جست‌وجوی استاد و پژوهشگر

Alessandro Gabbanaمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Lattice-Boltzmann Method
• Turbulence Modeling
• +۳ مورد دیگر

Alessandro Gabbana is a University Researcher at Eindhoven University of Technology, affiliated with the Applied Physics and Science Education school and the Computational Multiscale Transport Phenomena group. His research focuses on computational physics, particularly lattice-Boltzmann methods and fluid dynamics. He co-leads the HTCrowd project (2020–2026), developing high-tech platforms for human crowd flow monitoring and modeling. Research interests span: Advanced computational fluid dynamics (CFD) Turbulence subgrid modeling using data-driven approaches Pedestrian dynamics and probabilistic flow analysis Neural network integration with kinetic schemes His publications demonstrate strong emphasis on improving lattice-Boltzmann boundary conditions, turbulence closures, and large-scale pedestrian dynamics simulations.

Kerstin Lux-Gottschalkمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Stochastic Differential Equations
• Optimal Control Theory
• +۴ مورد دیگر

Kerstin Lux-Gottschalk is an Assistant Professor at the Department of Mathematics and Computer Science , Eindhoven University of Technology, affiliated with the Centre for Analysis, Scientific Computing and Applications (CASA) . She specializes in uncertainty quantification, stochastic differential equations, and optimal control, with applications to climate science, epidemiology, and ecology. Education : B.Sc. and M.Sc. from University of Mannheim, Germany; semester abroad at Université Nice Sophia Antipolis, France; Ph.D. from University of Mannheim (2020) under Prof. Dr. Simone Göttlich. Postdoctoral Research : Technical University of Munich (2020–2023) in Multiscale and Stochastic Dynamics group. Her research focuses on quantifying uncertainty in tipping points of complex systems, including: Analysis of random ordinary differential equations Climate modeling of Atlantic meridional overturning circulation Non-Markovian bifurcation detection Reinforcement learning for control systems Recent publications explore uncertainty quantification of tipping thresholds, stochastic control in transport systems, and numerical methods for SDEs. She contributes to UN Sustainable Development Goals related to climate action and sustainable infrastructure.

• Econometrics
• Operations Research
• Household Economic Analysis
• +۴ مورد دیگر

Dr. Marta Dziechciarz is a faculty member at Wrocław University of Economics, affiliated with the Department of Econometrics and Operations Research. She teaches courses in econometrics, financial data modeling, and programming in R. Current position: Lecturer Department: Econometrics and Operations Research University: Wrocław University of Economics Contact: marta.dziechciarz@ue.wroc.pl Her research focuses on: Subjective well-being and income expectations in households Fuzzy set theory applications in economic measurement Market segmentation techniques for durable goods Non-monetary poverty factors and housing economics Multivariate analysis of consumer behavior Econometric modeling of household welfare Key publication trends include: Panel data analysis of European well-being (2024) Fuzzy logic applications for perception measurement (2020) Comparative studies across Central European countries (2021) Consumer durables and housing market analysis (2017-2018) Human capital and training needs identification (2016) She utilizes both traditional econometric methods and unconventional fuzzy number coding for complex economic analyses. Teaching activities include: Applied econometrics Dynamic and financial econometrics Computer tools for data analysis Financial data modeling and forecasting Programming in R

Debarghya Ghoshdastidarمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Network Science
• +۳ مورد دیگر

Prof. Debarghya Ghoshdastidar is an Assistant Professor of Theoretical Foundations of Artificial Intelligence at Technische Universität München (TUM) since 2019. He holds a Tenure Track position within the TUM School of Computation, Information and Technology. His research focuses on the statistical foundations of machine learning, network science, and their applications in neuroscience, crowdsourcing, and computer vision. Education: B.E. in Electrical Engineering from Jadavpur University (2010), M.S. and Ph.D. in Computer Science from Indian Institute of Science (2016). Following his PhD, he conducted postdoctoral research at the University of Tübingen, leading a Baden-Württemberg Foundation-funded junior research group. Research Interests: Theoretical machine learning, network analysis, statistical learning theory, and algorithmic guarantees for AI systems. His work bridges abstract theoretical frameworks with real-world applications, particularly in understanding the robustness and generalization of learning algorithms. Awards: Recipient of the Baden-Württemberg Elite Program for Postdocs (2017) and Google Ph.D. Fellowship (2013). His contributions include novel algorithms for graph hypothesis testing and spectral hypergraph partitioning. Grants and Advising: Led a junior research group in Tübingen; no explicit student lists provided here. Active in theoretical contributions to AI, with a focus on interpretable and robust machine learning.

