جستجوی استاد راهنمای دانشگاهی

Robert Brandon Gramacyمشاهده پروفایل

استاد

• Bayesian modeling
• Statistical computing
• Monte Carlo inference
• +۷ مورد دیگر

Robert Brandon Gramacy is a Professor and Department Head in the Department of Statistics within the College of Science at Virginia Polytechnic and State University (Virginia Tech). He is also affiliate faculty in VT's Computational Modeling and Data Analytics (CMDA) program. Previously, he was an Associate Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business and a fellow of the Computation Institute. Dr. Gramacy specializes in computational statistics, focusing on areas where ideal modeling approaches are impractical or current solutions lack fidelity. His research spans Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. He develops new models, methods, and algorithms while remaining grounded in real-world applications across diverse fields including aerospace engineering, climate science, epidemiology, finance, and environmental management. His work on NASA rocket booster design led to the development of the tgp R package, which has found wide applicability across numerous scientific domains. His recent research focuses on large-scale surrogate modeling, particularly with local approximate Gaussian processes (laGP) and heteroskedastic Gaussian processes (hetGP). He has made significant contributions to predicting atmospheric drag of satellites in orbit and is currently leading an NSF-funded project to forecast phytoplankton blooms for drinking water quality management. His publications demonstrate a consistent focus on scalable methods for complex computer experiments and real-world applications. Savage Award for best Ph.D. thesis in Bayesian applied methodology (2006) Facebook faculty award for research on large scale surrogate modeling (2017) Outstanding Service Award from the ASA Section on Physical and Engineering Sciences (2022) Editor-in-Chief of Technometrics (since Jan 1, 2023) Lloyd S. Nelson Award and Youden Award for significant contributions to applied statistics Dr. Gramacy mentors numerous graduate students and postdocs, emphasizing reproducible research, high-quality software development, and clear scientific communication. His lab members work on cutting-edge problems in statistical methodology and applications across multiple domains. As Editor-in-Chief of Technometrics, he plays a key role in shaping publication standards for applied statistics research and has published guidance for authors on writing effective statistical papers.

Zequn Wangمشاهده پروفایل

دانشیار مدعو

• Uncertainty Quantification
• Reliability Engineering
• System Resilience
• +۴ مورد دیگر

Zequn Wang holds the position of Adjunct Associate Professor in Mechanical and Aerospace Engineering at Michigan Technological University (Michigan Tech) and concurrently serves as Associate Professor at the University of Electronic Science and Technology of China (UESTC). He earned his PhD in Industrial Engineering from Wichita State University (2014), MS in Thermodynamics from University of Science and Technology Beijing (2009), and BS in Mechanical Engineering (2006) from the same institution. His research focuses on design under uncertainty , uncertainty quantification , model calibration , and prognostics and health management . He leads the System Informatics & Reliability Laboratory (SIRL) at Michigan Tech, which develops computational algorithms for complex system design and health management, integrating statistics, optimization, machine learning, and simulation. Education : PhD, Industrial Engineering, Wichita State University (2014) MS, Thermodynamics, University of Science and Technology Beijing (2009) BS, Mechanical Engineering, University of Science and Technology Beijing (2006) Research Interests : Dr. Wang specializes in reliability engineering , system resilience , and uncertainty management , with applications in engineered material systems and smart sensing technologies. His work emphasizes prognostics and health management (PHM), time-variant reliability assessment , and accelerated failure analysis . The SIRL lab’s methodologies address challenges in rare event simulation, stochastic modeling, and robust design optimization. Lab and Collaborations : The SIRL lab recruits visiting scholars and graduate students with expertise in statistics, machine learning, optimization, and failure diagnostics. Applicants are encouraged to submit their CV and transcripts to Dr. Wang directly.

