جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Bayesian Statistics
• +۵ مورد دیگر

Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.

• Statistics
• Categorical Data Analysis
• Survey Methodology
• +۳ مورد دیگر

Thomas Suesse is a Senior Lecturer at the School of Mathematics and Applied Statistics, University of Wollongong, with a career spanning institutions in Germany, New Zealand, and Australia. His academic journey includes a M.Sc. in Mathematics from Friedrich-Schiller-University Jena (2003) and a PhD in Statistics from Victoria University of Wellington (2008). Education: M.Sc. in Mathematics (FSU Jena, 2003), PhD in Statistics (VUW, 2008) His research focuses on Statistics, particularly Categorical Data Analysis, Social Network Modeling, and Spatial Statistics. Recent work explores variational Bayes inference for spatial autoregressive models, environmental extreme value detection algorithms, and the impact of missing data in statistical models. His publications span topics in public health, environmental science, and educational methodology. Key trends in his 15 most recent articles (2021-2025) include advancements in spatial statistics, applications of social network analysis to finance, and educational research comparing laboratory teaching objectives. He has also contributed to understanding pandemic effects on child health and activity patterns. Scientific Awards: 2008 VUW PhD thesis submission award; 2005-2008 VUW Postgraduate Scholarship. Thomas co-supervises PhD students on topics like spatial autoregressive modeling with missing data and variance estimation in mixture models. He has secured internal grants for computational infrastructure upgrades and studies on child movement behaviors. His professional affiliations include the Statistical Society of Australia.

Ben Jeurissenمشاهده پروفایل

استاد پژوهشی

• Medical Image Computing
• Quantitative Magnetic Resonance Imaging
• Diffusion MRI
• +۴ مورد دیگر

Prof. Dr. Ben Jeurissen is an Associate Research Professor at the Department of Physics, University of Antwerp , Belgium, specializing in Medical Image Computing , Quantitative MRI , and Diffusion MRI . He received an ERC Consolidator Grant (2023) and FWO Senior Postdoctoral Fellowship (2018–2021) , with a focus on data-driven approaches to study brain microstructure. Education : MSc in Computer Science (2004), Biomedical Imaging (2006), PhD in Science (2012) from University of Antwerp. Research : His work bridges Neuroscience , Medical Imaging , and Computational Methods , particularly in Diffusion MRI and Quantitative MRI for applications in Alzheimer’s disease , spaceflight effects on the brain, and knee imaging . Awards : ERC Consolidator Grant (2023) Australian Museum Eureka Prize Finalist (2024) Multiple Magna/Summa Cum Laude Merit Awards at ISMRM conferences Prize Robert Oppenheimer (2015) Publications span NeuroImage , Human Brain Mapping , Journal of Alzheimer's Disease , and Investigative Radiology , with key contributions to super-resolution MRI , fiber tracking , and brain microstructure analysis . He serves as a contributor to MRtrix and advisor for PhD theses in Computational Anatomy and Medical Imaging .

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۳ مورد دیگر

Zhiqiang Que is a Research Associate in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering and the Centre for High-Throughput Digital Electronics and Machine Learning. His research focuses on computer architecture, embedded systems, high-performance computing, and CAD tools for hardware design optimization. His research interests include FPGA-based acceleration of machine learning models, hardware-software co-design, and real-time signal processing for scientific applications such as particle physics and gravitational wave experiments. He has contributed to projects involving low-latency graph neural networks (GNNs), Bayesian neural networks, and efficient stream processing on FPGAs. Recent work includes advancements in trustworthy design flows for deep learning acceleration, reconfigurable architectures for recurrent neural networks, and optimizing FPGA-based systems for high-energy physics experiments at the HL-LHC.

