جست‌وجوی استاد و پژوهشگر

George Streftarisمشاهده پروفایل

استاد

• Bayesian Statistics
• Epidemiology
• Actuarial Science
• +۳ مورد دیگر

Professor George Streftaris is a faculty member at Heriot-Watt University within the Actuarial Mathematics and Statistics department under the School of Mathematical and Computer Sciences . His academic career spans over two decades, including roles as associate professor and lecturer at Heriot-Watt University (2004-2019) and post-doctoral positions at BioSS and Heriot-Watt (2001-2004). He serves on the Board of Examiners for the Institute and Faculty of Actuaries and acts as an external examiner for multiple institutions. Professional memberships include Fellow of the Royal Statistical Society , member of the International Society for Bayesian Analysis , and the Greek Statistical Institute . Education: PhD in Statistics (University of Edinburgh) MSc in Statistics and OR (University of Essex, Distinction) BSc in Statistics and Actuarial Science (University of Piraeus, Greece) Research Interests: Streftaris specializes in Bayesian stochastic modeling , inference, and assessment at the intersection of statistics, epidemiology, and actuarial science. His work addresses critical illness insurance, longevity risk, and health-related insurance through predictive modeling and statistical machine learning. Key themes include disease transmission dynamics, model diagnostics, and uncertainty quantification in epidemic systems. Collaborations extend to life and biomedical sciences. Recent Publications: Recent articles focus on COVID-19 pandemic impacts on breast cancer mortality using semi-Markov models, neural network approaches for admission rate prediction, and Bayesian modeling of epidemic systems. Notable projects involve machine learning for multi-asset strategies, model uncertainty in insurance pricing, and stochastic frameworks for disease spread. Research Projects: Centers of Actuarial Excellence (SOA, 2019-2023): Predictive modeling for medical morbidity risk SCOR Foundation of Science (2022-2024): Breast cancer life insurance impact ARC Project (IFoA, 2016-2022): Longevity and morbidity risk management The Data Lab (2017-2018): Machine learning for multi-asset strategies Advising: Supervises ongoing PhD students in Bayesian and neural network modeling in epidemiology, with completed students working on topics like critical illness insurance, disease transmission, and stochastic mortality. Collaborations include researchers in the UK, USA, and international institutions.

Mathieu Fontaineمشاهده پروفایل

دانشیار

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +۹ مورد دیگر

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.

Bert de Vriesمشاهده پروفایل

استاد

• Bayesian Machine Learning
• Signal Processing
• Biomedical Applications
• +۳ مورد دیگر

Bert de Vries is a Professor at the Signal Processing Systems Group at Eindhoven University of Technology (TU/e), where he has been employed since January 2012. He maintains a dual career, also working at GN Hearing in the hearing aids industry since April 1999, where he holds both research and managerial roles. His academic journey began at TU/e, where he earned his MSc in Electrical Engineering in 1986, followed by a PhD from the University of Florida in 1991. Between 1992 and 1999, he worked at Sarnoff Research Center in Princeton, NJ, contributing to diverse signal and image processing projects. Professor de Vries's research centers on Bayesian Machine Learning, with particular focus on the Free Energy Principle and its applications to engineering problems. His work bridges theoretical neuroscience with practical signal processing systems, especially in biomedical applications. He directs the BIASlab research team at TU/e, which develops probabilistic programming tools including RxInfer.jl, ForneyLab.jl, GraphPPL.jl, ReactiveMP.jl, and Rocket.jl. His research spans active inference, variational message passing, probabilistic programming, and Bayesian neural networks, with applications ranging from hearing aids to multi-agent systems. Analysis of his recent publications reveals a strong trend toward practical implementations of Bayesian inference frameworks, particularly through Julia-based probabilistic programming tools. His work shows increasing focus on active inference applications, message passing algorithms, and the intersection of Riemannian geometry with probabilistic modeling. The research demonstrates consistent progression from theoretical foundations toward real-world engineering applications, particularly in biomedical signal processing and autonomous systems. Professor de Vries teaches a graduate-level course on Bayesian Machine Learning at TU/e and actively contributes to open-source software development through his GitHub profile (bertdv), with recent activity as recent as August 2025. His research team has developed several influential probabilistic programming libraries that have gained significant attention in the machine learning community. The BIASlab research group continues to advance the state of the art in Bayesian inference methods with applications in hearing technology, robotics, and signal processing.

