جست‌وجوی استاد و پژوهشگر

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۶ مورد دیگر

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.

Raj Mehrotraمشاهده پروفایل

پژوهشگر ارشد

• Hydrology
• Climate Change Impact Assessment
• Statistical Modeling
• +۳ مورد دیگر

Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.

Erhan Gülmezمشاهده پروفایل

استاد

• Experimental Nuclear Physics
• High Energy Physics
• Medical Physics
• +۳ مورد دیگر

Erhan Gülmez is a Professor at Boğaziçi University's Department of Physics, affiliated with the College of Engineering. He holds a visiting professorship at the University of Iowa and has been a CMS experiment member at CERN since 1999. His academic journey includes a double major in Physics and Electrical Engineering from Boğaziçi University (1982) and a PhD in Physics from Yale University (1986), focusing on high angular momentum structure in nuclei. Education: 1978: 7th in Turkey's national university entrance exam 1982: Double Major in Physics and Electrical Engineering, Boğaziçi University 1985: MS and M.Phil. in Physics, Yale University 1986: PhD in Physics, Yale University (Thesis: 'High Angular Momentum Structure in A~140 Nuclei') Research Interests: His work spans experimental nuclear physics, high-energy physics, and medical physics, with a focus on collider experiments such as CMS at CERN. Key areas include Higgs boson studies, dark matter searches, and jet physics. He contributes to detector development, notably the CMS-HF calorimeter, and has coordinated projects on radiation damage monitoring and heavy ion studies. Awards: Boğaziçi University Outstanding Achievement in Research Award (1999, 2012) TÜBİTAK Incentive Award (1996) Grants & Projects: He leads TÜBİTAK and TAEK projects on CMS detector upgrades, B-physics, and jet analysis. Recent projects include radiation damage monitoring (2021–present) and di-jet studies (2009–2013). His work also involves data analysis frameworks and machine learning techniques for background estimation. Teaching: He instructs core physics courses including Nuclear Physics (Physics 466), Elementary Particle Physics (Physics 472), and specialized topics in experimental physics.

Josip Arnerićمشاهده پروفایل

پژوهشگر

• Econometric methods and models
• Financial time-series and volatility
• Univariate and multivariate GARCH models
• +۳ مورد دیگر

Josip Arnerić, PhD, is a researcher at the Department of Statistics within the Faculty of Economics & Business at the University of Zagreb. His work focuses on econometric modeling, financial time-series analysis, and high-frequency data applications. Academic affiliation: Faculty of Economics & Business, University of Zagreb Research focus: Financial econometrics, volatility modeling, neural networks Email: jarneric@efzg.hr Research Interests Arnerić specializes in econometric methods for financial markets, with particular expertise in: Univariate and multivariate GARCH models Stochastic processes and volatility dynamics High-frequency data analysis Machine learning applications to financial forecasting Market risk modeling and portfolio optimization Publication Trends His work spans econometric modeling of stock market volatility (2023-2025), housing affordability analysis (2024), and neural network applications in inflation forecasting (2021). He has also explored: Price jump identification techniques Gold as safe-haven asset Interdependencies between traditional and cryptocurrency markets

