جست‌وجوی استاد و پژوهشگر

Boris Kozyrevمشاهده پروفایل

پژوهشگر

• Macroeconomics
• Forecasting
• Applied Econometrics
• +۳ مورد دیگر

Boris Kozyrev is a Researcher in the Department of Macroeconomics at the Halle Institute for Economic Research (IWH) since September 2021. He holds a bachelor's degree from Lomonosov Moscow State University and a master's degree from Toulouse School of Economics. Previously, he worked as a Junior Researcher at the Russian Presidential Academy of National Economy and Public Administration. His research focuses on macroeconomic forecasting, applied econometrics, and computational economics. Key contributions include developing random subspace regression methods for forecast aggregation and applying neural networks to GDP prediction in volatile environments. His work emphasizes model interpretability and handling high-dimensional data, with applications to German and global economic trends. Recent publications explore topics such as forecast combination techniques neural network-based GDP prediction energy crisis impacts on Germany structural challenges in post-pandemic recovery These articles highlight his expertise in quantitative modeling and real-world economic analysis. His affiliations include collaborations with institutions like the IWH and prior work at the Russian Presidential Academy. Research interests intersect with policy analysis, particularly in energy and trade sectors affecting Germany's economy.

Naim Celiniمشاهده پروفایل

پژوهشگر

• Petroleum Geology
• Reservoir Characterization
• Environmental Geology
• +۶ مورد دیگر

Dr. Naim Celini is a Researcher at the Bureau of Economic Geology , University of Texas at Austin. His work bridges geology, energy recovery, and environmental sustainability, with a focus on carbon sequestration, oil and gas reservoir characterization, and groundwater systems. His research spans Permian Basin studies , CO2 storage , enhanced oil recovery , and hydrological modeling . Recent projects include optimizing carbon capture methods via formate-based injection, analyzing induced seismicity in the Midland Basin, and exploring coastal river system modifications. Key trends in his publications highlight interdisciplinary approaches to energy-environment nexus challenges, integrating machine learning with satellite data for soil moisture nowcasting, and modeling mobile shale geomechanics for subsurface stability.

Peter Fienerمشاهده پروفایل

استاد

• Soil Erosion
• Surface Runoff
• Water and Soil Resource Management
• +۷ مورد دیگر

Peter Fiener serves as Professor for Water and Soil Resource Research at the Department of Geography, Faculty of Applied Computer Sciences, University of Augsburg since 2013. He concurrently holds the position of Dean of Students in the Department of Geography (since 2016) and coordinates the Marie Skłodowska-Curie Innovative Training Network SOPLAS (since 2021). His academic journey includes prior appointments as Professor at the Indo-German Centre for Sustainability, Indian Institute of Technology Madras (2011-2012) and Senior Scientist at the University of Cologne (2004-2013). PhD from Technische Universität München (2003) Habilitation from University of Cologne (2010) Master's in Geography from Universität Augsburg and LMU München (1998) Professor Fiener's research program focuses on measuring and modeling surface runoff, soil erosion, and lateral matter fluxes in agricultural landscapes. He investigates the effects of spatio-temporal patterns and connectivity on water and matter movement, coupling these processes with biogeochemical cycles in terrestrial and aquatic ecosystems. A significant recent emphasis has been on macro and microplastics in arable soil systems, particularly their redistribution mechanisms. His geographical research areas of specialization include South India and Central Africa, where he has conducted extensive field studies on sustainable agricultural practices. Analysis of his 2024-2025 publications reveals a strong research trajectory toward microplastic detection methodologies, advanced soil erosion modeling using machine learning, and carbon dynamics in eroding landscapes. His work increasingly integrates remote sensing technologies with computational approaches to address large-scale soil conservation challenges, demonstrating particular innovation in plasticulture detection using satellite data and erosion nowcasting systems. Coordinator of Marie Skłodowska-Curie Innovative Training Network SOPLAS Professor Fiener leads the Water and Soil Resource Research Team comprising multiple doctoral researchers including Dr. Florian Wilken, Dr. Pedro Batista, and Dr. Lena Katharina Öttl, alongside numerous M.Sc. researchers. His M²ESH laboratory supports advanced measurement and modeling of soil hydrological processes. Current research directions include understanding tillage erosion's role in carbon dynamics, developing cost-effective microplastic detection protocols, and assessing long-term impacts of agricultural practices on landscape evolution across multiple temporal scales.

