جست‌وجوی استاد و پژوهشگر

Soumaya Cherkaouiمشاهده پروفایل

استاد

• Artificial Intelligence
• Telecommunications Networks
• Quantum Computing
• +۶ مورد دیگر

Soumaya Cherkaoui is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Previously, she served as a Full Professor at Université de Sherbrooke and held industrial roles as an aerospace project manager. Her research integrates artificial intelligence with telecommunications, focusing on quantum computing, frugal edge intelligence, and applications in connected vehicles and IoT. Current Position: Full Professor, Polytechnique Montréal Prior Academic Role: Full Professor, Université de Sherbrooke Industry Experience: Aerospace Project Manager Research Interests: Convergence of AI and communications, quantum computing for networking, frugal intelligence at the edge, and applications in autonomous vehicles, industrial IoT, and smart grids. She leads government and industry-funded projects, including a $6 million quantum initiative in 2025. Recent Publication Trends: Her 2025–2024 work emphasizes quantum-enhanced anomaly detection (via QGANs), Open RAN slicing with quantum optimization, and reinforcement learning for secure cognitive radio networks. Topics span 5G/6G, vehicular networks, and zero-trust architectures. Scientific Awards: IEEE Communication Society Distinguished Lecturer (2020) ACM Mirela Notare Award (2023) IEEE Bio-Inspired Computing STC Leadership Award (2023) N2Women: Stars in Networking and Communications (2023) Best Paper Awards at IEEE ICC 2017, IEEE LCN 2021, ICCSPA 2024 Advising and Grants: Supervised 3 Master's students in 2024, with research on quantum GANs and federated learning for vehicular networks. Secured grants like the $6 million quantum project (2025) and participated in CFI-QC government funding (2022). Editorial and Leadership: Served as Associate Editor for IEEE, Wiley, and Elsevier journals. Chaired conferences like IEEE LCN 2019 and IEEE ICC2025, and held leadership roles in IEEE Communications Society committees.

Navid Bayatiمشاهده پروفایل

دانشیار

• Renewable/Hybrid Power Systems
• Protection and Transient Analysis of Power Grids
• Microgrids and Active Energy Networks
• +۵ مورد دیگر

Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .

Klaus Abbergerمشاهده پروفایل

پژوهشگر

• Business Cycle Analysis
• Composite Economic Indicators
• Policy Uncertainty
• +۳ مورد دیگر

Klaus Abberger is a Researcher at the KOF Business Tendency Surveys unit of the KOF Swiss Economic Institute , affiliated with ETH Zurich . His work focuses on business cycle analysis , composite economic indicators , and policy uncertainty impacts on investment and inflation expectations. He has developed methodologies for partial least squares modeling and real-time economic simulations , including the Global Economic Barometers for tracking worldwide output growth. His research frequently incorporates economic tendency survey data from multiple countries, with applications in Swiss business cycles and data-scarce regions like Abu Dhabi. Collaborations with scholars such as Jan-Egbert Sturm and Michael Graff highlight his expertise in composite indicator design and economic forecasting during crises like the Covid-19 pandemic and energy shortages . Abberger's publications span journals including Review of World Economics and European Economic Review , alongside contributions to KOF Studies and KOF Analysen . His work addresses policy uncertainty effects , inflation expectation pass-through , and compositional data analysis in economic contexts.

Anthony TUNG Kum Hoeمشاهده پروفایل

استاد

• Database Systems
• Indexing and Searching Complex Objects
• Time Series Analysis
• +۱۳ مورد دیگر

Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.

