-structure interaction, and geotechnical monitoring of critical infrastructure. Research Interests: His primary research areas include slope stability, earthquake-induced landslides, seismic ground motion analysis, and geotechnical modeling for infrastructure resilience. He employs advanced numerical methods and data-driven approaches such as machine learning and time-frequency analysis to study complex geotechnical phenomena. The recent publications highlight a consistent trend in applying innovative computational techniques—including metaheuristic algorithms and random forest models—to solve practical geotechnical problems related to seismic stability, liquefaction, and infrastructure safety. His work often involves case studies from Southern Italy and Japan, emphasizing real-world applicability and multidisciplinary approaches. Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals and conferences reflects significant scholarly contributions to geotechnical earthquake engineering. Teaching and Mentorship: He teaches courses such as Geotechnical Engineering and Geotechnical Design at both undergraduate and graduate levels. Although no students are listed, his role in research groups suggests advisory and collaborative mentorship. He is involved in research projects and supervises laboratory activities related to geotechnics. Laboratories and Research Groups: He is a member of the Geotechnical Engineering Research Group, focusing on porous media mechanics, numerical modeling of slope failure, seismic response analysis, and ground improvement techniques. He is associated with the Geotecnica Laboratory within the Department of Civil Engineering, supporting research, teaching, and technical services.

Riccardo Di Francescoمشاهده پروفایل

پژوهشگر

• Econometrics
• Causal Inference
• Statistical Learning
• +۴ مورد دیگر

Riccardo Di Francesco is a Postdoctoral Researcher in the Department of Economics at the University of Southern Denmark, Faculty of Social Sciences. His work bridges econometrics, causal inference, and statistical learning, with a focus on innovative methods for applied microeconomic analysis. His research interests include: Econometrics Causal Inference Statistical Learning Applied Microeconomics Machine Learning in Economics Covariate Modeling Distributional Analysis His recent publication introduces the Ordered Correlation Forest, a novel machine learning technique enhancing variable importance and conditional probability estimation in econometric modeling. This reflects a broader trend in his work of integrating advanced statistical learning tools into traditional econometric frameworks to improve robustness and interpretability. He is currently involved in the research project Beyond the Paycheck: Insights into the effect of Unemployment, Organisational Change, and Job Security on Mental Health , funded by private foundations and running from 2024 to 2027. This project explores the socio-economic determinants of mental health, aligning with his expertise in causal inference and applied microeconomics. Riccardo Di Francesco contributes to impactful interdisciplinary research at the intersection of data science and economics, with a strong methodological foundation and real-world policy relevance.

• Climate Science
• Environmental Engineering
• Sustainable Materials
• +۵ مورد دیگر

Parthiban Loganathan is a Postdoctoral Research Fellow at the School of Aerospace, Mobility, and Naval Architecture, KTH Royal Institute of Technology. His research focuses on interdisciplinary environmental and engineering challenges, including climate modeling, sustainable materials development, and remote sensing applications. He has contributed to studies on aviation-induced climate effects, solar energy efficiency, and geopolymer concrete optimization, leveraging machine learning for predictive modeling and material performance analysis. Key research areas include: Climate projections and statistical downscaling for river basins (e.g., Cauvery Basin) Environmental remediation via phytoremediation and constructed wetlands Aviation emissions and contrail radiative forcing quantification Material durability studies for sustainable infrastructure Landslide identification using SAR interferometry His work demonstrates a strong emphasis on data-driven methodologies for addressing global environmental challenges, with publications spanning climate science, environmental engineering, and materials innovation.