Oliver Holtemöllerمشاهده پروفایل

استاد

• Quantitative Macroeconomics
• Business Cycles and Forecasting
• Applied Econometrics
• +۴ مورد دیگر

Oliver Holtemöller is Professor of Economics at Martin Luther University Halle-Wittenberg and Deputy President of the Leibniz Institute for Economic Research Halle (IWH), where he heads the Macroeconomics Department. His academic trajectory includes a PhD from Freie Universität Berlin (2001) and prior positions as a research assistant at Humboldt-Universität zu Berlin (2001–2003) and Junior Professor at RWTH Aachen (2003–2009). His research encompasses: Core methodologies : Quantitative macroeconomics, econometric modeling, and time-series forecasting Policy-focused domains : Business cycle analysis, monetary policy design, environmental macroeconomics, and labor market dynamics Emerging interests : Neural network applications in economic forecasting and ecological transition impacts Recent publications (2021–2025) demonstrate a strong focus on policy-relevant quantitative analysis, including Brexit employment effects, German coal phase-out economics, pan-European business cycle forecasting, and pension system reforms. Methodologically, his work integrates DSGE modeling, input-output analysis, and machine learning techniques. Awards & Recognition : DFG Scholarship (1998–2001) supporting doctoral research in applied microeconomics He leads macroeconomic policy advisory initiatives, evidenced by testimony to the Saxon State Parliament on pension reforms. No explicit mentions of student advisees or grants were found in the source text.

• Mathematical Economics
• Econometrics
• Time Series Analysis
• +۳ مورد دیگر

Ivan Pavlovich Stankevich is an Associate Professor at the National Research University Higher School of Economics (HSE) , affiliated with the Faculty of Economic Sciences and Department of Applied Economics. He also serves as a Senior Research Fellow in the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy and a Leading Expert in the Project Laboratory for the Development of Intellectual Competitions in Economics. With 11 years of teaching and research experience since joining HSE in 2011, he specializes in mathematical economics , time series analysis , and dynamic economic equilibrium modeling. PhD in Economics (2022, HSE) Master's in Economics (2014, HSE) Bachelor's in Economics (2012, HSE) His research focuses on nowcasting GDP with MIDAS models , seasonal adjustment in economic data, and multiproduct decomposition of macroeconomic balances. Recent work demonstrates how news background analysis improves GDP projections during economic instability. He has received multiple scientific awards , including the HSE Rector's Letter of Thanks (2022), Best Teacher Awards (2024, 2019–2020, 2015), and bonuses for publications in List B journals. His grants include leadership in RFBR projects on intertemporal equilibrium (2016–2017) and participation in RSF-funded research on dynamic model identification. As a scientific advisor , he mentors PhD students Stefano Boscolo Fiore (Moscow Road Safety Analysis) and Natalia Makeeva (Uncertainty Forecasting). He actively participates in educational initiatives such as the National Olympiad in Data Analysis DANO and All-Russian School Olympiad in Economics.