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

Professor Nesrin Okay holds a Ph.D. in Economics from The City University of New York and is affiliated with Bogazici University. Her research focuses on Financial Engineering, Economics, Econometrics, and Statistical Analysis of Financial Markets, with particular emphasis on Stock Market Modeling and Forecasting, and Time Series Analysis. Education: Ph.D. in Economics, The City University of New York Her research interests include advanced econometric methodologies applied to financial markets, particularly in the context of emerging markets like Turkey. She explores topics such as volatility modeling, liquidity risk analysis, and the impact of renewable energy on economic indicators. Her work also examines structural breaks in economic data and policy uncertainties affecting financial systems. Publications span over two decades, with recent works addressing renewable energy’s role in current account dynamics and the Turkish banking system’s stability. Earlier research includes volatility modeling in financial markets and empirical analyses of liquidity risks on the Istanbul Stock Exchange. No scientific awards are listed. Advising and grants details are not provided in the text. No specific labs or teams are mentioned.

• Process Systems Engineering
• Chemical Engineering
• Energy Conversion Systems
• +۴ مورد دیگر

Prof. Kai Sundmacher is a Full Professor and Chair of Process Systems Engineering at Otto-von-Guericke University Magdeburg, and Director of the Max Planck Institute for Dynamics of Complex Technical Systems. His expertise spans model-based analysis of complex process systems, integrated chemical processes, energy conversion (fuel cells/electrolysis), and biosystems engineering. He holds a Dr.-Ing. habil. from Clausthal University of Technology and has held visiting positions at institutions worldwide. Research focuses include catalyst design for CO2 methanation, digital twins for power-to-X processes, and sustainable chemical production. Awards include the Arnold Eucken Prize (1999) and Carl Zerbe Prize (1998). He leads initiatives like the Max Planck Research Network for Synthetic Biology and chairs the German Chemical Energy Research group. Teaching covers advanced process systems engineering, fuel cell technology, and bioprocess design. Over 500 publications include 400 peer-reviewed articles, with contributions to journals like Chemical Engineering Science and Fuel Cells. Active in academic service, he reviews ERC grants, chairs selection committees, and advises the Alexander von Humboldt Foundation.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +۴ مورد دیگر

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

Jerry Brunnerمشاهده پروفایل

استاد

• Statistical Models
• Measurement Error
• Structural Equation Models
• +۳ مورد دیگر

Jerry Brunner is a retired Professor in the Department of Statistical Sciences at the University of Toronto, where he served from 1989 until his retirement on July 1, 2024. He holds a Ph.D. in Statistics (State University of New York, 1989) and an earlier Ph.D. in Social Psychology (University of Chicago, 1978). His research focuses on statistical models incorporating measurement error, parameter identifiability, and structural equation models. Notable contributions include work on z-curve methodology for estimating replicability and applications of Groebner basis techniques in statistical modeling. He developed open-source educational resources, including the Structural Equation Models: An Open Textbook project and contributed to SageMath workshops for statistical computation. His teaching and outreach efforts span advanced topics like Bayesian classification and mixed models analysis. Publications highlight methodological innovations in meta-analysis, error handling in regression, and algebraic approaches to statistical modeling. Though retired, he maintains involvement in academic activities and occasionally checks email.

• Structural Health Monitoring (SHM)
• Machine Learning
• Pattern Recognition
• +۵ مورد دیگر

Professor Nikolaos Dervilis is a faculty member in the Department of Mechanical Engineering at the University of Sheffield, serving as Director of Research and Innovation for the School of Mechanical, Aerospace and Civil Engineering. He holds a BSc from the National and Kapodistrian University of Athens, an MSc in Sustainable and Renewable Energy Systems from the University of Edinburgh, and a PhD from the University of Sheffield in Mechanical Engineering with a focus on machine learning for Structural Health Monitoring (SHM). His research emphasizes SHM, renewable energy systems (particularly wind turbines), data analysis, nonlinear dynamics, and advanced signal processing. His work spans population-based SHM (PBSHM), machine learning applications in structural dynamics, and probabilistic modeling. Recent publications focus on active learning frameworks, Bayesian methods, and generative models for damage prognosis. He collaborates with industry on wind energy and has contributed to datasets for experimental bridges and aerospace components. Notably, he leads efforts in transfer learning and domain adaptation for heterogeneous structural populations. Research highlights include developing frameworks for risk-informed decision support, model selection via approximate Bayesian computation, and digital twin tools for engineering systems. His lab, part of the Dynamics Research Group, addresses challenges in energy systems, composite materials, and condition monitoring of critical infrastructure.