Susan Anenbergمشاهده پروفایل

استاد

• Air Pollution and Health
• Climate Change Impacts
• Environmental Policy
• +۴ مورد دیگر

Susan Anenberg is a Professor and Chair of the Environmental and Occupational Health department at the George Washington University Milken Institute School of Public Health. She directs the GW Climate and Health Institute and serves as President of the AGU GeoHealth Section (2023-2024). Her research integrates epidemiology, atmospheric modeling, and policy analysis to quantify health impacts of air pollution and climate change. PhD in Environmental Science and Engineering from UNC (2011) Former EPA environmental scientist and U.S. Chemical Safety Board deputy director Key research themes include: Global air pollution burden of disease estimation Climate change health co-benefits analysis Satellite remote sensing for exposure assessment Environmental justice and exposure disparities GeoHealth policy frameworks Her recent work leverages geospatial datasets to reveal racial/ethnic disparities in NO2 exposure and develops decision-support tools for urban climate action plans. She has contributed to major publications in Lancet Planetary Health , Science , and Nature , and serves on advisory boards for EPA, WHO, and the U.S. National Academy of Sciences. Scientific engagement includes: Co-founding Environmental Health Analytics, LLC Technical advisory roles for Clean Air Act and climate policy Development of multi-modal vehicle electrification health impact models

Diana Tunaruمشاهده پروفایل

مدرس

• Empirical Finance
• Interest Rate Modeling
• Dynamic Estimation and Forecasting
• +۴ مورد دیگر

Dr. Diana Tunaru is a Lecturer in Finance at Kent Business School , University of Kent, with expertise in empirical finance and quantitative methods. Her research focuses on dynamic financial modeling and forecasting, particularly in interest rate structures and volatility spillover effects. Education: Diploma in Mathematics (University of Craiova), Master in Banking and Finance (Middlesex University), PhD in Finance (University of Westminster) Research Interests include multi-factor continuous-time models, Gaussian estimation techniques, and the analysis of financial variables like interest rates, equities, and derivatives during economic distress. She applies her industry experience in investment banking to bridge theoretical and practical finance. In publications , Diana has explored topics ranging from agricultural commodity forecasting to green finance, with recent work emphasizing sustainability-driven portfolio performance and market interconnectedness. Her methodological focus on statistical accuracy tests and spillover effects reflects her commitment to advancing financial modeling. Teaching: Quantitative Methods for Finance, Introduction to Econometrics, Financial Investments, Economics of Financial Markets Diana serves as Senior Tutor at Kent Business School (Medway campus) and contributes to postgraduate education through her supervision of undergraduate dissertations on empirical finance and financial time series analysis.

Guofu Zhouمشاهده پروفایل

استاد

• Empirical Asset Pricing
• Machine Learning in Finance
• Financial Economics
• +۱۰ مورد دیگر

Guofu Zhou , the Frederick Bierman & James E. Spears Professor of Finance at Washington University's Olin Business School , has been a faculty member since 1990. His academic career includes multiple Reid Teaching Awards (2020, 2019, 2018, 2014, 2010) Best Paper Awards (Institute for Quantitative Investment Research 2019, Chinese Finance Association 2010, Inquire UK/Europe 2019 & 2024) and affiliations with journals like the Journal of Financial Economics and Management Science . Education: PhD, Duke University (1990) MA, Duke University (1987) MS, Academia Sinica (1985) BS, Chengdu University of Technology (1982) His research bridges empirical asset pricing and applied AI/machine learning , with a focus on market efficiency anomaly exploitation Bayesian inference option pricing Chinese financial markets behavioral finance He has contributed to understanding equity risk premium predictability, technical analysis, and portfolio optimization techniques. Key trends in his Journal of Financial Economics , Journal of Finance , and Review of Financial Studies publications include machine learning applications in asset pricing , anomaly-market linkages , and fear sentiment in Treasury markets . Recent work with ChatGPT explores textual analysis of earnings calls. Scientific Awards: Best Paper Award, Institute for Quantitative Investment Research (2019) Finalist for Crowell Memorial Prize (2024) Led multiple Best Paper Awards at conferences like FMA and SIF Contact: zhou@wustl.edu | Office: Simon Hall Room 207

Martin Spindlerمشاهده پروفایل

استاد

• Econometrics
• Statistics with Applications to Finance, Insurance, and Health Economics
• Nonparametric Statistics
• +۳ مورد دیگر