Phaedon-Stelios Koutsourelakisمشاهده پروفایل

استاد

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +۳ مورد دیگر

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.

Thanos Avramidisمشاهده پروفایل

مدرس

• Operational Research
• Stochastic Simulation
• Financial Engineering
• +۳ مورد دیگر

Dr. Thanos Avramidis is a Lecturer in Operational Research at the School of Mathematical Sciences, University of Southampton. His research focuses on Markov Decision Processes , stochastic simulation , and financial derivatives pricing , with applications in dynamic pricing under demand uncertainty. He has held academic roles at Cornell University and research positions at the University of Montreal, collaborating with Prof. Pierre L'Ecuyer. Education & Career: PhD in Industrial Engineering, Purdue University Assistant Professor, Cornell University (1997–2001) Researcher at University of Montreal (2002–2006) Joined University of Southampton in 2007 Research Themes: Current work develops algorithms for pricing in unknown demand environments. Past contributions include variance reduction methods, call-center optimization models, and Monte Carlo analysis in finance. Awards: Recipient of the INFORMS Simulation Society Award (2009) and George Nicholson Prize (1993). Teaching: Courses include Introduction to Operational Research and Stochastic OR Methods . Lab/Teams: Member of the CORMSIS research group.

• Multi-Label Learning
• Probabilistic Inference
• Explainable AI
• +۷ مورد دیگر

Jesse Read is a Professor at École Polytechnique (Institut Polytechnique de Paris) affiliated with the ORAILIX team within the Data Analytics and Machine Learning pôle of the Computer Science Laboratory (LIX). His work focuses on multi-label and probabilistic inference , explainable and robust methods , and learning from data streams and sequential data with applications in medicine, energy, and transportation. He obtained his PhD from the University of Waikato (2010) and held postdoc positions at INFRES Télécom Paris, Aalto University, and Universidad Carlos III de Madrid before becoming Assistant Professor (2017) and Professor (2019) at École Polytechnique. Research Trends His recent publications emphasize multi-label learning (Classifier Chains, Regressor Chains) and data stream analysis with applications in degradation modeling and medical diagnostics. Key methodological contributions include probabilistic inference in chained models, scalable ensemble learning , and Monte Carlo optimization for sequential tasks. Application domains span healthcare (ECG analysis, insomnia detection), energy systems (wind turbine control), and transportation (route prediction). Scientific Awards Test of Time Award at ECML-PKDD 2019 for foundational work on Classifier Chains Habilitation à Diriger des Recherches (HDR) in Computer Science (2017) Advising & Grants Read has supervised PhD theses from Ekaterina Antonenko (Multi-Target Learning) and Olivier Pallanca (Paradoxical Insomnia Analysis). He secured an ANR grant for Dynamic Graph Signal Processing with Dr. Johannes Lutzeyer and Dr. Luca Martino.

Thomas S Richardsonمشاهده پروفایل

استاد

• Graphical Models
• Causal Inference
• Bayesian Inference
• +۳ مورد دیگر

Thomas S Richardson is a Professor at the University of Washington, holding adjunct appointments in Economics and Electrical Engineering. He is also affiliated with the eScience Steering Committee and Center for Advanced Studies in the Behavioral Sciences at Stanford University (Fellow, 2004). His research focuses on graphical models, algorithmic model selection, Bayesian inference, causal models, and their applications in economics and biostatistics. BA, University of Oxford MS and PhD, Carnegie Mellon University Key research areas include: Causal Inference: Instrumental variable models, counterfactuals, SWIGs (Single World Intervention Graphs), and identification of direct effects. Graphical Models: Acyclic directed mixed graphs, nested Markov properties, marginal log-linear parametrizations, and latent variable modeling. Applications: Epidemiology, econometrics, bone biology, and ecological inference. His recent publications emphasize causal effect bounds, finite population testing, and structural nested mean models. Articles span topics like SWIGs, IV model analysis, and high-dimensional causal discovery. Scientific Awards: Fulbright Fellowship (2014) Institute for Advanced Studies, University of Bologna (2007) Center for Advanced Studies in Behavioral Sciences, Stanford (2004) Rosenbaum Fellow, Isaac Newton Institute (1997) Rousseeuw Prize for Statistics (2022)

• Machine Learning
• Statistical Inference
• High Dimensional Data Analysis
• +۵ مورد دیگر

Donghui Yan is a Postdoctoral Researcher at the University of California, Berkeley, affiliated with the Department of Statistics. His research spans machine learning, statistical inference, and biomedical data analysis. Research interests include: Clustering algorithms and dimensionality reduction Deep learning for big data and hydropower forecasting Biomedical applications in genomics and medical imaging Recent publications emphasize fairness in AI, distributed data analysis, and pandemic modeling. His work integrates machine learning with environmental, biological, and healthcare domains.