• Medical Imaging
• Nuclear Medicine Physics
• Biomedical Engineering
• +۳ مورد دیگر

Roger Fulton is a Professor of Medical and Preclinical Imaging at the University of Sydney and a Principal Nuclear Medicine Physics Specialist at Westmead Hospital's Department of Medical Physics. He is part of the Brain and Mind Centre and leads the Physics and Biomodelling team. His roles include Conjoint Professor in Medical Radiation Sciences, RHD student supervision, and coordination of the MRTY5134 (Computed Tomography Theory) and MRTY2107 (Imaging Technology 2) units of study. He holds a PhD in Applied Physics. His career has spanned collaborations with institutions like the Research Center Julich (Germany), Massachusetts General Hospital (USA), and the International Atomic Energy Agency (Austria). Professional recognitions include Fellow of ACPSEM, Senior IEEE Membership, and Chair of the Nuclear Medical Imaging Sciences Council (IEEE). Research interests focus on motion tracking/correction for SPECT, PET, and CT imaging, including patent innovations and leadership in radiation dose reduction techniques. He has secured $9.2M in competitive grants since 2004 and contributed to global standards through IAEA initiatives. Grants and advising: Over $9.2M in grants since 2004, advising PhD student Bader ALOUFI on liver imaging kinetics. His work bridges hardware engineering (e.g., Intel RealSense depth cameras) with software solutions (neural networks, Bayesian methods) to enhance imaging accuracy and reduce patient motion artifacts. Scientific Awards: US Patent (2020) ARC College of Experts (2016–2018) ACPSEM Fellowship IEEE Senior Membership IEEE Nuclear and Plasma Sciences Society leadership Labs/Teams: Physics and Biomodelling team at the Brain and Mind Centre, collaborating with international groups on preclinical imaging systems and awake-animal PET methodologies.

Petros Dellaportasمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +۷ مورد دیگر

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.

Serge Guillasمشاهده پروفایل

استاد

• Functional Data Analysis
• Uncertainty Quantification
• Environmental Statistics
• +۴ مورد دیگر

Professor Serge Guillas is a faculty member at the University College London (UCL) Department of Statistical Science. His research focuses on functional data analysis, uncertainty quantification, environmental statistics, and emulation of complex computer models. He leads the NERC consortium on Uncertainty Quantification of Natural Hazards and has held roles such as Work Package Leader for quantifying uncertainties in natural hazard models. His work integrates statistical methods with geophysical and climate modeling, emphasizing tsunami risk analysis, climate dynamics, and ozone exposure studies. Education: PhD in Statistics from Paris 6 University (2001), followed by roles at the University of Chicago, Georgia Institute of Technology, and UCL. Current roles include teaching STAT1006 and STAT7001 courses. Research interests span functional regression, spatial data analysis, and probabilistic hazard modeling. Recent work explores machine learning-driven climate models, ozone exposure health impacts, and real-time data assimilation software (ParticleDA.jl). He collaborates globally, including with institutions in Georgia, Italy, and Indonesia, to advance tsunami modeling and disaster risk reduction. Key awards include ESRC-DFID-NERC funding, MAPS Faculty Postgraduate Research Prize (for student Ah Yeon Park), and leadership roles in SIAM’s Uncertainty Quantification group. Active in mentoring PhD students and securing interdisciplinary grants. Labs/Teams: Involved with the UCL Institute for Risk & Disaster Reduction and leads statistical emulation efforts in climate and geophysical modeling. Collaborates on fusion reactor design (ExCALIBUR project) and global temperature uncertainty quantification (GETQUOCS initiative).