Prof. Dr. Martin Spindler is a Professor for Statistics at the Department of Statistics with Application in Business Administration, University of Hamburg Business School. His research bridges Econometrics, Statistics, and Machine Learning, focusing on high-dimensional methods, causal inference, and applications in finance, insurance, and health economics. Current position since 2016 Visiting Professor at University Mannheim (2016), Boston College (2015), and MIT (2015, 2013-2014) Senior Researcher at Max Planck Society (2012-2016) Education: PhD in Economics, University of Munich (2012) Master in Mathematics and Economics, University of Munich (2008) and Regensburg (2003) B.A. in Mathematics, University of Regensburg (2005) His methodological work includes L2Boosting for treatment effect estimation, double machine learning frameworks, and nonparametric approaches for asymmetric information. Applications span from fraud detection in claims management to pandemic shielding strategies and financial forecasting. Research Trends: Recent publications emphasize high-dimensional statistical methods, causal machine learning, and interdisciplinary applications. Key tools include double machine learning, attention networks, and transformation models. Collaborations: Active partnerships with institutions like MIT, Boston College, and Max Planck Society, alongside contributions to open-source software (e.g., DoubleML, hdm package).

• Semantic Web
• Data Analytics
• Artificial Intelligence
• +۳ مورد دیگر

John Breslin is a Personal Professor in Electronic Engineering at the College of Science and Engineering, University of Galway, serving as Director of the TechInnovate and AgInnovate programmes. Associated with two Taighde Éireann – Research Ireland Centres, he is a Principal Investigator at Insight Centre for Data Analytics (specializing in data analytics) and a Funded Investigator at VistaMilk (Agri-Technology), while also leading the EDIH Data2Sustain project. With an h-index of 50, over 12,000 citations, and 300+ peer-reviewed publications including seminal books on the Social Semantic Web, he ranks among Ireland's most influential researchers in digital technologies. Breslin's research fundamentally bridges Semantic Web technologies, AI-driven data analytics, and practical innovation. His co-creation of the SIOC framework—implemented across 65,000+ websites by entities like Yahoo and Boeing—demonstrates real-world impact in social data interoperability. Current work leverages blockchain and federated learning for sustainable Agri-Technology through VistaMilk, while his TechInnovate programmes translate academic research into commercial ventures across healthcare, smart manufacturing, and energy systems. Analysis of his 15 most recent publications reveals dominant themes in AI-enhanced security (35% of works), blockchain applications for sustainability (27%), and multimodal AI for healthcare (20%). His team pioneers privacy-preserving techniques for IoT and medical devices, neurosymbolic visual reasoning frameworks, and federated learning architectures addressing data heterogeneity—directly supporting his roles in national research infrastructures like Insight and VistaMilk. John has received several prestigious awards: IIA Net Visionary Award (twice) ITAG Outstanding Contribution to the ICT Sector Award Galway Chamber President’s Award Best Irish-Published Book Award (2020 for Old Ireland in Colour) Multiple Best Paper Awards He leads major research initiatives funded by Taighde Éireann – Research Ireland: Insight Centre for Data Analytics (as Principal Investigator) VistaMilk SFI Research Centre (as Funded Investigator) EDIH Data2Sustain (as Principal Investigator) His entrepreneurial programs TechInnovate and AgInnovate have mentored 200+ startups, securing €50M+ in follow-on funding. Breslin co-founded PorterShed (Galway City Innovation District) and serves on Scale Ireland's Steering Group, creating Ireland's most active regional innovation ecosystem outside Dublin. He maintains active industry partnerships with Vodafone, Boeing, and agricultural cooperatives through VistaMilk's testbed facilities.