Ronny Hänschمشاهده پروفایل

پژوهشگر

• Semantic analysis of SAR data
• Deep learning
• Self-supervised learning
• +۷ مورد دیگر

Ronny Hänsch is a Researcher at the Microwaves and Radar Institute of the German Aerospace Center (DLR) in Weßling, Germany. His work focuses on advanced computer vision and remote sensing techniques applied to Earth observation data. Research interests include semantic analysis of SAR data, deep learning methodologies (including self-supervised learning and xAI), prediction uncertainty estimation, random forests/ensemble learning, and digital image processing innovations. He actively explores hot topics in computer vision for geospatial applications. Professional contact information: rww.haensch@gmail.com | +49 8153-283975

• Explainable AI
• Machine Learning
• Software Verification
• +۶ مورد دیگر

Alnis Murtovi is an Assistant Professor at TU Dortmund University's Faculty of Computer Science, Department of Software Engineering. They are a prominent researcher in the fields of explainable AI, machine learning, and software verification, collaborating extensively with Professor Bernhard Steffen and other colleagues at the university. Dr. Murtovi's research focuses on making machine learning models more interpretable and verifiable, particularly through work on decision trees, random forests, and neural networks. Their recent publications demonstrate a strong emphasis on developing practical tools and compilation-based approaches to explain complex models while maintaining performance. The research spans theoretical foundations in formal methods to practical implementations like the Forest GUMP tool for verification and explanation. The publication trend shows a consistent output with increasing focus on explainable AI in recent years, with significant contributions in 2023-2025. Their work bridges the gap between theoretical computer science and practical machine learning applications, particularly in verification techniques for AI models. The research has strong connections to software engineering principles and formal methods. Dr. Murtovi has made substantial contributions to the field through numerous publications in top venues including TACAS, STTT, and various conference proceedings. Their work on Forest GUMP represents a significant practical contribution to the community for model verification and explanation. As part of the research group led by Bernhard Steffen at TU Dortmund, Dr. Murtovi contributes to interdisciplinary projects that combine language-driven engineering approaches with practical software development challenges. Their research has implications for both academic and industrial applications where model interpretability and verification are critical requirements.

Ege Yazganمشاهده پروفایل

استاد

• Applied Econometrics
• Monetary Policy
• International Finance
• +۷ مورد دیگر

Prof. Ege Yazgan is a full Professor of Economics at Istanbul Bilgi University , where he serves in multiple leadership roles including Interim Rector and Director of the Center for Financial Studies (CEFIS). He is affiliated with the Faculty of Business Administration and the Department of Economics. His academic work spans econometrics, macroeconomics, and financial economics, with a strong focus on Turkish economic policy. PhD, University of Sussex (2000) MA, Istanbul University and Boğaziçi University BA, Faculty of Economics, Istanbul University (1991) His research interests include Applied Econometrics , Monetary Policy , International Finance , GDP Nowcasting , and Financial Cycles . He is the founder of simditahmin.com , a real-time Turkish GDP forecasting platform, and frequently uses high-frequency and big data in his models. He has published extensively in journals such as International Journal of Forecasting , Empirical Economics , and Journal of Business Cycle Research . The recent trends in his publications show a growing emphasis on nowcasting using alternative data sources (e.g., online prices, financial transactions), machine learning applications in macroeconomic forecasting, and nonlinear modeling of financial and business cycles in emerging markets, particularly Turkey. Scientific contributions and outreach: Regular commentator on macroeconomic issues in Turkish media (CNBC-e, Bloomberg, NTV, TRT) Organizer of major conferences in macro-finance and econometrics Active participant in policy debates on inflation, external deficits, and financial stability Advising and grants: He has advised multiple researchers and co-authored numerous papers with junior colleagues such as B. Soybilgen and H. Kaya. He has led research projects on forecasting and financial studies, supported through institutional and academic grants, though specific grant names are not listed. He is a member of several research centers including CEFIS, Murat Sertel Center, Rimini Center, and Economic Research Forum. Labs and teams: He leads the Center for Information Finance Research (CEFIS) at Istanbul Bilgi University, which focuses on financial data analysis, econometric modeling, and macroeconomic forecasting. The center organizes workshops and collaborates with international scholars in nonlinear dynamics and econometrics.

Luc Devroyeمشاهده پروفایل

استاد

• Computer Science
• Probability Theory
• Algorithms
• +۴ مورد دیگر

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.

