جست‌وجوی استاد و پژوهشگر

• Spatial Statistics
• Software Development
• Open Source Geospatial Software
• +۱ مورد دیگر

Barry Rowlingson is a Research Fellow at Lancaster University Medical School , affiliated with the Chicas Research Group and DSI-Health . He specializes in spatial statistics applied to disease epidemiology and geospatial software development . Teaches Geospatial Data module for MRes in Global Health Developed online course on Spatial Statistics in R with DataCamp Focuses on open-source geospatial tools for public health Research Interests: His work bridges spatial statistical methodology and infectious disease modeling , with applications in antimicrobial resistance mapping , wastewater-based pandemic surveillance , and health inequality analysis . Recent projects include modeling ESBL-producing bacteria in Malawi and developing spatio-temporal frameworks for COVID-19 wastewater monitoring . Scientific Contributions: Over 15 years, he has developed critical R packages like stpp for spatio-temporal analysis and rgdal for geospatial data abstraction. His 2023 publications address health workforce disparities and disease transmission dynamics using advanced statistical methods. Collaborations: Works with multidisciplinary teams across Lancaster Medical School , DataCamp , and SAVSNet Agile research projects. Supervises PhD student Charlotte Appleton in biostatistics.

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.

Feras Saadمشاهده پروفایل

استادیار

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +۴ مورد دیگر

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

• Econometrics
• Macroeconomics
• Panel Data Analysis
• +۴ مورد دیگر

Professor M. Hashem Pesaran is a leading academic in Econometrics and Macroeconomics at the University of Cambridge's Faculty of Economics. His research focuses on dynamic panel data models, asset pricing, climate change impacts, and spatial econometrics. He has contributed extensively to methodologies addressing cross-sectional dependence, factor models, and policy analysis. Notable works include advancements in testing for alpha in asset pricing, analyzing pandemic transmission via stochastic networks, and assessing climate change's macroeconomic effects. His empirical studies utilize large datasets and advanced econometric techniques, often with real-world policy implications. Key research interests include: Econometric theory (panel data, factor models) Financial economics (asset pricing, risk premia) Macroeconomic policy (climate change, fiscal impacts) Spatial and network analysis (dominant units, SIR models) Recent articles highlight his work on heterogeneous dynamic panels, climate effects on US states, and pandemic modeling. No explicit awards are listed, but his publications indicate peer recognition. Advising and grants are not detailed here, but his collaborative projects suggest extensive academic leadership.

• Computational Statistics
• Probabilistic Machine Learning
• Markov Chain Monte Carlo
• +۷ مورد دیگر

Professor Christopher Nemeth of Lancaster University's School Of Mathematical Sciences is a leading researcher in computational statistics and probabilistic machine learning. His work focuses on Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), Gaussian processes, and approximate Bayesian computation, with applications in environmental science, target tracking, and econometrics. He currently holds a UKRI Turing AI Acceleration Fellowship and leads the ProbAI research hub. Research Interests: Development of probabilistic AI algorithms for large-scale learning, state-space modeling, and intersections between sampling and optimization algorithms. Grants: £9M UKRI-EPSRC ProbAI hub (2024-2029), £1.1M Turing AI Acceleration Fellowship (2021-2026), and multiple NERC grants. Academic Roles: Turing University Academic Liaison (2023-present), Associate Editor for ACM Transactions on Probabilistic Machine Learning (2023-present), and leadership roles in the Royal Statistical Society. Supervision: Completed supervision of 7 PhD students with projects on scalable Gaussian processes, Monte Carlo methods, and network modeling.

• Computational Statistics
• Machine Learning
• Generative Modelling
• +۷ مورد دیگر

O. Deniz Akyildiz is an Assistant Professor in Statistics at the Department of Mathematics, Imperial College London. His research focuses on computational statistics, machine learning, and generative modelling, with applications to sampling, optimization, and inverse problems. He holds affiliations with the Artificial Intelligence Network and Mathematics research groups. Previously, he obtained degrees in Electronics and Communications Engineering from İTÜ, followed by a PhD in Signal Processing at Universidad Carlos III de Madrid. Before joining Imperial, he worked as a postdoctoral researcher at Warwick CS and The Alan Turing Institute. His research interests span diffusion-based parameter estimation, score-based generative models, Langevin dynamics for optimization, and adaptive importance samplers. Recent work includes contributions to latent diffusion models, Sinkhorn semigroups, and stochastic filtering techniques. Notable publications include works on statistical finite elements, interacting particle Langevin algorithms, and physics-informed deep generative models. His technical blog almost stochastic and GitHub repository provide further insights into his research.

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Inference
• Sampling Methods
• +۴ مورد دیگر

Anna Korba is an Assistant Professor at CREST-ENSAE Paris within the Statistics Department. She holds an ENSAE Engineering degree in Data Science and a Master's in Mathematics, Vision & Learning (MVA) from ENSAE Paris. Her career includes a Ph.D. in Machine Learning at Télécom ParisTech, followed by a postdoctoral position at UCL's Gatsby Unit. Her research focuses on sampling techniques, Bayesian inference, optimal transport, and generative modeling, with recent work on constrained sampling and fairness integration. She contributes to collaborative efforts at the intersection of machine learning, dynamical systems, and PDEs. Notably, she co-presented tutorials on Wasserstein gradient flows at ICML 2022. Her work addresses unsolved challenges in sampling efficiency and fairness constraints. She is actively involved in CREST research initiatives and academic mentorship.