Hong Yuمشاهده پروفایل

دانشیار

• EEG-Based BCI Systems
• Big Data Analysis
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Hong Yu is an Associate Professor in the Engineering Technology department at Fitchburg State University's School of Business and Technology. Specializing in wireless communication protocols, neural networks, and embedded systems, their research spans earthquake nowcasting, IoT applications, and industrial sensor development. Current projects include 5G network optimization and FPGA-based measurement systems. PhD in Electrical & Electronics Engineering from The Catholic University of America Active in NSF-funded initiatives like the Microcontroller Training System project Professional affiliations include IEEE Senior Membership and leadership roles in multiple IEEE chapters Teaches courses in microprocessor systems, digital electronics, and mobile application development Research focuses on merging biological neural network principles with technological applications, while maintaining expertise in analog electronics and environmental monitoring. Recent publications emphasize wireless protocol design (2021), color sensing automation (2021), and cross-platform smart home development (2020), showing consistent innovation across 5G networking, semiconductor design, and seismic analysis. Professional contributions include serving as Vice Chairperson in IEEE ROBOT Chapter (Worcester County) and IEEE AESS Chapter (Baltimore), plus judging robotics competitions like FIRST LEGO League.

Gabriele Moserمشاهده پروفایل

استاد

• Remote Sensing
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Gabriele Moser is a Full Professor (Professore ordinario) at the University of Genoa's Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), affiliated with the Polytechnic School. He serves as Coordinator of the Engineering for Natural Risk Management program, sits on the Departmental Board, and contributes to the School Council. His teaching portfolio spans telecommunications, signal processing, remote sensing, and machine learning courses for energy engineering and natural risk management programs. Professor Moser's research integrates remote sensing , machine learning , and signal processing to address environmental and infrastructural challenges. Key focus areas include: Multisensor data fusion for natural disaster monitoring Deep learning architectures for satellite/UAV image analysis Probabilistic graphical models for classification Lightning nowcasting using meteorological data Hyperspectral image unmixing and reconstruction His recent publications demonstrate a strong emphasis on AI-driven methodologies for remote sensing applications. Dominant themes include deep learning frameworks (CNNs, CRFs, diffusion models) for image segmentation, multiresolution fusion techniques, and heterogeneous change detection, frequently applied to disaster management scenarios like flooding and weather prediction.

Annunziata D'Aversaمشاهده پروفایل

پژوهشگر ارشد

• Time Series Forecasting
• Nowcasting
• Big Data Analytics
• +۱ مورد دیگر

Annunziata D'Aversa is a Research Fellow and PhD student in the Department of Computer Science at the University of Bari Aldo Moro, specializing in energy sector forecasting models. Her work focuses on developing incremental update methodologies for time-sensitive data streams in electricity networks and renewable energy systems. She earned her Master's degree in Data Science from the University of Bari Aldo Moro in July 2021 with full marks and honors, completing a thesis on spatial autocorrelation modeling for energy consumer forecasting in electricity networks. Her research interests include Time Series Forecasting, Nowcasting, Big Data Analytics, and Neural Networks, with emphasis on spatial-temporal pattern recognition for energy applications. She develops adaptive models that process real-time data streams for grid management and wind power optimization. Her 2022-2024 publications demonstrate consistent innovation in spatio-temporal modeling, applying model trees and linear architectures to multi-step forecasting challenges across smart grids and wind power plants. Key contributions include spatially-aware nowcasting techniques and autocorrelation leverage for consumption prediction.

Francesco Ravazzoloمشاهده پروفایل

استاد

• Econometrics
• Energy Economics
• Financial Econometrics
• +۳ مورد دیگر

Francesco Ravazzolo is a Full Professor of Econometrics at the Faculty of Economics and Management, Free University of Bozen-Bolzano, and Head of the Department of Data Science and Analytics at BI Norwegian Business School. He holds a Ph.D. from Tinbergen Institute (2007). His research focuses on commodity markets, energy economics, financial econometrics, and macroeconometrics, with over 40 publications in leading journals such as the Journal of Applied Econometrics and Annals of Applied Statistics. He serves on the editorial boards of the International Journal of Forecasting, Journal of Applied Econometrics, and others. His work addresses policy-relevant issues like energy price forecasting, macroeconomic volatility, and cryptocurrency modeling. He co-founded AIAQUA and COMMODIA, spin-offs from the Free University of Bozen-Bolzano. Key research interests include Bayesian econometric methods, nowcasting techniques, and the application of advanced statistical models to energy and financial markets. His recent work examines gas price caps, smart energy systems, and pandemic impacts on businesses. He has collaborated with institutions like the Reserve Bank of Australia on core inflation measures and co-developed the DeCo toolbox for density forecast combination.