Kai C. Rainsمشاهده پروفایل

دانشگاهی

• Ecohydrology
• Wetland Ecology
• Environmental Stress Response
• +۳ مورد دیگر

Kai C. Rains is a Research Associate Professor in the School of Geosciences at the University of South Florida. He holds a B.S. from Oregon State University, an M.S. from the University of Washington, and a Ph.D. from the University of California, Davis. His research focuses on ecohydrology, particularly ecosystem responses to environmental stressors like inundation and nutrient limitations. Key areas include geospatial analysis of wetlands/waters, mycorrhizal associations, and groundwater-surface water interactions. Education: B.S., Oregon State University M.S., University of Washington Ph.D., University of California, Davis Research emphasizes hydrological connectivity across landscapes, with applications to water policy and natural resource management. Major contributions include remote sensing for groundwater discharge mapping and machine learning applications in environmental monitoring. He co-leads the Ecohydrology Research Group, focusing on ridge-to-reef connectivity and policy-relevant science.

• Remote Sensing
• Water Biogeochemistry
• Harmful Algal Blooms
• +۷ مورد دیگر

Padmanava Dash is an Associate Professor in the Department of Geosciences at Mississippi State University (MSU), specializing in remote sensing and water biogeochemistry. His work integrates satellite, unmanned aerial systems (UAS), and autonomous surface vessels to monitor and model water quality parameters such as harmful algal blooms, suspended sediments, nutrients, and heavy metals. He leads the development of web-based visualization tools for water quality management and supports federal, state, and coastal community initiatives. Education Ph.D. in Oceanography and Coastal Sciences, Louisiana State University, 2011 M.S. in Geology, Bowling Green State University, 2005 Research Interests Dr. Dash’s research is centered on understanding the drivers and consequences of water quality degradation in aquatic systems. Using multi-scale remote sensing techniques—from handheld spectrometers to satellites—he quantifies spatiotemporal patterns of phytoplankton blooms, dissolved organic matter, acidification, and trace metals. Advanced machine-learning algorithms are routinely integrated to improve retrieval accuracy and predictive capacity, enabling early warning systems for human and ecological health risks. A significant component of his portfolio involves the design and deployment of autonomous platforms (UAS and ASV) for high-resolution, real-time data acquisition. These efforts feed into web portals that translate complex geospatial data into intuitive maps and dashboards for stakeholders, thereby bridging the gap between science and policy. Publication Trends Between 2024 and 2025, Dr. Dash has published extensively on three converging themes: (1) machine-learning-enhanced retrieval of water quality parameters from satellite and UAS imagery, (2) geochemical immobilization of iodine and uranium using phosphate minerals for nuclear waste management, and (3) physical-biological interactions in marine systems such as mesoscale eddies and hypoxia in the Gulf of Mexico and Bay of Bengal. The work spans freshwater lagoons, estuaries, and coastal oceans, demonstrating a commitment to transdisciplinary, data-driven environmental science. Scientific Awards No specific awards are listed in the provided materials. Advising & Funding While current Ph.D. or master’s students are not named in the text, Dr. Dash’s active laboratory and consistent publication record suggest ongoing mentoring and grant support. External collaborations span federal agencies (NASA, NOAA), state departments of environmental quality, and international partners in India’s Chilika Lagoon. Laboratory & Field Infrastructure Dr. Dash operates the Remote Sensing and Water Quality Lab at MSU, equipped with hyperspectral sensors, UAS platforms, autonomous surface vessels, and high-performance computing resources. The lab maintains two public web tools for real-time water quality visualization accessible at water.geosci.msstate.edu .