• Machine Learning
• Bayesian Nonparametrics
• Statistical Theory
• +۳ مورد دیگر

Peter Orbanz is a Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (UCL). He previously held roles as Assistant and Associate Professor in the Department of Statistics at Columbia University, and completed postdoctoral research at the University of Cambridge under Zoubin Ghahramani. His research focuses on machine learning, Bayesian nonparametrics, symmetry in statistical models, and dynamics. He has spent sabbaticals at the Isaac Newton Institute, Microsoft Research New England, and UC Berkeley. Orbanz earned his PhD in 2008 from ETH Zurich under Joachim M. Buhmann, with a thesis on Bayesian nonparametric models. His work bridges theoretical foundations of machine learning with practical applications, emphasizing symmetry properties and scalable inference methods. Current research includes causal uncertainty quantification, Gaussian process dynamics, and equivariant neural networks. He teaches advanced courses on probabilistic learning, Bayesian nonparametrics, and statistical theory at UCL and Columbia. His research group at UCL includes PhD students Hugh Dance, Kevin H. Huang, Vasco Portilheiro, and Vince Velkey. Former advisees include Morgane Austern (Harvard), Benjamin Bloem-Reddy (UBC), and Victor Veitch (Chicago). Orbanz has authored influential papers on topics such as exchangeable random structures, Gaussian universality, and scalable MCMC methods. His work often integrates concepts from probability theory, statistical physics, and computational neuroscience. He maintains active collaborations with institutions worldwide and contributes to open-source tools for Bayesian modeling.

• Bayesian Statistics
• Computational Statistics
• Markov Chain Monte Carlo
• +۵ مورد دیگر

Dr. Alexandros Beskos is a Reader in the Department of Statistical Science at University College London (UCL). He holds a PhD in Statistics from Lancaster University (2006) and a B.Sc. in Statistics from Athens University of Economics and Business (2000). His research focuses on computational statistics, Bayesian methods, Monte Carlo algorithms, and stochastic differential equations. Beskos has led significant grants, including the Leverhulme Trust Prize (2015-2018) and EPSRC grants, and has supervised multiple PhD students and postdoctoral researchers. His academic career includes roles such as Senior Lecturer at UCL (2013-2015) and Lecturer at UCL (2008-2012). He has collaborated with institutions worldwide, including Imperial College London, National University of Singapore, and Harvard University. His work addresses high-dimensional inverse problems, sequential Monte Carlo methods, and applications in biostatistics and econometrics. Beskos has published extensively in top journals like Annals of Applied Probability and Journal of Computational Biology . He is a Fellow of the Royal Statistical Society and actively contributes to academic service, including editorial roles and conference organization. His teaching spans statistical methods, probability, and stochastic processes at both undergraduate and graduate levels.

Maryam Tavakolمشاهده پروفایل

استادیار

• Uncertainty in Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.

• Machine Learning
• Statistical Inference
• Sequential Monte Carlo Methods
• +۳ مورد دیگر

Alessandro Mastrototaro holds a PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology. His research focuses on advancing statistical learning methods, particularly sequential Monte Carlo (SMC) and variational inference techniques for state-space models. He currently teaches Regression Analysis (SF2930) at KTH, serving as course responsible and instructor. His work emphasizes online learning algorithms that enable real-time parameter estimation and particle proposal adaptation in dynamic data environments. Key contributions include the development of the Online Variational Sequential Monte Carlo algorithm and the ALVar estimator for adaptive variance estimation in particle filters. Publications span machine learning conferences (e.g., ICML) and journals like the Journal of the American Statistical Association, showcasing expertise in computational statistics and algorithmic innovation.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Adam Ścibior is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC), situated in the Faculty of Science. His research focuses on artificial intelligence, machine learning, and probabilistic programming, with a strong emphasis on autonomous systems, generative models, and reinforcement learning applications in autonomous driving. He contributes to developing simulation benchmarks, diffusion models, and algorithms for multi-agent behavior prediction. His work bridges theoretical advancements in probabilistic inference with practical implementations in robotics and computer vision. Key research areas include autonomous vehicle navigation, scene simulation, and improving synthetic training data realism. He has published extensively on topics like diffusion models for traffic scene generation, reinforcement learning benchmarks for autonomous systems, and conditional permutation-invariant flows. His research often involves collaboration across disciplines, integrating probabilistic programming with real-world applications. Notable contributions include the TorchDriveEnv benchmark for autonomous driving and advancements in diffusion-based generative models for video inpainting and interactive navigation. While no specific awards or grants are explicitly listed, his work reflects a focus on impactful contributions to AI and robotics. No advising relationships or lab affiliations are detailed in the provided materials.