Raid Aminمشاهده پروفایل

استاد

• Statistical Process Control
• Geospatial Cluster Analysis
• Environmental Statistics
• +۷ مورد دیگر

Dr. Raid Amin is a Distinguished University Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He has been a faculty member since 1987 and is recognized for his contributions to statistical research and education. His work bridges academia and real-world applications through collaborations with healthcare institutions and environmental agencies. Education: B.S. in Statistics, Baghdad University, Iraq M.S. in Statistics, Virginia Polytechnic Institute and State University Ph.D. in Statistics, Virginia Polytechnic Institute and State University Dr. Amin's research focuses on statistical process control , geospatial cluster analysis , and applied statistics in public health and environmental science . His work applies advanced statistical methods to critical societal issues such as crime pattern detection, cancer cluster identification, and human-shark interactions. He also contributes to medical education by providing statistical consulting for clinical residency projects at Sacred Heart Hospital and West Florida Hospital. His recent publications reflect a strong trend in using SaTScan and spatial statistics for identifying clusters in environmental, health, and criminological data. Articles span journals like Technometrics , Journal of Quality Technology , and Animal Cognition , demonstrating interdisciplinary reach across engineering, ecology, and public policy. Scientific Awards: UWF Faculty Catalyst Initiative Award Dr. Amin has secured grants and institutional partnerships, most notably winning a proposal that led to UWF becoming an official partner in the EPA’s Toxic Release Inventory University Challenge Program. He actively involves undergraduate and graduate students in research, particularly in analyzing EPA datasets. He also serves as a reviewer for top-tier journals including Technometrics and Journal of Quality Technology , contributing to the scholarly community. His work in statistical consulting supports both academic and clinical training programs. He is affiliated with research initiatives involving the EPA and leads student-focused research courses in environmental data analysis. His lab or research group emphasizes hands-on learning and real-world data applications, fostering statistical literacy and analytical skills among students.

• Applied Probability
• Insurance Mathematics
• Statistics
• +۳ مورد دیگر

Martin Rainer Bladt is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, focusing on Applied Probability and Insurance Mathematics. He works on statistical and stochastic modeling, particularly in Markov processes and risk theory. Research trends include Markov jump processes, phase-type distributions, survival analysis, and applications in insurance and financial risk modeling. His recent publications emphasize methodological developments in actuarial science, extreme value theory, and homogeneous approximation techniques for inhomogeneous processes.

• Quantum Technologies
• Photonic Engineering
• Integrated Photonics
• +۳ مورد دیگر

Professor James Gates is a Professorial Fellow (Research) at the University of Southampton, specializing in photonic engineering, quantum technologies, and ultra-precision fabrication. With over 25 years' experience, he leads a research team developing proprietary techniques like flame hydrolysis glass deposition and air-bearing stabilized laser inscription for applications in quantum computing, telecommunications, and aviation sensing. Quantum Technologies Integrated Photonics Ultra-precision Machining His research focuses on overcoming fabrication challenges in quantum systems, including optical waveguides, superconducting components, and atom trap technologies. Recent work involves intermodal four-wave mixing for broadband wavelength conversion and diamond machining of photonic circuits. Current projects include EPSRC-funded initiatives UPROAR and PURE, plus leadership roles in the Quantum Computing Hub and CDT in Quantum Technology Engineering. His team has contributed to over £100m in research grants. Research group award: Highly Commended (2017) Vice Chancellor's Award (2012) Professor Gates supervises multiple PhD students and collaborates on international projects, including quantum hubs in Oxford and Birmingham. His work bridges academic research with commercial applications through patented fiber assembly methods and invited technical reviews.