Michael Zabarankinمشاهده پروفایل

استاد

• Optimization
• Inverse Optimization
• Risk Analysis
• +۱۵ مورد دیگر

Michael Zabarankin is a Professor and Department Chair in the Department of Mathematical Sciences at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. His research bridges applied mathematics, optimization, and real-world applications in finance, engineering, and environmental systems. His research interests include optimization under uncertainty, risk analysis, inverse optimization, machine learning, threat detection, network optimization, and fluid dynamics. He has made significant contributions to the theory of deviation and risk measures, particularly in portfolio optimization, and has developed mathematical models for electrohydrodynamic drop deformation and shape optimization in viscous flows. The recent trend in his publications shows a strong focus on risk modeling in finance (e.g., deviation measures, drawdowns, entropy), machine learning under adversarial conditions, and mathematical modeling of multiphase fluid systems. His work often combines theoretical rigor with practical applications in security, environmental science, and financial engineering. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Advising and Grants: While specific students and grants are not listed, Dr. Zabarankin has extensive collaborative research, particularly with Bogdan Grechuk, Stan Uryasev, and Alexander Molyboha, suggesting active mentorship and grant-funded projects in optimization and applied mathematics. Labs and Research Teams: Although no formal lab name is provided, his work in optimization, risk analysis, and fluid dynamics suggests leadership in research groups focused on mathematical modeling, decision science, and computational mechanics within the Department of Mathematical Sciences.

Liyuan Linمشاهده پروفایل

مدرس ارشد

• Actuarial Science
• Quantitative Risk Management
• Dependence and Risk Aggregation
• +۴ مورد دیگر

Liyuan Lin is a Senior Lecturer in the Department of Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He holds a PhD in Actuarial Science from the University of Waterloo, awarded in 2024. His academic work centers on advanced risk modeling and actuarial theory. Research Interests: Actuarial Science Quantitative Risk Management Dependence and Risk Aggregation Stochastic Control Risk Measures and Diversification Reinsurance Strategy and Market Competition His recent publications, appearing in top journals such as Management Science , Mathematics of Operations Research , and Scandinavian Actuarial Journal , reflect a strong focus on theoretical and applied aspects of risk modeling. Key themes include diversification measurement, optimal reinsurance under competition, calibration of risk models, and structural dependence in multivariate settings. His work often involves deep mathematical analysis and collaboration with leading scholars in actuarial mathematics. Scientific Awards: No awards listed in the provided text. Advising and Grants: There is no explicit information available regarding student supervision, grant funding, or advisory roles. However, given his active publication record and position as a Senior Lecturer, it is likely that he mentors students and participates in research projects, though specifics are not disclosed in the source material. Labs and Research Teams: No specific lab or research group is mentioned. However, his collaborations suggest active participation in the broader actuarial and quantitative risk research network, particularly with researchers at institutions connected to the University of Waterloo and international actuarial research centers.

Khizer Saeedمشاهده پروفایل

عضو هیئت علمی

• Robotics and Control
• Human-Robot Interaction
• Autonomous Robots
• +۱۸ مورد دیگر

Khizer Saeed is a Principal Lecturer and Academic Subject Leader in Engineering at the School of Architecture, Technology and Engineering, University of Brighton. He holds a DPhil in Engineering Sciences from the University of Oxford and has been a key figure in advancing research and education in mechanical engineering since joining the university in 2006. His educational background includes an undergraduate degree in Mechanical Engineering and a Master's in Thermal Sciences, culminating in a DPhil from Oxford, where he conducted research on fundamental combustion measurements as a Felix Scholar of Somerville College. Khizer's research spans Robotics and Control, Diagnostic Techniques, Clean Energy, and Nanoparticle Synthesis. He has developed novel systems for human-robot interaction in healthcare, defense, and industrial automation, and pioneered advanced diagnostics using laser and spectroscopic methods. His work in clean energy focuses on hydrogen, biofuels, and combustion control, while his nanoparticle synthesis research uses dry pyrolysis to produce functional metal oxides. His recent publications reflect a strong trend in intelligent robotics, energy-efficient systems, and sustainable fuels. The 2025 articles highlight integration of large language models with robotic manipulation, energy-efficient trajectory planning, and multilingual NLP for domestic robotics, indicating a shift toward AI-driven control systems. Earlier works focus on combustion diagnostics, biofuel stability, and nanoparticle synthesis, showcasing a consistent theme of innovation in energy and automation technologies. EPSRC-funded ATRACT project (£850K) on robotic casualty triage Human-Robot Interaction for Defence (£125K) Combustion Synthesis of Iron Oxide and Titania Nanoparticles (£125K each) BRITE project on sustainable energy for Africa (£10K) Zero Carbon electricity via ORC Systems (TSB, £125K) Development of biofuels testing facilities with Brighton Council and Commercial Fund Khizer has supervised several PhD students, including Dr. Xingming Lu (Chinese Academy of Science), Dr. Ayad Al Thuwaite (University of Babylon), Dr. Gelu Verghese (Airbus UK), Dr. James Pullen (EDT-Consulting UK), and Mr. Sajjad Husain (University of Brighton). He actively mentors research in human-robot interaction, clean energy, and advanced diagnostics. He has established the £1 million MOCAI (Multidisciplinary Robotics, Control and Artificial Intelligence) laboratory and a state-of-the-art bioenergy research lab at Brighton. These facilities support long-term, collaborative, and interdisciplinary research in robotics, sustainable energy, and advanced materials. The labs serve as platforms for innovation in automation, energy systems, and nanoparticle applications.

