جست‌وجوی استاد و پژوهشگر

Henning Kirschenmannمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• CMS Experiment
• +۵ مورد دیگر

Henning Kirschenmann is a tenured Professor at LUT School of Engineering Sciences and an Academy Research Fellow at the Helsinki Institute of Physics . He specializes in jet-energy corrections , hadronic final states , and machine learning applications for particle physics data analysis. Education Dr. rer. nat. (Physics) from University of Hamburg (2011–2014) Dipl. Phys. (Physics) from University of Hamburg (2010–2014) Research Interests His research focuses on particle physics with emphasis on CMS experiment data analysis. Key areas include jet energy corrections , top quark properties , Higgs boson decays , and quantum entanglement studies. He develops machine learning techniques for detector optimization and trigger systems , while investigating new physics through dark matter signatures and long-lived particles . Scientific Contributions Kirschenmann has contributed to 15+ CMS experiment publications since 2024, covering: Higgs boson and Z boson rare decay analyses Top quark polarization and spin correlations Jet substructure studies in proton-proton and heavy-ion collisions Detector performance evaluations for muon reconstruction and electromagnetic calorimetry Statistical analysis of multijet events and WW production Awards & Grants Research Council of Finland Starting Grant (1.1 million EUR) Academy Research Fellow (2024–present) Leadership Roles He has led multiple CMS subgroups including: Group leader for top quark mass (2022–2023) Group leader for jets and MET (2020–2022) Group leader for jet energy corrections (2016–2018)

• Statistics
• Meteorology
• Econometrics
• +۴ مورد دیگر

Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.

Matteo Faveroمشاهده پروفایل

مدرس

• Building Sustainability
• Thermal Comfort
• Occupant Behavior Modeling
• +۳ مورد دیگر

Matteo Favero is a Lecturer and Scientist at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He conducts research at the ETHOS Lab, focusing on data-driven interventions to enhance environmental and social sustainability in the built environment. His work integrates computational tools with human-centric approaches to optimize building performance and occupant well-being. Education: PhD in Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 2022 MSc in Building Engineering, Politecnico di Milano, 2015 BSc in Building Engineering, Politecnico di Milano, 2012 Research Interests: Favero's research explores occupant behavior modeling, statistical analysis of thermal comfort, and human-building interactions. He emphasizes multi-domain studies to bridge gaps between energy efficiency, indoor environmental quality, and user satisfaction. His work supports the development of adaptive building controls and sustainable design guidelines through empirical data and interdisciplinary methodologies. Publication Trends: Recent articles (2020–2024) prioritize occupant-centric building operations, statistical rigor in comfort studies, and standardized documentation for behavior models. Themes include predictive algorithms for thermal preferences, validation of human-in-the-loop methods, and critical reviews of multi-domain research practices, reflecting a consistent focus on enhancing data reliability and practical applicability in sustainable building science. Awards: 2022 Best Paper Award: A guideline to document occupant behavior models for advanced building controls (Building and Environment) 2022 Best Paper Award: Quality criteria for multi-domain studies in the indoor environment (Building and Environment) Labs and Teams: He is a core member of the ETHOS Lab at EPFL, an interdisciplinary group advancing sustainability through computational and engineering solutions for human-oriented built environments.

Eric V. Sludمشاهده پروفایل

استاد

• Mathematical Statistics
• Probability
• Census Statistics
• +۱۱ مورد دیگر

Eric V. Slud is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He has taught numerous graduate-level statistics courses including Mathematical Statistics, Categorical Data Analysis, and High-Dimensional Statistics since at least 2017. Dr. Slud's research spans several key areas of mathematical statistics and probability. His work focuses on: Census statistics, particularly demographic modeling of nonresponse to national surveys Small Area Estimation (SAE) with applications to the SAIPE program Survival data analysis including semiparametric inference and clinical trial design Meta-analysis in biostatistics Pharmaceutical statistical methods Large-scale data problems with emphasis on cross-classified data His publications demonstrate continued contributions across these domains, with particular emphasis on small area estimation methodologies, survival analysis techniques, and applications in survey statistics. Dr. Slud has developed innovative approaches to weighting adjustment, nonresponse correction, and estimation with complex survey data. Dr. Slud has advised several PhD students including Yang Cheng (2004), Sophie Tsou (2005), and others working on topics ranging from Factor Analysis to Survival Data. He has collaborated extensively with researchers at the Census Bureau and FDA. Dr. Slud maintains active research programs through multiple Research Interaction Teams (RITs) at the University of Maryland, focusing on advanced statistical methodologies and their applications to real-world problems in census methodology, healthcare, and other domains requiring sophisticated statistical analysis.