Ali Ehsanمشاهده پروفایل

پژوهشگر ارشد

• Renewable Distributed Generation
• Multi-energy Microgrids
• Network Planning and Optimization
• +۳ مورد دیگر

Ali Ehsan is a Postdoctoral Research Associate at the Department of Electrical and Electronic Engineering, The University of Manchester. His current research focuses on modeling and analyzing risks and uncertainties in multi-vector energy networks as part of the Supergen Energy Networks Hub. He investigates uncertainty propagation, quantification across timescales, and stakeholder communication strategies in interconnected energy systems. Education: PhD (Electrical Engineering, Zhejiang University, 2019); MSc (Renewable Energy Engineering, Kingston University London); BSc (Electrical Engineering, University of Engineering and Technology Lahore). Research Interests: Renewable distributed generation, multi-energy microgrids, network planning/optimization, and energy demand management. His work aligns with UN Sustainable Development Goals, particularly energy transition and climate action. Recent Articles Highlight Trends in: Demand Side Management in low-carbon grids, interdisciplinary education for energy systems, probabilistic assessment of grid vulnerabilities, and uncertainty quantification in gas-electricity networks. His 2025 works emphasize low-carbon technologies and smart grid integration. Grants/Advising: No specific grants or advisees listed. Collaborations include CIRED and POWERTECH conferences. Contributions to Energy Systems: Pioneering methods for multi-vector network resilience, scenario-based microgrid planning, and heat electrification impact studies.

Long Chenمشاهده پروفایل

مدرس

• Digital Twin Technology
• Systems Engineering
• Design Change Management
• +۲ مورد دیگر

Dr. Long Chen is a Lecturer at the School of Architecture, Building and Civil Engineering, Loughborough University. He holds a PhD and BEng, with professional affiliations including MCIOB and A.M.ASCE. Previously, he was a Postdoctoral Research Associate at Imperial College London's Centre for Systems Engineering and Innovation (CSEI), where he contributed to the CDBB-funded project 'Analysing Systems Interdependencies using a Digital Twin' and the Alan Turing Institute/Lloyd’s Register Foundation's 'Grand Challenge III: Data-Driven Design Under Uncertainty'. His research focuses on digital twin-driven approaches for design change management in complex systems and image-based digital twinning for as-built buildings. Key research areas include Systems Engineering, Data-Centric Engineering, and Building Information Modeling. Dr. Chen's publications primarily explore machine learning fairness, Bayesian computation, and computer vision techniques, with recent works addressing algorithmic bias mitigation, transformer calibration, and semantic segmentation robustness. His research demonstrates consistent applications in probabilistic modeling and adaptive systems. He maintains active collaborations with Imperial College London and the Alan Turing Institute, leveraging his expertise in systems interdependencies and digital twin implementations.

Chan Hock Pengمشاهده پروفایل

استاد

• Sequential analysis
• Change-point detection
• Monte Carlo methods
• +۲ مورد دیگر

Chan Hock Peng is a Professor at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His academic role focuses on advancing statistical methodologies and their applications in complex systems. His research interests include: Sequential analysis and change-point detection Monte Carlo methods and particle filtering Multi-armed bandit problems and decision optimization Notable contributions span theoretical advancements in sequential detection, bandit algorithms, and computational statistics. His work addresses challenges in detecting changes in multi-stream data, optimizing decision-making under uncertainty, and developing efficient methods for sparse signal analysis. Scientific Awards: No awards explicitly mentioned in the provided information. Advising and Grants: Details on his advising of students or involvement in grants are not available in the current data. Labs/Teams: Affiliated with IORA, contributing to research in operations research and analytics.

• Machine Learning
• Computational Statistics
• Causal Inference
• +۴ مورد دیگر

Dr. Rob Cornish is a Florence Nightingale Bicentenary Research Fellow at the University of Oxford's Department of Statistics. His research focuses on robustness in complex systems, emphasizing valid methodologies under minimal assumptions for real-world applications. He holds a DPhil from the AIMS CDT program under Arnaud Doucet and George Deligiannidis, and conducted postdoctoral work with Chris Holmes and Arnaud Doucet on causal inference and conformal prediction. Key research areas include uncertainty quantification, causal inference, and machine learning. His work spans Bayesian computational methods, Monte Carlo techniques, and generative models. Recent publications highlight advancements in neural network symmetries, equivariant diffusions, and causal falsification of digital twins. He is affiliated with the Statistical Theory and Methodology and Computational Statistics and Machine Learning research groups. Publications reflect a trend toward integrating theoretical rigor with applied challenges, such as scalable Bayesian inference and robust predictive models. His contributions bridge foundational statistical theory and modern machine learning applications, particularly in safety-critical system reliability.