• Energy Systems
• Sustainable Energy
• Micro Gas Turbines
• +۷ مورد دیگر

Mohsen Assadi is a Professor at the Department of Energy and Petroleum Engineering, Faculty of Science and Technology, University of Stavanger (UiS), Norway. His work focuses on sustainable energy technologies, particularly micro gas turbines, hydrogen energy systems, geothermal applications, and AI-driven energy modeling. He actively collaborates with researchers across Europe and contributes to the clean energy transition through techno-economic and performance analyses. Research Interests: His research spans key areas in modern energy systems, including micro gas turbines , hydrogen and fuel-flexible combustion , geothermal energy , thermal energy storage , smart grids , and artificial intelligence applications in energy . He emphasizes techno-economic feasibility, decarbonization strategies, and system integration for residential, industrial, and urban environments. The recent publications highlight a strong trend toward AI-enhanced modeling for heating and cooling prediction, optimization of microgrids with hydrogen and renewables, and innovative energy storage solutions such as subsea pumped hydro and wastewater thermal reservoirs. His work bridges engineering fundamentals with real-world sustainability challenges. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While specific students or grants are not listed, Dr. Assadi frequently co-authors with early-career researchers and engineers, suggesting an active mentoring and advisory role in research projects. His publications appear in high-impact journals and conferences in energy and engineering, indicating sustained research funding and collaborative project involvement. Labs and Teams: Though not explicitly named, his research output suggests involvement in energy systems labs focused on microturbines, geothermal applications, and AI-based energy modeling at the University of Stavanger. He collaborates with interdisciplinary teams across thermodynamics, fluid mechanics, and sustainable urban infrastructure.

Annette Kopp-Schneiderمشاهده پروفایل

استاد

• Medical Biometry
• Biostatistics
• Clinical Trials
• +۷ مورد دیگر

Prof. Dr. Annette Kopp-Schneider is Head of Department at the German Cancer Research Center (DKFZ) and affiliated with the Faculty of Medicine at the University of Heidelberg. She holds a professorial rank and leads research in medical biometry with a focus on clinical trial methodology in precision oncology. Diploma in Mathematics, RWTH Aachen, 1984 Dr. rer. nat. in Mathematics, RWTH Aachen, 1987 Habilitation in Medical Biometry, University of Heidelberg, 1997 Her research interests include clinical trials for precision oncology , dose-response modeling , and biologically based mechanistic modeling of cell systems . She develops advanced statistical methods for trial design, particularly in pediatric oncology and settings with small sample sizes. Her work emphasizes rigorous control of type I error when borrowing historical data and optimal experimental designs for drug combinations. The recent publications reflect a strong trend in methodological biostatistics , particularly in Bayesian and frequentist approaches to clinical trial design , external data incorporation , and transparent evaluation in biomedical image analysis . Her contributions span oncology, toxicology, and translational medicine, with a consistent focus on statistical rigor and reproducibility. She has contributed to major initiatives such as the Pediatric Precision Oncology INFORM Registry, advancing personalized treatment for children with cancer. Her methodological work supports fair comparisons in hybrid and single-arm trials and improves the reliability of challenge-based research in medical imaging. Prof. Kopp-Schneider is actively engaged in grant-funded research and mentoring within her department. She leads a team focused on biostatistical innovation in clinical research, particularly in oncology settings where data are sparse and traditional designs face limitations. She is involved in collaborative teams working on biomedical image analysis challenges and pediatric cancer registries. Her lab contributes to open science through toolkits for challenge analysis and transparent reporting guidelines like BIAS (Transparent Reporting of Biomedical Image Analysis Challenges).