Miguel Rico-Ramirezمشاهده پروفایل

دانشیار

• Radar Hydrology
• Hydroinformatics
• Flood Forecasting
• +۳ مورد دیگر

Dr. Miguel Rico-Ramirez serves as Associate Professor of Radar Hydrology and Hydroinformatics at the University of Bristol's School of Civil, Aerospace and Design Engineering. His research integrates advanced radar technology with hydrological modeling to address critical water resource challenges including flood forecasting, drought management, and precipitation measurement across diverse global contexts from South Korea to Mexico City. Education: Bachelor of Engineering (Eng.) Master of Engineering (M.Eng.) Ph.D. in Engineering, University of Bristol His research program focuses on radar-based precipitation estimation, hydroinformatics, and flood prediction systems. He pioneers deep learning applications for rainfall nowcasting and develops innovative methods for uncertainty quantification in hydrological modeling. Current work emphasizes cosmic-ray neutron sensor validation, satellite-based flood mapping, and seasonal forecast applications for reservoir operations, with strong emphasis on translating research into operational water management solutions. Recent publications (2023-2025) reveal three dominant research thrusts: (1) deep learning frameworks for spatiotemporal rainfall prediction, (2) global validation of precipitation and soil moisture datasets using novel sensor networks, and (3) operational implementation of seasonal forecasts for drought mitigation in South Korea. His work consistently bridges radar meteorology with practical hydrological applications across urban and data-scarce environments. Scientific Awards: No specific awards documented in source materials Dr. Rico-Ramirez supervises postgraduate researchers in radar hydrology and hydroinformatics, with projects spanning flood early warning systems, precipitation nowcasting, and climate adaptation strategies. His research receives funding for international collaborations focused on water security challenges, particularly in drought-prone regions and data-scarce basins like the Nile Delta. Current grants support development of integrated forecasting systems combining global datasets with machine learning for extreme event management. He leads the Radar Hydrology research group within Bristol's Water and Environmental Engineering division, collaborating closely with Professor Dawei Han on hydroinformatics and Dr. Rafael Rosolem on water-climate interactions. The team maintains active partnerships with meteorological agencies and water authorities globally, particularly in flood forecasting system implementation across South Korea and Mexico.

Ann Bostromمشاهده پروفایل

استاد

• Risk Perception
• Environmental Policy
• Decision-Making Under Uncertainty
• +۴ مورد دیگر

Ann Bostrom is a Professor at the Daniel J. Evans School of Public Policy and Governance, University of Washington. Previously, she served as Associate Dean for Research at Georgia Tech's Ivan Allen College of Liberal Arts (1992-2007) and co-directed the National Science Foundation's Decision Risk and Management Science Program (1999-2001). Her work bridges environmental policy, risk communication, and decision-making under uncertainty. Education: Ph.D. in Public Policy Analysis (Carnegie Mellon University), M.B.A. (Western Washington University), B.A. in English (University of Washington), Postdoctoral studies in Engineering and Public Policy (Carnegie Mellon) and Cognitive Survey Methodology (Bureau of Labor Statistics) Bostrom's research focuses on risk perception , environmental policy , and decision-making under uncertainty , particularly in climate change, natural disasters, and science communication. She has pioneered mental models approaches to risk assessment and contributed to understanding public attitudes toward carbon emissions, weather hazards, and astronaut health risks. Her scientific awards include the 2020 Distinguished Educator Award and 1997 Chauncey Starr Award (both from the Society for Risk Analysis), and fellowships from AAAS, WSAS, and SRA. She has received research funding from the National Science Foundation, EPA, and NIH. Bostrom leads or co-leads multiple NSF-funded initiatives, including the Cascadia Coastlines and Peoples Hazards Research Hub and the NSF AI Institute for Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) . She serves on advisory committees for AAAS, Washington State Academy of Sciences, National Renewable Energy Laboratory, and NCAR's Mesoscale & Microscale Meteorology Lab.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

• Smart Communities
• Next-generation Mobility Systems
• Unused Biomass Utilization Technologies
• +۶ مورد دیگر