Jason Raphael Rambachمشاهده پروفایل

پژوهشگر

• Computer Vision
• Augmented Reality
• Robotic Vision
• +۷ مورد دیگر

Dr. Jason Raphael Rambach is a Senior Researcher and Deputy Director at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, leading the team "Spatial Sensing and Machine Perception." His work focuses on Scene Perception and Reasoning using Machine Learning, with affiliations spanning Computer Vision, Augmented Reality, and Robotics. Education Diploma in Computer Engineering, University of Patras, Greece (2012) M.Sc. in Information and Communication Engineering, Technical University of Darmstadt, Germany (2014) PhD in Computer Science, University of Kaiserslautern (2020) Dr. Rambach's research bridges Object Pose Estimation , Semantic Scene Understanding , Hybrid AI , and Robotic Vision . His publications (50+ in top conferences) and projects like EU Horizon HumanTech highlight AI applications in construction and recycling. Recent articles analyze symmetry ambiguity resolution, spherical image segmentation, and radar-camera fusion. Scientific Awards CVPR 2025 Outstanding Reviewer Best Paper Award, ISMAR 2017 Five BOP Challenge Awards (ECCV 2022, ICCV 2023) Best Industrial Paper, ICPRAM 2024 Scan2BIM Third Place (CVPR2023, CVPR2024) As a coordinator of EU Horizon HumanTech and contributor to projects like COPPER, BERTHA, KIMBA, and TWIN4TRUCKS, Dr. Rambach integrates AI into industrial workflows. He reviews for CVPR, T-PAMI, ECCV, ICCV, and organizes workshops on AI in Construction Robotics.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +۳ مورد دیگر

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

Hyunsoo Kimمشاهده پروفایل

استاد

• Top quark properties in hadron collisions
• Neutrino physics
• Neutrinoless double beta decay
• +۳ مورد دیگر

Hyunsoo Kim is a Professor in the Department of Physics and Astronomy at Sejong University. With over 1341 research outputs and a Scopus h-index of 112, he has been active in academic research since 1993. His academic journey includes positions at University of Illinois at Urbana-Champaign (1999-2004) as Research Assistant/Assistant Professor Seoul National University (2005-2008) as Research Assistant Professor Chonbuk National University (2008-2015) as Assistant/Associate Professor Education Ph.D. in Physics from University of Toronto (1999) M.S. in Physics from University of Toronto (1992) B.S. in Physics from McGill University (1991) Research Interests focus on Top quark properties in hadron collisions Neutrino physics Neutrinoless double beta decay His major achievements include top quark cross section measurement, neutrino oscillation parameter determination, and sterile neutrino searches. Research Trends indicate strong expertise in Particle collision dynamics Detector technologies High-energy physics simulations Quantum Chromodynamics Lepton/muon detection Large Hadron Collider data analysis Recent work involves CMS detector optimization and cross-section measurement methodologies. Collaboration Network shows active participation in international high-energy physics research through the CMS collaboration, with 1308 articles and 63612 citations.

• Medical Informatics
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de

• Statistical Science
• Mathematical Statistics
• Multivariate Analysis
• +۴ مورد دیگر

Kiyoshi Inoue is a Professor at the School of Political Science and Economics, Waseda University. He specializes in statistical science, mathematical statistics, and multivariate analysis, with a focus on heteroscedastic regression models and inverse regression problems. His research includes asymptotic improvements of estimators like the Graybill-Deal estimator and iterative weighted least squares methods. Professional Memberships: Japan Statistical Society, Mathematical Society of Japan His teaching portfolio spans advanced linear algebra, mathematical statistics, and calculus courses at both undergraduate and graduate levels. Research projects funded by the Japan Society for the Promotion of Science (2001-2003) explored high-accuracy estimators for inverse regression, with applications in econometrics and statistical modeling. Key trends in his publications include asymptotic theory development for heteroscedastic models, iterative estimation techniques, and robust methods under non-normality. His work bridges theoretical statistics with practical applications in economics and education.