Jason Hiltonمشاهده پروفایل

دانشیار

• Demographic Forecasting
• Bayesian Statistics
• Human Mortality
• +۴ مورد دیگر

Jason Hilton is an Associate Professor in Social Statistics at the University of Southampton's Faculty of Social Sciences, specializing in demographic methods and data science. His work bridges academic research and practical applications through collaborations with government bodies and international organizations. His research focuses on probabilistic forecasting of demographic components (mortality, fertility, migration) and kinship modeling , employing Bayesian statistics and agent-based simulation techniques. Key interest areas include demographic forecasting, human mortality analysis, and modeling intergenerational relationships. Hilton's publications reveal strong thematic continuity in demographic methodology, with recent work emphasizing kinship dynamics (2024-2025), migration flow prediction (2021), and Bayesian mortality modeling (2021). His research consistently integrates computational approaches with real-world demographic challenges. As a supervisor, he currently guides four PhD students in Social Statistics and Demography: Andrew Peter Hind (MPhil/PhD), Md Sifat Ar Salan, Shu Li, and Clara Renee Girault. His research is supported by major grants including ESRC's 'Connecting Generations' project and the Bill & Melinda Gates Foundation's WorldPop initiative. Hilton contributes to the Centre for Population Change and Demographic and Migration Modelling research groups, focusing on projects like 'Modelling kinship and quantifying connections between generations' and 'FuturePop' for high-resolution population projections.

• Economics
• Behavioral Economics
• Cultural Economics
• +۴ مورد دیگر

Avner Seror is a CNRS Research Fellow at Aix-Marseille University's School of Economics, conducting high-impact research at the intersection of economics, culture, and institutions. His position within France's National Center for Scientific Research (CNRS) places him in a dedicated research track focused exclusively on scholarly output without teaching requirements. Seror's research program explores how cultural and religious frameworks shape economic behavior and institutions. Key themes include the impact of religious rituals on judicial decision-making, the co-evolution of cultural norms and economic prohibitions, behavioral biases in survey responses, and child development dynamics. His work combines theoretical modeling with empirical analysis, often leveraging natural experiments and innovative methodologies like the Priced Survey approach. His publication record shows consistent output in top economics journals including Nature Human Behaviour, Journal of Economic Growth, and Journal of Political Economy, with recent work increasingly addressing AI ethics and addiction economics. The research demonstrates strong thematic continuity in examining cultural-religious-economic interactions across historical and contemporary contexts. Scientific recognition includes: SIOE Ronald Coase Medal for 'Religious Leaders and Rule of Law' (2023) As a CNRS researcher, Seror operates within France's structured public research funding system, with primary support from CNRS core funding and competitive project grants. His collaborative network spans international institutions, though the source materials do not specify current grant details or student supervision activities. No dedicated research laboratory or center is associated with his profile; his work appears conducted through standard CNRS/university research frameworks with emphasis on individual and collaborative projects rather than lab-based structures.

Mehrisadat Makki Alamdariمشاهده پروفایل

مدرس ارشد

• Structural Health Monitoring
• Structural Dynamics
• Signal Processing
• +۳ مورد دیگر

Dr. Mehrisadat Makki Alamdari is a Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She specializes in Structural Health Monitoring (SHM), structural dynamics, signal processing, and data mining. Her research bridges theoretical innovations with real-world infrastructure applications, including landmark projects on the Sydney Harbour Bridge and Gateway Bridge. Education: Ph.D. in Civil Engineering, University of Technology Sydney (2012–2014) M.Sc. in Mechanical Engineering, University of Manitoba (2011) M.Sc. in Aerospace Engineering, Iran University of Science and Technology (2006–2008) B.Sc. in Aerospace Engineering, Sharif University of Technology (2002–2006) Research Focus: Her work integrates machine learning, sensor technologies, and advanced signal processing for infrastructure resilience. Key areas include bridge health monitoring via drive-by inspections, piezoelectric energy harvesting, and damage detection using ultrasonic and computer vision techniques. Recent publications emphasize data-driven methodologies, unsupervised learning, and multi-sensor fusion for structural diagnostics. Awards & Honors: ARC Discovery Early Career Research Award (DECRA) JSPS Award (Japan Society for Promotion of Science) ITS Australia National Award (2020) Most Practical SHM Solutions Award (Stanford University, 2015) Best Paper Award (SHMII-8, 2017) Leadership & Service: She serves on the executive committee of the Australian Network of Structural Health Monitoring (ANSHM) and is an Associate Editor for Elsevier journals. She has secured over $5.5M in grants, including an ARC DECRA grant and a $5M Industrial Transformation Hub for resilient infrastructure.