Christopher Bishopمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۶ مورد دیگر

Christopher Bishop is a Microsoft Technical Fellow and Director of Microsoft Research AI for Science, concurrently serving as Honorary Professor of Computer Science at the University of Edinburgh and Fellow of Darwin College, Cambridge. His distinguished career spans theoretical physics, neural computing, and leadership in AI research. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Founding member of UK AI Council Member of Prime Minister's Council for Science and Technology (2019) Delivered Royal Institution Christmas Lectures (2008) His research focuses on probabilistic models and machine learning, with significant contributions to AI for scientific discovery. Bishop pioneered the concept of the fifth paradigm of scientific discovery , where AI transforms traditional research methodologies across natural sciences. His work bridges theoretical computer science with practical applications in fusion energy, materials science, and computational biology. Analysis of his recent publications reveals a strategic shift toward AI-driven scientific infrastructure , with emphasis on machine learning foundations that endure technological evolution. His 2024 textbook became Springer Nature's top-selling publication, demonstrating exceptional impact in both academic and industrial contexts. Deep Learning: Foundations and Concepts (2024) Pattern Recognition and Machine Learning (2006) Neural Networks for Pattern Recognition (1995) Bishop leads Microsoft's global AI for Science initiative, establishing research teams in Berlin and coordinating interdisciplinary projects that apply machine learning to climate science, fusion energy, and molecular biology. His leadership in the Prime Minister's Council shapes national AI strategy while maintaining active engagement in public science communication through lectures and media appearances.

• Structural Health Monitoring (SHM)
• Smart / self-sensing cementitious composites
• Bridge engineering and life-cycle assessment
• +۶ مورد دیگر

Filippo Ubertini is Professor of Civil and Environmental Engineering at the University of Perugia, Italy, where he coordinates the International Doctoral Programme in Civil & Environmental Engineering and represents the University inside the FABRE national bridge-research consortium. He leads the Structural Health Monitoring Laboratory ( SHM-Lab ) and is the primary contact for assignments linked to smart-infrastructure research. Education: While explicit degrees are not listed in the supplied text, his role as programme coordinator and full professor implies completion of a PhD and habilitation in Civil Engineering. Research focus: Ubertini’s work sits at the intersection of smart materials and data-driven infrastructure management . He develops self-sensing cementitious composites doped with carbon micro-fibers or graphene nano-platelets that can measure strain, cracking and moisture in real time, turning whole bridges and buildings into distributed sensors. Complementary research threads include low-cost acquisition electronics, UAV & InSAR remote sensing, Bayesian & adversarial machine-learning algorithms for damage detection, digital twins and life-cycle cost analysis of bridge networks. Publication trends (2024-2025): Roughly 30 peer-reviewed items per year concentrate on (i) AI-enhanced operational modal analysis and transfer-learning damage classification across bridge populations, (ii) experimental characterisation of 3D-printed and cast self-sensing concrete, (iii) full-scale validation on curved box-girder, masonry and railway bridges, and (iv) integration of satellite radar data with numerical collapse simulations to predict residual service life of landslide-affected viaducts. Scientific awards & recognition: No specific prizes or fellowships are mentioned in the provided text. Doctoral supervision & grants: The text does not enumerate individual students or funded projects; however, his coordination of an international PhD programme and numerous experimental campaigns imply sizeable supervisory and funding responsibilities. Laboratory & team: Ubertini heads the SHM-Lab at UniPg, maintaining facilities for material mixing, 3D concrete printing, electrical impedance tomography, UAV photogrammetry, and large-scale structural testing, while collaborating with the European FABRE consortium and multiple EU projects.