Raimund Kirnerمشاهده پروفایل

استاد

• Parallel Computing
• Real-Time Systems
• Embedded Systems
• +۵ مورد دیگر

Dr. Raimund Kirner is a Reader in Cyberphysical Systems at the University of Hertfordshire, UK. His research focuses on embedded computing, parallel computing, and real-time systems, with notable contributions to worst-case execution time (WCET) analysis, compiler design for predictability, and cybersecurity. He leads several projects, including the EU-funded CRAFTERS initiative and the FWF-supported SECCO and FORTAS-rt projects. Kirner holds a PhD from TU Vienna (2003) and a Habilitation (2010). He has authored over 100 publications and holds two patents. His work bridges many-core and embedded systems, emphasizing reliability and predictability. Education: PhD in Computer Science, TU Vienna, 2003 Habilitation, TU Vienna, 2010 Research Interests: Kirner's research spans parallel computing architectures, WCET analysis for real-time systems, compiler optimizations for predictability, and cybersecurity in embedded systems. He explores machine learning applications for medical diagnostics (e.g., EEG-based Alzheimer detection) and quantum cryptography frameworks. His work on mixed-criticality systems aims to enhance reliability in automotive and aerospace domains. Grants and Projects: Principal Investigator (CRAFTERS): EU Artemis-JU project addressing multi-core predictability (2012–2015) Co-Investigator (ADVANCE): FP7 project optimizing parallel programs (2012–2015) FWF-funded projects: SECCO (coverage preservation), FORTAS-rt (WCET measurement), and CoSTA (compiler timing predictability) Awards: Two patents granted for innovations in cybersecurity and embedded systems Labs and Teams: Kirner contributes to the Centre for AI and Robotics Research and the Cybersecurity and Computing Systems Group at the University of Hertfordshire. His collaborations include projects with TU Vienna and industry partners like the Technology Strategy Board. Advising: He has supervised one doctoral student, as documented in his Scopus profile, and oversees interdisciplinary research teams in cyber-physical systems.

Kee Kimمشاهده پروفایل

دانشیار

• Edge Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

In Kee Kim is an Associate Professor at the University of Georgia, specializing in interdisciplinary research at the intersection of computing systems, edge computing, and health informatics. His work focuses on optimizing resource utilization in distributed systems, advancing cybersecurity in smart environments, and analyzing behavioral trends through social media platforms. He holds dual appointments in advanced computing and engineering disciplines. Kim's research portfolio includes groundbreaking contributions to cloud and edge computing resource management, with notable work on predictive modeling for workload forecasting, energy-efficient IoT systems, and secure smart home architectures. His recent studies explore the societal implications of online behavior, including analyzing self-harm trends on platforms like Reddit and Instagram. His technical innovations span multiple domains: from developing systems like CloudBruno for cloud workload prediction to creating low-cost environmental monitoring frameworks for coastal ecosystems. He also pioneered tools like CNT for genomic workflow automation and Edgefaasbench for edge benchmarking. His cybersecurity investigations into smart home vulnerabilities highlight proactive approaches to securing personal area networks. Kim maintains an active research lab at the Boyd Graduate Studies Research Center and has published extensively in top-tier conferences and journals since 2005. His work bridges theoretical advancements with practical implementations, addressing real-world challenges in distributed systems and digital health.

Sheng-I Yangمشاهده پروفایل

استادیار

• Forest Biometrics
• Applied Statistics
• Forest Inventory
• +۳ مورد دیگر

Sheng-I Yang is an Assistant Professor of Forest Biometrics at the University of Georgia. He specializes in quantitative methods for forest growth modeling, stand dynamics, and inventory systems. His work integrates applied statistics, remote sensing, and geospatial technologies to inform sustainable forest management. He currently coordinates IUFRO's 4.01.03 working party and serves as an Associate Editor for the Journal of Forestry and Forest Ecosystems . Education: B.S. Forestry, National Taiwan University (2013) M.S./Ph.D. Forest Biometrics & Statistics, Virginia Tech (2016-2019) Affiliations: Plantation Management Research Cooperative (PMRC) His research focuses on forest biometrics , including stem taper modeling, stand carrying capacity estimation, and tree allometry. Recent projects involve predicting Caribbean tree growth via machine learning and optimizing pine plantation management in Turkey. He develops novel statistical frameworks for integrating Sentinel satellite data with ground-based inventories. His 2023-2025 publications emphasize tropical forest modeling, bark volume quantification, and elk population dynamics collaborations. Major funders include USDA Forest Service and the NCASI Foundation. Awards: 2024 Early Career Achievements Award (Northeastern/Southern Mensurationists Conference) 2023 Early Career Investigator Award (Forests Journal) Teaching responsibilities include Timber Management and Statistical Software for Natural Resources . His lab actively collaborates with IUFRO networks to advance global forest mensuration standards.