Maths Halstensenمشاهده پروفایل

استاد

• Chemometrics
• Acoustic Chemometrics
• Process Analytical Technology
• +۴ مورد دیگر

Professor Maths Halstensen is a distinguished faculty member in the Department of Electrical Engineering, Information Technology and Cybernetics at the University of South-Eastern Norway (USN), Faculty of Technology, Natural Sciences and Maritime Sciences, Campus Porsgrunn. With a career spanning over two decades since his 2002 PhD, he has established himself as a leading expert in chemometrics and process analytical technology, maintaining continuous research output through 2019 with significant contributions to industrial monitoring systems. His academic journey includes: PhD in Acoustic Chemometrics from NTNU (2002) MSc in Process Automation from Telemark University College (1997) BSc in Industrial Electronics from Gjøvik University College (1995) Professor Halstensen's research program centers on developing multivariate data analysis techniques for industrial process monitoring, with particular emphasis on: Acoustic chemometrics for multiphase flow characterization CO2 capture process optimization using spectroscopic methods Real-time monitoring of scale deposition in pipelines Multivariate regression modeling for energy and process prediction Raman and NIR spectroscopy applications in industrial settings His work bridges theoretical chemometrics with practical industrial implementations, focusing on solutions for metallurgy, chemical processing, and carbon capture sectors. Analysis of his 15 most recent publications reveals a dominant focus on carbon capture technologies (60% of recent work), particularly equilibrium measurements in ammonia-CO2 systems and real-time monitoring of absorption processes. His research demonstrates consistent application of multivariate modeling to solve industrial challenges, with strong emphasis on sensor development and practical implementation. Key methodological contributions include acoustic chemometric approaches for velocity measurement and novel calibration techniques for complex industrial mixtures. As leader of the ACRG Laboratory, Professor Halstensen directs research in: Acoustic sensor development for industrial environments Spectroscopic monitoring system integration Multivariate data analysis for process optimization Real-time control system implementation His teaching encompasses BSc, MSc, and PhD levels with focus on practical engineering skills. The research group benefits from institutional support through USN's strategic focus on 'Energy, climate and the environment,' with likely connections to Research Council of Norway funding and EU research programs. Professor Halstensen maintains active industry collaborations through his applied research on process monitoring solutions for energy-intensive industries.

Frédéric Abergelمشاهده پروفایل

پژوهشگر

• Quantitative Finance
• Econophysics
• Mathematical Finance
• +۳ مورد دیگر

Frédéric Abergel is a researcher affiliated with CentraleSupélec, working in the field of Mathematics and Computer Science for Complexity and Systems. His research focuses on econophysics, mathematical finance, and computational finance, with a particular emphasis on high-frequency financial data, order book modeling, and liquidity risk. He has published extensively on topics such as Hawkes processes, non-quadratic hedging strategies, and agent-based models of financial markets. His recent publications highlight work on limit order books, Navier-Stokes equations in finance, and risk minimization techniques under transaction costs. While no explicit awards or teaching roles are mentioned in the provided texts, his contributions to interdisciplinary modeling, financial engineering, and complex systems suggest a strong engagement in both theoretical and applied research. Frédéric Abergel collaborates closely with researchers in quantitative finance and econophysics, as evidenced by his co-authored works on market microstructure and data-driven modeling of financial systems.

• Sensor Technology
• Measurement Systems
• Dynamic Error Analysis
• +۳ مورد دیگر

Krzysztof Tomczyk is a Professor at the Krakow University of Technology , affiliated with the Faculty of Electrical and Computer Engineering and the Department of Automation and Computer Science . His work focuses on sensor accuracy, dynamic error modeling, and measurement systems for industrial and energy applications. Research interests include Sensor Technology Measurement Systems Dynamic Error Analysis Industrial Automation Signal Processing Energy Systems Recent publications highlight trends in LVDT sensor calibration , accelerometer modeling , and dynamic error bounds for sensors in energy and machining contexts. His work integrates algorithms like Levenberg–Marquardt for system identification and emphasizes uncertainty quantification in measurements.