Andrea Carrieroمشاهده پروفایل

استاد

• Econometrics and Quantitative Methods
• Macroeconomics
• Financial Economics
• +۴ مورد دیگر

Andrea Carriero is a Professor of Economics at Queen Mary University of London, affiliated with the School of Economics and Finance. His research focuses on applied macroeconometrics, empirical macroeconomics, and financial econometrics, with a particular emphasis on the term structure of interest rates and forecasting with large datasets. He has consulted for institutions such as HM Treasury Debt Management Office, the European Central Bank (ECB), and central banks of the Czech Republic and Estonia. His work bridges econometric methodology and real-world macroeconomic challenges, including uncertainty quantification and structural modeling. Notable contributions include Bayesian vector autoregressions (BVARs), volatility analysis, and nowcasting techniques. His research has implications for policy-making, financial markets, and economic forecasting.

Simon Lacoste-Julienمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Simon Lacoste-Julien is a Full Professor at the Université de Montréal's Department of Computer Science and Operations Research, part of the Faculty of Arts and Sciences. He is affiliated with the Centre de recherches mathématiques (CRM) and Mila – Quebec AI Institute. His research focuses on machine learning, deep learning, optimization, and theoretical foundations of AI. Notable contributions include advancements in constrained optimization algorithms, game-theoretic learning, and sparsity-aware models. Education: Doctorate in Machine Learning (PhD), specialized in optimization and graphical models. Research Interests: His work bridges theory and practice, exploring topics such as: - Optimization for Machine Learning : Developing efficient algorithms for large-scale problems. - Game-Theoretic Approaches : Applying game theory to prediction and mechanism design. - Disentangled Representations : Learning structured latent variable models for better interpretability. - Adaptive Gradient Methods : Improving convergence and stability of training processes. Grants & Projects: Lead researcher on multiple grants including: - Model-based Machine Learning with Guarantees (2025-2031, CRSNG) - Robust and Efficient Structured Prediction (2017-2025) - Projects on few-shot learning and algorithmic fairness funded by MITACS and IVADO. Labs & Teams: Active member of Mila, leading research in optimization and theoretical aspects of deep learning. Collaborates widely with industry (e.g., Samsung Electronics) and contributes to open-source tools like the Cooper library for constrained optimization.

• Capital Markets Research
• Corporate Finance
• Accounting
• +۶ مورد دیگر

Panos N. Patatoukas is a Professor and the L.H. Penney Chair in Accounting at UC Berkeley's Haas School of Business. He serves as Co-Faculty Director for Sustainable & Impact Finance and Faculty Director at the Center for Financial Reporting & Management. PhD, MPhil, MA - Yale University MSc - London School of Economics (with distinction) BA - Athens University of Economics & Business (valedictorian) His research bridges accounting, corporate finance, law, and economics with focus areas: Corporate financial reporting and valuation ESG sustainability measurement and investing Cross-industry economic linkages Firm/macroeconomic performance measurement Nowcasting using financial + environmental data Research trends show increasing focus on climate finance, big data applications, and ESG investing. His work on satellite imagery analysis and carbon emission valuation has gained significant media attention. Scientific recognition includes: AAA/AICPA Notable Contributions (2017, 2019) UC Berkeley Distinguished Teaching Award (2018) Earl F. Cheit Outstanding Teaching Awards (2012-2022) Fortune Magazine Top-10 Under 40 Professor As an educator, he has directed Berkeley ExecEd programs on financial data analysis and corporate finance for lawyers, while maintaining active collaborations with organizations across sectors through executive education and consulting engagements.