Hiroshi Onoda is a Professor at the Faculty of Science and Engineering and Graduate School of Environment and Energy Engineering , Waseda University. His work focuses on environmentally friendly design , LCA , resource recycling , renewable energy systems , and smart communities . He holds a Doctor of Engineering from Waseda University and has held various leadership roles, including Dean of the Graduate School of Environment and Energy since 2022. Education : PhD in Mechanical Engineering (Waseda University, 2006) Professional Leadership : Environment Energy Advisor (Saitama Prefecture, 2010–2015), Director of Waseda Environmental Institute Co., Ltd. (2011–2017) Research Themes : Onoda’s research spans smart waste management , EV integration , CO2 reduction , and renewable energy deployment . He emphasizes AI and IoT applications in waste systems, decentralized energy solutions for developing nations, and decarbonization strategies for urban and industrial contexts. Publication Trends : His recent work addresses AI-driven recycling optimization , community-level GHG inventories , and DAC-based fertilizer systems , reflecting his commitment to circular economy and low-carbon mobility . Key sub-fields include robotic waste sorting , decentralized energy planning , and plastic recycling economics . Scientific Awards : Environment Minister’s Commendation (2024) Best Paper Award, ICET4SD (2021) JSME Environment Division Award (2014) JSME Research Encouragement Award (2018) Grants and Projects : Onoda has led nationwide evaluations of low-carbon industrial zones , hydrogen utilization , and plastic recycling policies . He contributes to Japan’s decarbonization roadmap through roles in NEDO and Ministry of the Environment committees. Labs and Initiatives : He founded the Waseda Environmental Research Institute, Inc. (2003) and drives innovation through the BRIDGE LIFE Platform for smart communities in Japan.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Goran Oreški is an Associate Professor and Head of the Laboratory at the Faculty of Informatics in Pula (University Jurja Dobrile, Croatia), where he has been employed since 2019. He teaches courses on databases, object-oriented programming, data warehousing, and artificial intelligence at both undergraduate and graduate levels. Education: Ph.D. in Informatics (2016), Faculty of Organization and Informatics Industry Experience: 9 years as software architect and programmer in banking sector Research Focus: Artificial Intelligence systems, classical machine learning algorithms, and deep learning architectures. His work bridges theoretical advancements with practical applications in autonomous vehicles, traffic monitoring, and financial risk assessment. Recent Publication Trends: 2023-2025 works emphasize generative AI for synthetic credit data, traffic object segmentation with monocular cameras, and context-aware detection models (YOLO*C). Earlier works focus on genetic algorithms and ensemble learning for imbalanced datasets. Awards: Google RFP Award for autonomous vehicle research Highly Cited Paper (Web of Science, top 1%) Best Paper at CECIIS conference Leadership: Director of FIPU Laboratory since 2022, leading projects like ai.Shuttle (autonomous mini-bus) and CenAI (industry collaboration with Cenosco).

David Bastvikenمشاهده پروفایل

استاد

• Environmental Science
• Climate Change
• Biogeochemistry
• +۷ مورد دیگر

David Bastviken is a Professor at the Environmental Change Theme (TEMAM), Linköping University , specializing in environmental science and biogeochemical cycles. His research focuses on greenhouse gas emissions, particularly methane and carbon dioxide, from freshwater systems and human activities, with implications for climate policy and pollution management. Research Pillars : Aquatic greenhouse gas dynamics, chlorine cycling in soils, drinking water disinfection by-products, landscape-scale carbon budgets Methodologies : Drone-based sensing, hyperspectral imaging, sensor networks, cross-disciplinary ecosystem experiments Notable findings include the discovery of underestimated methane emissions from lakes and rivers, the role of trees in methane uptake , and natural chlorine production in boreal forests. His work has been funded by ERC , Formas , VINNOVA , and The Swedish Research Council . Recent publications highlight climate sensitivity of methane emissions, day-night emission patterns , and global methane budget modeling. He leads international collaborations across Amazonas , Arctic , and Boreal regions.