Jonas Dovernمشاهده پروفایل

استاد

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

Prof. Dr. Jonas Dovern holds the Chair of Statistics and Econometrics at the Department of Economics and Social Sciences at FAU. He has been a Professor since 2019, previously serving as Assistant Professor at Heidelberg University (2014–2019) and Visiting Professor at RWTH Aachen University (2016). His work focuses on econometrics, macroeconomics, and forecasting methodologies. He leads the Bachelor's program in Economics at FAU and is an Associate Editor for Empirical Economics . Education: PhD in Economics (University of Kiel, 2009–2013), Master in Econometrics (Maastricht University), and further training at institutions like the IMF and Kiel Institute. Research Interests: Forecasting accuracy, inflation expectations, economic crises impact on potential output, and firm behavior under uncertainty. His work bridges empirical analysis with policy-relevant insights. Publications highlight contributions to density forecast calibration, expectation dispersion analysis, and tax policy evaluation. Collaborations include projects on sustainable development and crisis management. His research often employs advanced statistical methods to address macroeconomic challenges. Teaching spans introductory macroeconomics to specialized courses in econometrics and forecasting. He actively contributes to academic communities through editorial roles and conference participations.

• Machine Learning
• Metabolomics
• Chemical Sensor Systems
• +۷ مورد دیگر

Santiago Marco Colás is a Full Professor in the Department of Electronics and Biomedical Engineering at the University of Barcelona and leads the Signal and Information Processing for Sensing Systems group at IBEC. His research focuses on advanced computational methods for chemical sensor arrays, gas chromatography-ion mobility spectrometry (GC-IMS), mass spectrometry, and NMR data analysis in metabolomics, biomedical diagnostics, and environmental monitoring. Developed open-source tools like GCIMS R-package for untargeted IMS data analysis Innovated drone-based chemical sensing systems for indoor/outdoor odor mapping Created IoT-enabled respiratory impedance devices with mobile app control Explored machine olfaction algorithms for biomarker discovery in colorectal cancer and COPD Investigated dielectric excitation methods for metal oxide semiconductor sensors His recent work addresses real-time odor quantification in wastewater plants, predictive modeling for postoperative complications, and low-cost IoT respiratory monitoring. Publications span sensor signal processing, calibration transfer techniques, and collaborative STEM education initiatives.

Jeongjin Leeمشاهده پروفایل

پژوهشگر

• Statistics
• Extreme Value Theory
• Environmental Statistics
• +۳ مورد دیگر

Dr. Jeongjin Lee is a Senior Research Associate at Lancaster University's School of Mathematical Sciences . His research focuses on statistical methods for modeling multivariate extremes and projecting environmental risks. Department: School of Mathematical Sciences Email: j.lee58@lancaster.ac.uk Dr. Lee's work explores extreme value theory , multivariate statistical modeling , and climate change risk assessment . He has developed innovative approaches like X-vine copula models for extremal dependence and calibration techniques for future projections. His recent publications in Journal of the Royal Statistical Society Series B and Environmental and Ecological Statistics highlight applications in environmental risk analysis , stochastic modeling , and climate data interpretation .

