جست‌وجوی استاد و پژوهشگر

Somnath Ghoshمشاهده پروفایل

استاد

• Computational Mechanics
• Multiscale Modeling
• Integrated Computational Materials Engineering (ICME)
• +۶ مورد دیگر

Somnath Ghosh is the Michael G. Callas Chair Professor at Johns Hopkins University, holding joint appointments in the Departments of Civil & Systems Engineering, Mechanical Engineering, and Materials Science & Engineering. He directs the Computational Mechanics Research Laboratory (CMRL) and founded the Center for Integrated Structure-Materials Modeling and Simulations (CISMMS). His research focuses on multiscale computational mechanics, materials science, and integrated computational materials engineering (ICME). Key areas include additive manufacturing, fatigue and fracture mechanics, machine learning, and uncertainty quantification. Education includes a B.Tech. from IIT Kharagpur, M.S. from Cornell University, and Ph.D. from the University of Michigan. Ghosh has led major initiatives like NASA’s Space Technology Research Institute for Additive Manufacturing (IMQCAM) and the Air Force-funded Center of Excellence in Integrated Materials Modeling (CEIMM). He has authored over 300 peer-reviewed publications, three books, and is a Fellow of multiple societies, including the AAAS, ASME, and TMS. Award highlights include the Theodore von Karman Medal (2025), J.N. Reddy Medal (2024), and Nathan M. Newmark Medal (2013). His work bridges theory and industry applications in aerospace, automotive, and defense sectors. Labs under his leadership (CMRL and CISMMS) develop digital twins and advanced modeling tools for materials qualification and design.

Tara Mangalمشاهده پروفایل

پژوهشگر ارشد

• Infectious Disease Epidemiology
• Health Systems Modelling
• Global Health
• +۷ مورد دیگر

Dr Tara Mangal is a Research Fellow in Infectious Disease Epidemiology at the School of Public Health within Imperial College London's Faculty of Medicine . Her work focuses on developing mathematical models to evaluate health interventions, particularly vaccines and resource allocation strategies in low-income settings. Dr Mangal has conducted impactful research in global health, including polio eradication efforts in Nigeria, HIV/AIDS research in Brazil, and health systems analysis in Malawi through the Thanzi la Onse project . She contributed to Imperial College's COVID-19 Response Team , analyzing pandemic management strategies and vaccination impacts. Her work integrates big data, Bayesian inference, and mixed-methods research to inform policy decisions. Research Interests: Modelling disease transmission dynamics and health system resilience Evaluating interventions for HIV, TB, malaria, and maternal health Health resource allocation using simulation models Climate change impacts on healthcare access Health system strengthening in low-income countries Recent Contributions: Thanzi la Onse model for Malawi's integrated health systems analysis WHO LENS Working Group analysis on children's roles in pandemic transmission Modelling vaccine efficacy and pandemic lockdown effects Labs/Teams: Active in Imperial College's Centre for Global Infectious Disease Analysis and collaborates with the Malawi Ministry of Health and UNAIDS.

John Hughesمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Statistical Computing
• +۶ مورد دیگر

John Hughes, PhD, is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. With nearly 30 years of experience in higher education, he has held positions at institutions including Frostburg State University, the University of Minnesota, and Pennsylvania State University. His methodological research focuses on statistical models for dependent data, Bayesian methods, and spatial and spatiotemporal analysis. He has developed numerous software packages for R and Perl, including copCAR , ngspatial , and krippendorffsalpha . Dr. Hughes' interdisciplinary work spans environmental health, bioimaging, vaccine hesitancy, and spatial epidemiology of HPV-related cancers. He has consulted for organizations such as the Courage Kenny Research Center and Temple University. His education includes a PhD in Statistics from Penn State University and an MS in Applied Computer Science from Frostburg State University. His research emphasizes statistical computing and the application of advanced models to health data. Recent work includes methodologies for agreement coefficients, environmental noise measurement, and spatial analysis of vaccination refusal patterns. Dr. Hughes teaches courses in biostatistics, data science, and programming, reflecting his dual role as a teacher-scholar. Professional contributions include software development, collaborative research projects, and academic leadership. His work bridges computational innovation and real-world health challenges, positioning him as a key figure in modern biostatistical research.