Joseph Lewisمشاهده پروفایل

پژوهشگر ارشد

• Computational Archaeology
• Geographic Data Science
• Spatial Modelling
• +۴ مورد دیگر

Joseph Lewis is a Research Associate at the McDonald Institute for Archaeological Research within the Department of Archaeology at the University of Cambridge. He completed his PhD in Archaeology at Cambridge (2020-2024) following an MSc in Geographical Information Science with Distinction from the University of Leicester and a BSc in Applied Geology from the University of Plymouth. Dr. Lewis specializes in Computational Archaeology, with research interests spanning Geographic Data Science, Spatial Modeling, Spatial Uncertainty, and Bayesian Data Analysis. His work focuses on developing quantitative methods for analyzing archaeological landscapes through spatial statistics and computational approaches to understanding ancient movement and connectivity across Britain, Europe, and the Mediterranean. His publication record demonstrates expertise in Roman road networks, least cost path analysis, and uncertainty quantification in archaeological modeling. Lewis has developed innovative methodological approaches that combine spatial statistics with archaeological theory to better understand past human movement patterns and landscape use, with particular focus on Roman Britain and Mediterranean connectivity. Scientific Awards: Open-Oxford-Cambridge AHRC DTP - Judy and Nigel Weiss Studentships at Robinson College Match-funded Studentship Vice-Chancellor's & King's College Scholarship (now Honorary) CASA Prize for best paper on Spatial Analysis - GISRUK 2018 2nd Prize GISCRG Dissertation Prize 2017 Royal Institution of Chartered Surveyors Prize 2017 - Best Dissertation 2016 Lewis is the author and creator of the R package 'leastcostpath,' which provides tools for calculating Least Cost Paths and networks while incorporating barriers and uncertainty through probabilistic modeling. His technical proficiency spans R, Python, QGIS, ArcGIS, and SQL, enabling sophisticated spatial analyses of archaeological data across multiple periods and geographical regions.

David Langeمشاهده پروفایل

دانشیار

• Fire Safety Engineering
• Structural Engineering
• Risk Analysis
• +۳ مورد دیگر

Associate Professor David Lange serves as Deputy Director of Higher Degree by Research at the School of Civil Engineering , University of Queensland . With academic qualifications from the University of Edinburgh (PhD and MSc), he specializes in fire safety engineering and structural engineering , focusing on timber buildings, lithium-ion battery fire safety, and risk analysis techniques. Chartered Engineer (CPEng), Fellow of Engineers Australia (FIEAust) Registered Professional Engineer of Queensland (RPEQ) APEC Engineer and International Professional Engineer (IntPE) His research explores fire behavior in mass timber structures , vertical fire spread , and infrastructure resilience . Publications highlight his expertise in probabilistic modeling, fire dynamics, and structural response to extreme conditions. Recent collaborative studies examine flame spread in ventilated façades, compartment fire behavior, and risk evaluation frameworks. He actively supervises PhD projects in areas like fire safety of low-carbon concrete, diagrid structures, and physics-informed machine learning for fire modeling.

Kyle T Spikesمشاهده پروفایل

دانشیار

• Seismic Reservoir Characterization
• Rock Physics Modeling
• Geophysical Inversion Techniques
• +۴ مورد دیگر