Joonha Parkمشاهده پروفایل

استادیار

• Computational Statistics
• Sequential Monte Carlo Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Joonha Park is an Assistant Professor in the Department of Mathematics at the University of Kansas (since 2020), affiliated with the College of Liberal Arts & Sciences. His research focuses on computational statistics, including sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods, with applications to public health, Bayesian analysis, and spatio-temporal systems. He holds a Ph.D. in Statistics from the University of Michigan (2018), an M.A. from KAIST (2012), and a B.S. from MIT (2007). Education: Ph.D. in Statistics, University of Michigan, 2018 M.A. in Mathematical Sciences, KAIST, 2012 B.S. in Mathematics, MIT, 2007 Research Interests: Computational methods for statistical inference, spatiotemporal modeling, Bayesian statistics, and applications to public health. His work emphasizes developing scalable algorithms for implicitly defined models and high-dimensional systems. Publications: Recent work includes cross-cultural studies on well-being, computational inference methods (e.g., the sbi R package), and epidemiological modeling. His research bridges statistical methodology and real-world applications, often involving international collaborations. Labs/Teams: Active in computational statistics research groups, contributing to open-source tools like sbi and athmc . Collaborates with interdisciplinary teams on public health data analysis and spatiotemporal dynamics.

Hanwen Xingمشاهده پروفایل

عضو هیئت علمی

• Computational Statistics
• Bayesian Statistical Modelling
• Health Informatics
• +۳ مورد دیگر

Hanwen Xing is a Research Associate at St Peter's College and a Postdoctoral Researcher in Artificial Intelligence at the Nuffield Department of Women's & Reproductive Health, University of Oxford. He holds a DPhil in Statistics (2022) and MSc in Statistical Science (2018) from Oxford, following a Bachelor of Mathematics from the University of Waterloo (2017). Affiliations: University of Oxford, St Peter's College Roles: Academic support for MSc/DPhil students, Bayesian methodology development His research focuses on computational statistics and Bayesian modelling, particularly applying approximate Bayesian inference methods to healthcare and medical science challenges. He has developed novel Bayesian approaches for integrating drug response and protein profiling data to identify tumor-specific cancer dependencies, demonstrated through projects like DepInfeR-GP. His work bridges statistical theory with practical applications in precision oncology. Key research outputs include advancements in Gaussian process modelling for single-cell perturbation data, continual learning frameworks using probabilistic methods, and improved bridge estimators via f-GAN techniques. These contributions highlight his expertise in both foundational statistical theory and applied computational methods. No scientific awards explicitly mentioned. He advises students in statistics programs and contributes to collaborative projects involving ex-vivo drug sensitivity analysis. His GitHub repository hosts implementations of his Bayesian models, reflecting a commitment to open-source scientific software development.

Cristian Rojasمشاهده پروفایل

استاد

• Automatic Control
• System Identification
• Control Theory
• +۷ مورد دیگر

Cristian Rojas is a Professor of Automatic Control at KTH Royal Institute of Technology, specializing in system identification. His research bridges control theory, statistics, and machine learning to develop data-driven methods for analyzing and controlling dynamical systems. He holds an MS in Electronics Engineering from Universidad Técnica Federico Santa María (Chile) and a PhD in Electrical Engineering from the University of Newcastle (Australia). Research focuses on efficient utilization of data for self-learning systems, including topics like continuous-time system identification, robust control, and statistical estimation. Notable contributions include work on subspace identification, input design for sparse systems, and algorithms for H-infinity norm estimation. His methodologies emphasize practical applications in industrial automation, smart infrastructure, and autonomous systems. Recent publications highlight advancements in inverse filtering, decentralized learning systems, and the theoretical underpinnings of data-driven control. He collaborates widely on projects involving Bayesian methods, adversarial systems, and privacy-protected decision-making frameworks. Rojas' work often addresses challenges such as undersampling effects, model consistency, and computational efficiency in real-world control scenarios. His academic contributions include organizing academic ceremonies at KTH and mentoring researchers in the Department of Automatic Control. Current research explores intersections between machine learning interpretability and control theory, with applications to explainable AI in engineering systems.