Alan Antonovićمشاهده پروفایل

دانشیار

• Lignocellulosic Biomass Utilization
• Biorefinery Technologies
• Wood Fibre and Paper Engineering
• +۳ مورد دیگر

Full Associate Professor Alan Antonović holds a position at the University of Zagreb's Faculty of Forestry and Wood Technology, Department of Wood Technology. He has been affiliated with the institution since 1999, progressing through roles from junior assistant to his current rank. His expertise centers on lignocellulosic biomass utilization, biorefinery technologies, and sustainable materials development. Research Interests: Dr. Antonović's work focuses on chemical composition of biomass, wood fiber applications, biorefinery processes for biofuel production, and novel biopolymer systems derived from liquefied biomass. Recent projects emphasize valorizing agricultural and forestry waste into high-value materials. Teaching: Teaches courses including Wood Biorefinery Technologies and Wood Fibers and Paper Technology. Fluent in Croatian and English, he provides consultations daily between 08:00-15:00 at IV Pavilion, 2nd floor, room 244. Grants & Collaborations: Active in EU-funded projects on biomass conversion and participates in international conferences. His lab focuses on optimizing biomass preprocessing, biofuel quality enhancement, and sustainable material design. Labs/Teams: Leads the Wood Technology Research Group, collaborating with industry partners to scale-up biorefinery prototypes and bio-based materials for construction and packaging sectors.

Charles D. Nicholsonمشاهده پروفایل

دانشیار

• Community Resilience
• Predictive Modeling
• Optimization
• +۴ مورد دیگر

Charles D. Nicholson is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Oklahoma's Gallogly College of Engineering. He serves as Director of the Data Science and Analytics Institute, leading interdisciplinary efforts in community resilience and predictive modeling. Nicholson holds a Ph.D. in Operations Research from Southern Methodist University and dual B.S. degrees in Physics and Mathematics from the University of North Texas. His research focuses on optimizing disaster mitigation strategies through systems engineering and data science. Key areas include enhancing community resilience against natural hazards, improving public health practices (domestically and internationally), and securing national supply chains. Methodologically, his work integrates metaheuristics, predictive modeling, and network analysis. Recent research trends show strong emphasis on: Covid-19 forecasting with county-level clustering Flood-resilient infrastructure design Causal discovery methods for root cause analysis Multi-objective optimization for building mitigation He received the 2017 Teaching Scholars Award from the Gallogly College. Nicholson advises the Data Science and Analytics Student Organization and chairs the Institute's Executive Committee. His work bridges academic research with real-world impact through collaborations with government agencies and industry partners. Notable contributions include: Developing resilience frameworks for cyber-physical-social systems Advancing hurricane impact prediction models Optimizing maritime supply chain recovery strategies Labs/Teams: Leads the Data Science & Analytics Institute's research initiatives focusing on complex systems analysis and decision support systems.

Gero Szepannekمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistics
• +۴ مورد دیگر

Prof. Dr. rer. nat. Gero Szepannek is a Professor at the Faculty of Business at Stralsund University of Applied Sciences, specializing in Statistics, Business Mathematics, and Machine Learning. He teaches courses such as Analytical Statistics, Exploratory Data Analysis, and Data Science for Business. His research focuses on clustering algorithms for mixed-type data, machine learning interpretability, credit scoring models, and educational technology. Notable contributions include work on k-prototypes initialization strategies, explainable AI, and R package development for data science (e.g., clustMixType , mlr3shiny ). Prof. Szepannek organizes the European Conference on Data Analysis (ECDA2026) and participates in academic committees such as the GfKl. His work emphasizes practical applications in finance, healthcare, and education. He advises students on thesis topics in Machine Learning/Data Science/Statistics and engages in R User communities.