Susan Frankenbergمشاهده پروفایل

پژوهشگر ارشد

• Anthropology
• Paleodemography
• Museum Studies
• +۱۰ مورد دیگر

Susan Frankenberg is a Research Affiliate and Graduate Faculty member in Anthropology at the University of Illinois at Urbana-Champaign, with a PhD and MA in Anthropology from Northwestern University and a BA in Anthropology from Indiana University. She also serves as Coordinator of the Museum Studies Program, focusing on interdisciplinary collaborations between museums and academic research. Education: PhD, MA, and BA in Anthropology. Research Interests span museum organizational behavior, stakeholder engagement, and cultural policy development, alongside quantitative methods in biological anthropology. Her work integrates museum informatics and collections data systems with Bayesian modeling, addressing questions in demography, health, and migration patterns of past and present human populations. Recent Publications highlight her expertise in paleodemographic reconstruction, skeletal analysis, and statistical methodologies, including stature estimation from long bones, dental age thresholds, and computational approaches like Markov Chain Monte Carlo methods. Despite no explicitly listed scientific awards, her contributions to museum studies and anthropological demography underscore her academic impact. Professional Roles include mentoring graduate students in Anthropology, though specific advisees are not named, and advancing museum data federation protocols through the Museum Studies Program.

Ken Kitsonمشاهده پروفایل

استاد مهمان

• Causal Discovery
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Ken Kitson is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on causal discovery, Bayesian networks, and machine learning methodologies. He specializes in developing algorithms that infer causal relationships from data, particularly through active learning frameworks that integrate human expert knowledge. Kitson's work emphasizes probabilistic graphical models and their applications in diverse fields such as healthcare, sports analytics, and epidemiology. He has contributed to improving the stability and accuracy of Bayesian network structure learning algorithms, addressing challenges like variable ordering instability and noisy data. His recent research trends highlight advancements in causal machine learning, including the use of large language models like GPT-4 to enhance causal discovery processes. His studies on sepsis and COVID-19 demonstrate practical applications of causal modeling in health informatics. Kitson's publications span algorithmic improvements, surveys of Bayesian network learning techniques, and empirical validations of methods in real-world scenarios. He holds a focus on bridging theoretical advancements with practical implementations in data-driven decision-making contexts.

Vaibhav Srivastavaمشاهده پروفایل

دانشیار

• Cyber Physical Human Systems
• Networked Multi-Agent Systems
• Mathematical Neuroscience
• +۳ مورد دیگر

Vaibhav Srivastava is an Associate Professor in the Electrical and Computer Engineering Department at Michigan State University (MSU), with affiliations in Mechanical Engineering and the Cognitive Science Program. He holds a Ph.D. and M.A. from the University of California, Santa Barbara, and a BTech from the Indian Institute of Technology Bombay. His research focuses on Cyber-Physical Human Systems, including networked multi-agent systems, mathematical neuroscience, and autonomous robotics. He has held leadership roles in conferences like the American Control Conference and serves as an Associate Editor for the ASME Journal of Dynamical Systems and IEEE Open Journal of Control Systems. Education: Ph.D. in Mechanical Engineering, University of California, Santa Barbara (2012) M.A. in Statistics, University of California, Santa Barbara (2012) M.S. in Mechanical Engineering, University of California, Santa Barbara (2011) BTech in Mechanical Engineering, Indian Institute of Technology Bombay (2007) Research Interests: Prof. Srivastava’s work bridges human decision-making with engineering systems, emphasizing mixed human-robot teams, networked systems, and mathematical models of neural processes. His projects address challenges in autonomous vehicles, surveillance strategies, and collaborative robotics, supported by grants from NSF, ONR, and the Army Research Office. Recent Trends in Articles: His publications explore adaptive control strategies for UAVs, trust-aware autonomy, and multi-fidelity models for human-robot collaboration. Key themes include robust trajectory estimation, impedance tuning, and epidemic modeling in networked environments. Awards: Best Student Paper Award (coauthor), European Control Conference 2014 Grants & Advising: He has secured funding for projects on data-driven control and human-in-the-loop systems. His advising includes contributions to robotics and control theory, with a focus on interdisciplinary approaches combining cognitive science and engineering. Labs/Teams: Engaged in collaborative efforts across MSU’s College of Engineering, focusing on soft robotics, decision-making algorithms, and autonomy in dynamic environments.