Cristina Malegoriمشاهده پروفایل

پژوهشگر

• Chemometrics
• Analytical Chemistry
• Near-Infrared Spectroscopy
• +۳ مورد دیگر

Cristina Malegori is a Researcher in the Department of Pharmacy at the University of Genoa , specializing in chemometrics and analytical chemistry . She teaches courses such as Data Analysis and Chemometrics for Master's programs in Pharmaceutical Chemistry and Technology. Department: Pharmacy Research Focus: Chemometric methods, NIR spectroscopy, TD-NMR applications Teaching: Data analysis tools for pharmacy students Her research combines data science with spectroscopic techniques to solve analytical challenges in material characterization, environmental studies, and pharmaceutical quality control. Recent work includes chemometric modeling of soil rare-earth elements and cross-linking prediction in rubber materials. Office hours are held at the Chemistry and Pharmaceutical and Food Technologies Section (Viale Cembrano, 4) or online via MS-Teams, with appointments arranged via email.

• Geotechnical Engineering
• Mining Engineering
• Soil Mechanics
• +۲ مورد دیگر

Irena Bagińska is a Researcher at the Department of Mining within the Faculty of Geoengineering, Mining and Geology at Wrocław University of Science and Technology. Her work focuses on geotechnical engineering applications in mining, particularly landslide hazard assessment, soil mechanics, and mine waste management. She utilizes advanced field testing methods like CPTu and SCPTu for soil characterization and foundation design. Primary Affiliation: Wrocław University of Science and Technology Faculty: Faculty of Geoengineering, Mining and Geology Department: Department of Mining Email: irena.baginska@pwr.edu.pl Research trends include: Reliability analysis of structures (sheet pile walls, retaining walls, piles) in spatially variable soils Integration of CPTu and SCPTu data for soil strength and filtration coefficient estimation Comparative studies of static (CPTu) and dynamic (DPH) testing methods Application of probabilistic and kinematic approaches to mining waste dumps and slope stability Case studies on Polish mining infrastructure like the Żelazny Most tailings facility Labs & Teams: Affiliated with the Department of Mining, contributing to geotechnical research and Eurocode 7 compliance studies in Polish mining contexts.

Nachiketa Sahooمشاهده پروفایل

دانشیار

• Machine Learning
• Information Systems
• Personalized Recommender Systems
• +۵ مورد دیگر

Nachiketa Sahoo is an Associate Professor in the Information Systems Department at Boston University's Questrom School of Business, where he has been faculty since 2011. His research sits at the intersection of Machine Learning and Information Systems, with particular focus on personalized recommender systems and user preference modeling. Research Focus: Dr. Sahoo's work centers on learning user preferences and decision-making processes from large disaggregate datasets. His primary projects include: Personalization and Matching: Developing systems to match items to users while examining stability and fairness implications Retail Analytics: Analyzing customer activity data to reduce product expiration through salesforce incentive alignment Data-driven Healthcare: Estimating value of genomic tests and physician decision modeling Content Recommendation: Studying multi-category utility models for diversified content consumption His recent publications demonstrate strong trends toward ethical implications of recommender systems, particularly regarding fairness and market stability, while maintaining technical depth in machine learning applications. Scientific Contributions: Developed novel methods for uncovering population response paths using time-series data Created approaches to match donors with philanthropic causes on crowdfunding platforms Designed salesforce compensation schemes reducing $15B+ in annual product waste Pioneered multi-category utility models for content recommendation Academic Leadership: Dr. Sahoo advises PhD students in Information Systems, with graduates securing tenure-track positions at institutions like University of Minnesota and University of South Florida. His teaching portfolio includes advanced machine learning courses and doctoral seminars on ML methods for social science research. He previously served as a Visiting Assistant Professor at Carnegie Mellon's Tepper School of Business. Education: He holds a PhD in Information Systems from Carnegie Mellon University's Heinz College, an MS in Knowledge Discovery and Data Mining from CMU's Machine Learning Department, and a B.Tech in Industrial Engineering from IIT Kharagpur. His industry experience includes software engineering at i2 Technologies (now JDA Software).