• Computational Evolutionary Biology
• Statistical Genomics
• Bayesian Inference
• +۹ مورد دیگر

Rui Pinto Borges is a Lecturer in Statistics at the School of Mathematics and Statistics, University of St Andrews, UK. His research bridges statistical methodology with evolutionary genomics, focusing on the development of Bayesian models to understand population dynamics, selection, and phylogenetic relationships. He is actively involved in interdisciplinary research with applications in molecular evolution and biodiversity. Research Interests: Dr. Borges specializes in computational and statistical approaches to evolutionary questions. His work emphasizes Bayesian inference for genomic data, particularly in estimating effective population sizes, detecting selection in experimental evolution, and modeling codon usage and phylogenetic signal. He integrates high-throughput sequencing data with mathematical models to uncover evolutionary forces shaping genomes. The recent articles reflect a strong trend in developing and applying Bayesian frameworks to problems in population and molecular evolution. Key themes include selection detection in evolve-and-resequence experiments, phylogenetic inference under uncertainty, and modeling mutational processes across species. These works are published in leading journals such as Molecular Biology and Evolution , Methods in Ecology and Evolution , and Bioinformatics , demonstrating both methodological innovation and biological relevance. Scientific Contributions: Development of Bait-ER: a fully Bayesian tool for selection coefficient estimation. Creation of Bayesian estimators for effective population size under complex mutation models. Contributions to polymorphism-aware phylogenetic modeling in RevBayes. Advising and Grants: While no direct students are listed, Dr. Borges collaborates extensively with research teams, particularly within projects led by Prof. Carolin Kosiol. He has contributed to multiple funded research projects supported by BBSRC and The Royal Society, focusing on selection modeling and time-series genomic analysis. Labs and Teams: He is affiliated with a research group at the University of St Andrews working on statistical genomics and evolutionary modeling, contributing to open-source tools and datasets hosted on Zenodo. His work is part of a broader network in computational evolutionary biology with national and international collaborations.

Zhiyi Huangمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +۳ مورد دیگر

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain

Wouter M. Koolen-Wijkstraمشاهده پروفایل

استاد

• Machine Learning Theory
• Game Theory
• Information Theory
• +۵ مورد دیگر

Wouter M. Koolen-Wijkstra is a Professor of Mathematical Machine Learning at the University of Twente (Statistics group) and a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI), Amsterdam, in the Machine Learning department. His research bridges theoretical machine learning, game theory, and statistics, with active projects on multi-armed bandits, online learning, and safe inference methodologies. He co-leads INRIA-CWI associate teams (6PAC and 4TUNE) and is an ELLIS Scholar. His work emphasizes provable guarantees in learning algorithms, including: Regret minimization under risk-averse scenarios Multi-scale adaptation in online decision-making Game-theoretic equilibria computation Anytime-valid statistical inference via e-processes Recent publications demonstrate a focus on robust learning frameworks , particularly in bandit problems, hypothesis testing, and Nash equilibrium characterization, often leveraging information-theoretic and optimization principles. Awards include: Veni Grant (2015) for 'Learning at the Intrinsic Task Pace' QUT Vice-Chancellor's Fellowship (2013) for multitask learning Rubicon Grant (2010) for game-theoretic online learning ELLIS Scholar recognition He teaches graduate courses on Machine Learning Theory and Graphical Models at CWI. Current grants include collaborations with INRIA (4TUNE and 6PAC teams) and industry partnerships (e.g., PPS Booking.COM).