Paweł Kalinowskiمشاهده پروفایل

استادیار

• gas sensors
• electronic nose
• data mining
• +۲ مورد دیگر

Dr. Paweł Kalinowski serves as an Assistant Professor at the Department of Functional Materials Engineering, Faculty of Electronics Telecommunications and Informatics, Gdańsk University of Technology. His office is located in Building A of the Faculty at room 811, with contact phone number 583472792 and email pawkalin@pg.edu.pl. He has taught 85 courses throughout his academic career at the institution. Dr. Kalinowski obtained his dr inż. (Doctor of Engineering) degree in 2018 in Electronics (Technology) from the Faculty of Electronics Telecommunications and Informatics. His research focuses on advanced sensor technologies with particular emphasis on gas detection systems and environmental monitoring applications. His primary research interests include gas sensors , electronic nose technology, and data mining techniques for sensor array analysis. His work addresses critical challenges in sensor technology such as baseline drift, fault detection in sensor arrays, humidity interference, and temperature modulation effects on semiconductor gas sensors. His research bridges electrical engineering with environmental science, developing practical solutions for air quality monitoring systems. Analysis of his publication record (31 total publications) reveals consistent research in gas sensor technology with increasing sophistication in data analysis methods. His most recent work (2019-2022) focuses on fault detection in sensor arrays and advanced signal processing techniques, while earlier publications (2013-2018) established foundational work in gas identification and environmental monitoring applications. His research demonstrates a clear trajectory toward more complex sensor network analysis and environmental applications. Dr. Kalinowski has contributed to both engineering journals like Sensor Review and Metrology and Measurement Systems , as well as environmental science publications including Science of the Total Environment , demonstrating interdisciplinary research impact. His teaching portfolio of 85 courses indicates substantial educational contributions to the Faculty of Electronics Telecommunications and Informatics, though specific course details aren't provided in the available documentation. His workplace in Building A suggests integration with the main electronics and telecommunications research facilities at Gdańsk Tech.

Abdul K. Mohammedمشاهده پروفایل

استاد

• Environmental Chemistry
• Bioinorganic Chemistry
• Photochemistry
• +۱ مورد دیگر

Dr. Abdul K. Mohammed serves as Professor and Chair of the Department of Chemistry and Biochemistry at North Carolina Central University's College of Science and Technology, having previously held leadership roles as Dean of the same college and Chair of Chemistry at Winston Salem State University. His academic foundation includes: B.Sc. in Chemistry from University of Benin, Nigeria (1983) Ph.D. in Inorganic Chemistry from Louisiana State University (1992) Postdoctoral Research at Florida State University (1992-1993) His research integrates Environmental Chemistry through semiconductor-assisted photodegradation of water pollutants, Bioinorganic Chemistry via transition-metal complexes as antineoplastic agents, and Photochemistry of metal complexes. He pioneers Chemical Education innovations with student-centered pedagogies at HBCUs. Publication analysis reveals a consistent trajectory from foundational photochemistry (1990s) to applied environmental remediation (2005-2014), culminating in recent nanomaterial-focused wastewater solutions (2019-2021). Over 60% of his work addresses environmental applications, particularly photocatalytic pollutant degradation using TiO2/ZnO systems. His scholarly recognition includes: Fulbright Scholar award for research at Sultan Qaboos University, Oman (2003-2004) Carnegie African Diaspora Fellowship for collaborations in Nigeria (2017, 2019) As principal investigator for the NCCU-UNC Master's to Ph.D. Chemistry Bridge Program, he develops pathways for underrepresented students. His mentorship is evidenced by 12+ student co-authored publications since 2009, particularly in chemical education and environmental analysis projects. His research team collaborates internationally on wastewater treatment technologies and maintains active partnerships with UNC Chapel Hill through the Chemistry Bridge Program, focusing on nanomaterial synthesis and environmental applications.

• Markov processes
• Ruin theory
• Extreme value theory
• +۵ مورد دیگر

Romain Biard is a Senior Lecturer in Mathematics at the University of Franche-Comté, holding dual research affiliations with the Besançon Mathematics Laboratory (UMR 6623, LMB) and the Research Center on Economic Strategies (UR 3190, CRESE). His career bridges theoretical mathematics with practical applications in economics and healthcare systems. His primary research interests span Markov processes, Ruin theory, Extreme value theory, Optimal allocations, and Game theory. These areas converge in his work on modeling complex systems with random elements, particularly focusing on risk assessment and resource allocation problems. His research demonstrates how mathematical frameworks can address real-world challenges in healthcare management and economic competition. Analysis of his recent publications reveals a strong trend toward applied research with immediate societal relevance. His 2023-2024 work on healthcare resource allocation (nursing shortages, ventilator availability) applies sophisticated Markov process modeling to critical hospital management problems. Earlier work on economic competition models demonstrates how random entry of firms affects market equilibrium, with specific applications to the taxi industry facing competition from ride-hailing services. Active researcher with publications spanning 2008-2024 Primary collaborator network includes Marc Deschamps, Mostapha Diss, Alexis Roussel, and Stéphane Loisel Publications appear in high-quality journals across mathematics, statistics, economics, and actuarial science Biard's research approach consistently combines rigorous mathematical theory with practical applications, making significant contributions to both theoretical developments and real-world problem solving. His current work suggests continued focus on applying stochastic modeling to healthcare resource challenges and economic competition dynamics.