Kyle T Spikes is an Associate Professor in the Department of Earth and Planetary Sciences at the Jackson School of Geosciences, The University of Texas at Austin. His research focuses on integrating geologic data with quantitative tools for seismic reservoir characterization, emphasizing forward and inverse problems in rock physics, stochastic modeling, and seismic inversion. He works across scales from sub-micron rock samples to surface seismic data, developing effective medium models and numerical techniques to estimate heterogeneous and anisotropic rock properties. His work spans applications in carbonates, shales, and fractured reservoirs, with a strong emphasis on Bayesian methods, stochastic inversion, and machine learning for data analysis. Recent studies include fluid flow effects in porous media, distributed acoustic sensing (DAS), and rock physics modeling of unconventional reservoirs like the Haynesville Shale. He has contributed to CO2 sequestration monitoring through inversion of 3D VSP data at the Cranfield site. Notable trends in his publications include advancements in Bayesian-based rock physics modeling, integration of multi-scale data (laboratory to field scale), and innovative applications of DAS technology for seismic monitoring. His research bridges geophysics, petrophysics, and reservoir engineering, addressing challenges in reservoir characterization and monitoring under varying fluid and stress conditions. Dr. Spikes advises postdoctoral researchers and graduate students in the Jackson School, though specific advisee names are not listed. His work is supported by collaborative projects with industry and academic partners, focusing on practical solutions for subsurface reservoir challenges.

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Pierre Lermusiauxمشاهده پروفایل

استاد

• Ocean Data Assimilation
• Numerical Ocean Modeling
• Uncertainty Quantification
• +۷ مورد دیگر

Pierre Lermusiaux is the Nam Pyo Suh Professor of Mechanical Engineering and Associate Department Head for Operations Research at MIT. His research focuses on numerical ocean modeling, data assimilation, uncertainty quantification, and applications to ocean dynamics and engineering. He holds a B/M.Eng. from University of Liège, M.Sc. and Ph.D. from Harvard University. Recognized with awards like the MIT Doherty Chair and Spira Teaching Award, his work bridges computational science and marine systems. Education: B/M.Eng., University of Liège, 1992 M.Sc., Harvard University, 1993 Ph.D., Harvard University, 1997 Research Interests: Combines oceanographic modeling with advanced computational methods. Specializes in stochastic ocean prediction, autonomous systems optimization, and interdisciplinary applications of uncertainty quantification. His work enables real-time environmental forecasting and marine robotics navigation strategies. Publications: Focus on developing computational frameworks for probabilistic ocean forecasts, acoustic propagation modeling, and machine learning-based environmental prediction systems. Recent work emphasizes adaptive sampling strategies and energy-efficient path planning for underwater vehicles. Awards: Fulbright Foundation Fellowship (1993-96) Ruth and Joel Spira Award for Teaching (2010) MIT Doherty Chair in Ocean Utilization (2009-2011) Advising & Grants: Leads MIT’s MSEAS group and has secured funding from NSF, ONR, and international collaborations. Advises projects on ocean robotics, climate modeling, and underwater acoustics. Key contributions include the MIT Red Sea Modeling System and real-time forecasting platforms. Labs/Teams: Directs MIT’s multidisciplinary ocean modeling efforts, integrating mechanical engineering, computer science, and environmental science. Co-leads initiatives like the Ocean Predictions with Ensembles (OPEN) project and the METEOR autonomous observatory system.

Tejaswini Herathمشاهده پروفایل

استاد

• Information Security Governance
• Mobile Payment Technologies
• Cybersecurity
• +۳ مورد دیگر

Tejaswini Herath is a Professor of Information Systems at the Goodman School of Business, Brock University. Her research focuses on information security governance, mobile payment technologies, and the ethical implications of technology adoption. She has contributed extensively to understanding cybersecurity challenges, digital privacy, and the socio-economic impacts of emerging technologies. Her work often bridges technical and organizational aspects, emphasizing frameworks for risk management and innovation adoption. Recent studies include analyses of mobile payment systems in developing economies, cryptocurrency valuation ethics, and pandemic-era technology governance lessons. Her research spans interdisciplinary topics such as phishing susceptibility, user behavior in digital environments, and the integration of cybersecurity into organizational strategies. She has published over 50 peer-reviewed articles, with a focus on practical applications of theoretical models like Bayesian post-audit frameworks and technology-organization-environment (TOE) models. Her work addresses both academic and industry audiences, advocating for balanced scorecard approaches to security performance measurement. Tejaswini Herath is actively involved in curriculum development for information systems education, emphasizing student motivation and learning outcomes in IS courses. She collaborates with global institutions to study cross-cultural technology adoption patterns and policy implications.