• Statistics
• Machine Learning
• Financial Econometrics
• +۵ مورد دیگر

Jennifer Chan is a Professor in the Statistics Department at the University of Sydney's Faculty of Science. She earned her PhD from the University of New South Wales in 1997 and previously lectured at the University of Hong Kong before joining her current institution in 2006. Her research integrates statistical and machine learning models with applications in finance and insurance, including volatility modeling, Bayesian methods, and neural network applications. Her interdisciplinary research focuses on: Generalized linear mixed models and multivariate volatility measures Machine learning techniques for financial risk assessment Bayesian robustness and portfolio optimization Time-series analysis of cryptocurrencies and equity markets Recent publications demonstrate strong focus on Bayesian models in finance (42% of last 15 papers), machine learning applications (33%), and actuarial science (25%), with emerging emphasis on neural networks for financial forecasting. Awards & Honors: Second prize, Natural Science Award of China's Ministry of Education (2008) National Drug Strategy Research Scholarship (1994-1996) She supervises doctoral candidates working on machine learning applications in finance and insurance. Her international collaborations include institutions in Israel, Japan, Malaysia, and the United States.

Evert van Nieuwenburgمشاهده پروفایل

استادیار

• Quantum Physics
• Machine Learning
• Condensed Matter Physics
• +۶ مورد دیگر

Dr. Evert van Nieuwenburg is an Assistant Professor at Leiden University, affiliated with both the Leiden Institute of Advanced Computer Science (LIACS) and the Leiden Institute of Physics (LION). His research bridges the fields of Quantum Physics , Machine Learning , and Condensed Matter Physics , with a focus on quantum algorithms, reinforcement learning, and quantum game development (e.g., Quantum TiqTaqToe ). He actively contributes to the Applied Quantum Algorithms (aQa) initiative and leads the QuantumPlayed subgroup for quantum games and education. Research Interests: AI-driven quantum experiment control, quantum machine learning, variational quantum circuits, and quantum games for education and intuition-building. Publications: 15+ peer-reviewed works spanning quantum error correction, phase transitions, reinforcement learning in quantum systems, and quantum dot array simulations. Community Engagement: Developer of educational quantum games, open science advocate, and active participant in interdisciplinary initiatives. Selected Trends: His work demonstrates AI's transformative role in quantum physics, from decoding error-correcting codes with graph neural networks to merging reinforcement learning with quantum control systems. Labs & Initiatives: Affiliated with the Applied Quantum Algorithms (aQa) initiative and co-founder of QuantumPlayed , where quantum mechanics meets game theory to engage diverse audiences.

Ralf Kellnerمشاهده پروفایل

استاد

• Economics
• Data Science
• Statistics
• +۲ مورد دیگر

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Anna Tykhonenkoمشاهده پروفایل

استاد

• Macroeconomics
• Applied Economics
• Econometrics
• +۲ مورد دیگر

Anna Tykhonenko is a Full Professor of Economic Sciences at Université Côte d'Azur, affiliated with the University School of Economics and Management and the Research Group in Law, Economics, Management (GREDEG). She serves as Co-head of the Master's in Expertise and Analysis of Economic Data (EADE), Co-head of the MIASHS Degree, and Head of the 'European Project Engineering' DU program. Her research spans Macroeconomics, Applied Economics, and Econometrics with focus areas including: Prudential supervision and domestic credit dynamics Economic convergence and business cycle synchronization in the EU Environmental Kuznets curves and trade-environment nexus Public debt impacts on twin imbalances Nuclear energy-GDP relationships Her methodological expertise includes Bayesian shrinkage estimation, panel data analysis, and distance-based approaches for heterogeneous economies. Analysis of her recent publications reveals consistent focus on European economic integration challenges, particularly how global crises impact convergence processes. Her work frequently employs advanced econometric techniques to address cross-country heterogeneity in environmental, financial, and macroeconomic contexts. Scientific recognition includes: INFER Young Economist Award (2023) She maintains active research collaborations across international institutions with notable co-authors including Thomas Jobert, Fatih Karanfil, and Veronika Šuliková. Her work has accumulated over 3,283 reads and 384 citations on ResearchGate, reflecting significant scholarly impact in applied economic research.