Luca Tiozzo Pezzoliمشاهده پروفایل

استاد مهمان

• Econometrics
• Machine Learning in Economics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Luca Tiozzo Pezzoli serves as a Visiting Professor in the Department of Applied Economics at the University of the Balearic Islands. His teaching portfolio spans undergraduate and graduate programs including Business Administration, Economics, and the Master's Degree in Economics of Tourism, where he instructs courses such as Economic Data Analysis, Microeconometrics, and Causal Analysis and Prediction. His research focuses on the intersection of econometrics and machine learning, with particular expertise in big data applications for economic forecasting. Key interest areas include: Real-time nowcasting using mixed-frequency data News sentiment analysis for financial markets Climate economics and event-driven market shocks Alternative data sources in crisis monitoring Interpretable AI for economic policy analysis His publication record reveals a strong trend toward integrating unconventional data streams (news, social media, seismic economic indicators) with machine learning techniques to enhance economic forecasting precision, particularly during volatile periods like the COVID-19 pandemic. Recent work emphasizes Bayesian interpretability and climate-economic linkages. Dr. Pezzoli actively contributes to the Econometrics and Data Science (ECD) Consolidated R+D+I Group at UIB. His teaching responsibilities include supervising numerous Final Degree Projects across Business Administration programs, though specific student names aren't documented. He maintains technical engagement through ORCID and Dialnet profiles while utilizing advanced data infrastructure for his research initiatives.

Humberto Vergaraمشاهده پروفایل

استادیار

• Hydrology
• Flash Flood Forecasting
• Remote Sensing
• +۳ مورد دیگر

Humberto Vergara is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Iowa, and concurrently serves as an Assistant Research Engineer at IIHR—Hydroscience and Engineering. He is also affiliated with the Iowa Flood Center. His work spans numerical hydrologic modeling, flash flood forecasting, and remote-sensing hydrology, with a strong emphasis on scientific computing in hydrologic applications. Education: PhD, Civil Engineering (Water Resources), University of Oklahoma MSc, Civil Engineering (Water Resources), University of Oklahoma BS, Environmental Engineering, El Bosque University, Colombia Research Interests: Vergara’s research is centered on extending flash flood forecasting capabilities in data-scarce regions, understanding flash floods in post-fire environments, and improving the physics representation in parsimonious hydrologic models. He also focuses on extending lead times for flash flood forecasts and warnings, leveraging satellite observations and machine learning techniques. His recent work includes developing satellite-based frameworks for early warning systems in West Africa, evaluating global precipitation products, and integrating machine learning models to enhance precipitation nowcasting and flood impact classification. Scientific Awards: No awards or honors are explicitly mentioned in the provided text. Research Labs and Teams: He leads the Advanced Hydrology and Warning Applications Laboratory at the University of Iowa, which focuses on cutting-edge research in hydrologic modeling, early warning systems, and remote sensing applications for flood forecasting.

Andre Amaralمشاهده پروفایل

مدرس

• Spatio-temporal Statistical Modelling
• Bayesian Modelling
• Machine Learning
• +۲ مورد دیگر

Dr André V. Ribeiro Amaral is a Lecturer in Statistical Learning at the School of Mathematical Sciences, University of Southampton. His research focuses on spatio-temporal statistical methods for environmental and public health applications, including Bayesian computation and machine learning for infectious disease modeling. PhD in Statistics from KAUST (2023) MSc in Statistics from UFMG (2020) Previous Research Associate at Imperial College London (2024–2025) Key research themes include: Computationally tractable spatio-temporal models Bayesian inference for complex datasets Machine learning integration in statistical frameworks Applications to epidemiology and environmental science His methodological work spans data fusion, preferential sampling correction, and disease spread dynamics, with recent publications on dengue fever nowcasting and Antarctic krill abundance modeling. Teaching: Co-teaching Statistical Modelling II (MATH3091)