Piotr Gmytrasiewiczمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Multiagent Systems
• Decision Theory
• +۷ مورد دیگر

Piotr Gmytrasiewicz is an Associate Professor at the Department of Computer Science, University of Illinois at Chicago (UIC). He leads the Multiagent Systems Group within the Artificial Intelligence Laboratory at UIC. His research focuses on rationality in artificial agents , particularly in environments with multiple interacting agents. Key areas include Interactive Decision-Making Bayesian Modeling for Agent Communication Recursive Belief Frameworks Time Pressure and Computational Trade-offs Evolution of Agent Communication Languages Dynamic Resource Allocation His recent work examines the rationality of insincere communication and methods to discount potentially insincere information. Past projects include modeling emotions in agent design and developing decision-theoretic approaches to game theory. He has secured funding from prestigious institutions such as the National Science Foundation (NSF) , Office of Naval Research (ONR) , and DARPA . Current and past projects emphasize Automated Linguistic Competence Evolution Emergent Communication Protocols Scalable Multiagent Learning Strategic Coordination under Uncertainty He earned his Ph.D. from the University of Michigan, Ann Arbor (1992) and has previously collaborated with the Department of Computer Science and Engineering (CSE) at the University of Texas at Arlington on DARPA-funded research.

• Stochastic Processes
• Financial Mathematics
• Statistics
• +۳ مورد دیگر

Alexandra Chronopoulou is a Clinical Associate Professor in the Department of Statistics at the University of Illinois. She is affiliated with the Coordinated Science Lab and the Center for Social & Behavioral Science, contributing to interdisciplinary research in both mathematical theory and healthcare applications. Her roles emphasize bridging statistical methodologies with practical challenges in diverse fields. Her research interests span stochastic processes, including fractional Brownian motion and volatility modeling, alongside healthcare systems analysis and medical diagnostics. She applies statistical techniques to study burnout among healthcare professionals, particularly in pandemic testing environments, and explores biomechanical factors in gastrointestinal disorders. Her work also intersects with finance, focusing on risk management and option pricing through advanced stochastic frameworks. In recent years, her articles highlight trends in volatility estimation, fractional processes simulation, and healthcare ergonomics. Earlier contributions include methodological advances in sequential Monte Carlo, community detection algorithms, and multiscale diffusions. No scientific awards were explicitly mentioned in the provided texts. Though no advising or grant information was listed, her research collaborations extend to international partnerships. She contributes to labs and centers that foster interdisciplinary innovation, including work at the Coordinated Science Lab and the Center for Social & Behavioral Science.

Eric R. Fossumمشاهده پروفایل

استاد

• Solid-State Image Sensors
• CMOS Active Pixel Sensors
• Quanta Image Sensors
• +۴ مورد دیگر

Eric R. Fossum is the John H. Krehbiel Sr. Professor for Emerging Technologies at the Thayer School of Engineering at Dartmouth College. He serves as Vice Provost for Entrepreneurship and Technology Transfer and Director of Dartmouth's PhD Innovation Program. As one of the world's leading experts in solid-state image sensors, he invented the CMOS active pixel sensor technology that revolutionized digital imaging in smartphones, medical devices, and automotive systems. His work has earned him numerous accolades, including the National Medal of Technology and Innovation (2025) and the Queen Elizabeth Prize (2017). His research interests focus on: Solid-state image sensors (CCDs, CMOS active pixel sensors, Quanta Image Sensors) Advanced imaging systems and on-chip processing New applications for image sensors in medicine, security, and space Dr. Fossum's recent publications demonstrate significant advancements in: Photon-counting sensors for low-light applications High-speed imaging for microscopy and radiography Backside-illuminated and sub-diffraction-limit pixel designs Quantum random number generation using sensor technology Infrared spectral extension of CMOS sensors His scientific awards include: National Medal of Technology and Innovation (2025) Queen Elizabeth Prize for Engineering (2017) IEEE Andrew S. Grove Award (2009) Induction into National Inventors Hall of Fame (2011) Emmy Award for Technology & Engineering (2021) Doctor of Science, Honoris Causa from Trinity College (2014) As an entrepreneurial leader, Dr. Fossum has: Co-founded Gigajot Technology with former PhD students Previously led Photobit and Siimpel Corporations Active participant in technology transfer initiatives at Dartmouth Founder and Past President of the International Image Sensor Society

Zhenxia Liuمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Probability Theory
• Large Deviations
• +۳ مورد دیگر

Zhenxia Liu is an Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). Her work contributes to theoretical and applied probability, with a focus on stochastic processes and statistical modeling. Her research interests lie at the intersection of mathematical statistics and probability theory. Key areas include large deviations , longest runs in Markov chains , and Monte Carlo methods . These topics are central to understanding rare events, sequential dependencies, and numerical estimation techniques in complex systems. The recent publications demonstrate a consistent focus on probabilistic analysis of dependent structures, particularly through Markov models. Her work combines theoretical rigor with applications in computational statistics, showing trends toward improving bounds and simulation efficiency in stochastic modeling. Mathematical Statistics Probability Theory Large Deviations Markov Chains Monte Carlo Methods Computational Mathematics Zhenxia Liu has actively contributed to high-quality journals such as Statistics and Probability Letters , Results in Applied Mathematics , and Probability and Mathematical Statistics . While no formal advising or grant information is available in the provided text, her collaborative publications suggest engagement in research networks within applied mathematics. She is part of the research environment in Applied Mathematics at Linköping University, which focuses on computational mathematics, optimization, and mathematical modeling across science and engineering disciplines.

Hendrik Baierمشاهده پروفایل

استادیار

• Artificial Intelligence
• Planning
• Search Algorithms
• +۵ مورد دیگر

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.