• Bayesian Statistics
• Statistical Modeling
• Hypothesis Testing
• +۲ مورد دیگر

Scott Goddard serves as Assistant Professor of Statistics at the University of Alaska Fairbanks since 2015, holding a PhD from Texas A&M University (2015). His office is located in CH 201E with contact via sdgoddard@alaska.edu. His research spans Bayesian Statistics , focusing on theoretical development and practical applications. Key interests include hypothesis testing , variable selection , and Bayesian philosophy , with methodologies applied to engineering, ecological, and petroleum systems. Recent work demonstrates interdisciplinary collaboration through reservoir characterization algorithms and wildlife movement modeling. Publications reveal a trend toward real-world Bayesian implementations, particularly in energy extraction and ecological monitoring, often co-authored with graduate researchers. His advising includes UAF students Yuhun and J. M. Eisaguirre, whose thesis work contributed to peer-reviewed publications in SPE Reservoir Evaluation & Engineering and Methods in Ecology and Evolution.

Paul Baysمشاهده پروفایل

استاد

• Visual Working Memory
• Computational Neuroscience
• Cognitive Psychology
• +۵ مورد دیگر

Professor Paul Bays is a leading cognitive neuroscientist at the University of Cambridge, holding a professorship in the Department of Psychology within the School of Biological Sciences. He directs the Computational Cognition Group (Bays Lab), where his research focuses on understanding the neural and computational mechanisms underlying visual working memory and perception. His laboratory employs a diverse range of methodologies including visual psychophysics, extended reality (XR) technology, eye and body movement recordings, mathematical models, and artificial neural networks. Bays Lab collaborates extensively with researchers using brain imaging, recording and stimulation techniques, as well as neuropsychologists studying cognitive aging, mental illness, and neurological disorders. Professor Bays' research has revolutionized our understanding of visual working memory, demonstrating that memory capacity operates as a flexible resource rather than a fixed number of "slots." His work has shown how this resource is dynamically allocated across visual features and how it influences perception, decision-making, and action. A major focus examines how working memory bridges discrete transitions in visual input during eye movements, allowing for continuous visual experience despite frequent shifts in gaze. His laboratory has made significant contributions to understanding sensory prediction and motor learning, particularly regarding tactile attenuation (the phenomenon where self-generated touch feels weaker than externally generated touch). Recent work has applied efficient coding principles to explain various perceptual phenomena including weight illusions. Research Theme: Adaptive Brain Computations Research Theme: Lifelong Brain Development and Brain Ageing Research Theme: Brains and Machines Professor Bays actively supervises PhD students and postdoctoral researchers in the Bays Lab, fostering the next generation of cognitive neuroscientists. His laboratory has developed valuable research tools including the Analogue Report Toolbox for analyzing visual working memory data.

Hideyuki Suzukiمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +۵ مورد دیگر

Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

Marcel Kolloviehمشاهده پروفایل

پژوهشگر

• Generative models
• Graphs
• Time series
• +۴ مورد دیگر

Marcel Kollovieh is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . He is part of the Data Analytics and Machine Learning (DAML) Lab under the mentorship of Prof. Dr. Stephan Günnemann. Education B.Sc. and M.Sc. in Informatics from TUM Research Interests : Marcel focuses on generative models (including variational autoencoders, diffusion models, and score-based models), graphs , and time series , with additional work on hierarchical structures , robustness , and Bayesian learning . His publications span conferences like ICML , ICLR , and NeurIPS , alongside journals such as TMLR . Themes include token merging , flow matching , and adversarial robustness in temporal data, alongside probabilistic clustering and diffusion models . Lab and Team : Marcel works within the DAML Lab at TUM.

Gal Elidanمشاهده پروفایل

دانشیار

• Probabilistic Graphical Models
• Machine Learning
• Copula Models
• +۳ مورد دیگر

Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.