Emmanuel Mamatzakisمشاهده پروفایل

استاد

• International Macroeconomics
• Accounting
• Banking
• +۳ مورد دیگر

Emmanuel Mamatzakis is a Professor of Finance at Birkbeck Business School, University of London. He holds a DPhil in Economics from University of London (Queen Mary College), MSc from University of Warwick, and BSc from National and Kapodistrian University of Athens. His research focuses on empirical studies in accounting, banking, finance, and public finance, emphasizing real-world economic impact. Key roles include Director of Accounting and Finance Research Centre and Program Director for MSc Accounting and Finance and MSc Management and Finance. Education: PhD, University of London, 1999 MSc, University of Warwick, 1994 BSc, University of Athens, 1993 Research Interests: International Macroeconomics, Accounting, Banking, Finance, Applied Econometrics, Public Finance. Recent work examines household debt repayment behavior during the pandemic (ESRC-funded project), fiscal policies, and environmental impacts on financial reporting. His studies often combine quantitative methods like Bayesian models and neural networks. Grants & Awards: £102,974 ESRC grant for pandemic-era debt research (2020–2022). Honored as Fellow of the Higher Education Academy (2021). Advising & Grants: Supervises PhD students in banking, international macroeconomics, and applied econometrics. Leads the Accounting and Finance Research Centre. Collaborates with institutions like ESMA and the Hellenic Fiscal Council. Labs/Teams: Principal Investigator at Accounting and Finance Research Centre, leading projects on fiscal policy and pandemic economics. Active in research clusters like the UK ESRC Rapid Response initiative.

Jason Swansonمشاهده پروفایل

دانشیار

• Stochastic Differential Equations
• Probability Theory
• Mathematical Logic
• +۲ مورد دیگر

Jason Swanson is an Associate Professor in the Department of Mathematics at the University of Central Florida, specializing in stochastic processes and probability theory. His research encompasses stochastic differential equations, fractional Brownian motion, and the foundations of probability. Recent work includes developing the iterated Dirichlet process for Bayesian inference and extending representations for row-exchangeable arrays. He maintains an active research program connecting mathematical logic with probability theory. Teaching responsibilities include graduate courses in Measure and Probability (MAA 6238) and undergraduate probability (MAP 4113). His lecture notes on measure-theoretic probability are publicly available.

Cory Simonمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Optimization
• Computational Materials Science
• +۳ مورد دیگر

Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.

Aleksandar Dimitrovskiمشاهده پروفایل

دانشیار

• Power Systems
• Microgrid Protection and Control
• Parallel Simulation of Power Systems
• +۵ مورد دیگر

Dr. Aleksandar Dimitrovski is an Associate Professor at the University of Central Florida (UCF) in the Department of Electrical and Computer Engineering, College of Engineering and Computer Science. Holding a Ph.D. in Power Engineering from Saints Cyril and Methodius University (1997), his research focuses on power system modeling under uncertainty, microgrid protection, parallel simulation techniques, and renewable energy integration. Ph.D., Power Engineering, Saints Cyril and Methodius University (1997) Active research areas: Power System Protection, Parallel Simulation, Solar Integration, Magnetic Amplifiers His publications emphasize advanced simulation algorithms like Parareal, probabilistic forecasting for solar power, and magnetic reactor applications in grid control. He has led DOE-funded projects on solar integration and real-time grid protection. Fulbright Scholar (2016) R&D 100 Award (2014) Prof. Dimitrovski serves as an IEEE Senior Member, CIGRE member, and reviewer for journals like IEEE Transactions on Power Systems and Smart Grid. He developed the open-source RAPID simulation toolbox and contributed to cyber-physical security frameworks for smart grids.