Thang Buiمشاهده پروفایل

مدرس

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +۳ مورد دیگر

Thang Bui is a Lecturer (equivalent to tenure-track Assistant Professor) in Machine Learning at the School of Computing, Australian National University (ANU) since July 2022. Previously, he was a Lecturer at the University of Sydney (2018–2022) and spent two years (2019–2020) at Uber AI. He holds a PhD from the Cambridge Machine Learning Group at the University of Cambridge, supervised by Richard Turner and advised by Carl Rasmussen. Research Interests: His work focuses on probabilistic modeling and inference, Monte Carlo methods, distributed/continual learning, and model-based reinforcement learning. Current projects include uncertainty estimation in Gaussian processes and neural networks, adaptive models for changing environments, and interpretable machine learning techniques. Awards: Best Paper Award, ACL Workshop on Information Extraction from Scientific Publications (2023) Best Paper Award, NeurIPS workshop on Deep Learning through Information Geometry (2020) Best Paper Award, NIPS Workshop on Advances in Approximate Bayesian Inference (2015) Advising & Grants: Actively recruiting PhD/MPhil students and postdoctoral fellows. His research has been supported by grants focusing on Bayesian methods and scalable machine learning. He has advised students on topics ranging from Gaussian processes to federated learning and continual learning. Labs/Teams: Leads a research group exploring uncertainty quantification, adaptive learning systems, and probabilistic AI at ANU's College of Engineering and Computer Science.

Carlo Berzuiniمشاهده پروفایل

استاد

• Biostatistics
• Causal inference
• Genomic epidemiology
• +۲ مورد دیگر

Professor Carlo Berzuini holds the Research Chair in Biostatistics at the University of Manchester with joint appointments at Cambridge University and University of Pavia. His pioneering work develops statistical methods for causal inference in genomics and biomedical research, with applications spanning cardiovascular disease, cancer diagnostics, and COVID-19 therapeutics. Research focuses on: Bayesian Mendelian randomization for genomic epidemiology Causal mediation and mechanistic interaction analysis Sequential Monte Carlo methods for disease prediction Biomarker-based early cancer detection Recent COVID-19 research identified renin-angiotensin inhibitors' protective effects. He leads European consortia including FP7 MIMOmics and Horizon 2020 projects. Honors include Isaac Newton Trust and MRC research awards. Educational contributions include establishing Manchester's MSc in Biostatistics and teaching at Cambridge, Pavia, and Leiden. He chaired the Royal Statistical Society Bioinformatics Study Group and serves on editorial/review boards for major journals and funding bodies.

Omiros Papaspiliopoulosمشاهده پروفایل

استاد

• Computational Statistics
• Machine Learning
• Probability
• +۵ مورد دیگر

Omiros Papaspiliopoulos is a Full Professor at Bocconi University’s Department of Decision Sciences. He joined Bocconi in 2021, previously serving as an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona. His academic career includes roles at Warwick, Oxford, Lancaster, Berlin, Osaka, Paris, Madrid, and Lima. He has been awarded the Royal Statistical Society’s Guy Medal (2010) and the DeGroot Prize for his influential work with Nicolas Chopin. His research focuses on computational statistics, spanning Bayesian inference, machine learning, probability, and applied mathematics. Notably, he founded Europe’s first Master in Data Science at the Barcelona Graduate School of Economics (2013) and directed the Data Science Center until 2021. Since 2022, he has been Director of Bocconi’s Bachelor of Science in Economics, Management, and Computer Science (BEMACS). Papaspiliopoulos is co-Editor of Biometrika (top-4 in Statistics) and Associate Editor of the Journal of Uncertainty Quantification . He has delivered keynote talks at institutions like Chicago Booth, Duke, and LSE, emphasizing the societal impact of data science. His teaching spans courses on data science, statistics, machine learning, and quantitative methods in social sciences. Education & Academic Leadership PhD in Statistics (Implied by career trajectory) Founded Bocconi’s BEMACS program (2022) Directed the Master in Data Science (2013–2021) Executive course design for SDA Bocconi and Barcelona School of Economics Research Interests Computational Statistics & Bayesian Methods Machine Learning & Applied Mathematics Data Science in Social Sciences Hidden Markov Models & State-Space Systems High-Dimensional Inference & Scalable Algorithms Recent Work Trends His publications emphasize scalable Bayesian computation, treatment effect inference, and applications in social sciences. Recent topics include confounder importance learning, particle filtering, and computational efficiency in hierarchical models. Collaborations bridge theory and practice, addressing challenges in policy, finance, and public administration. Awards & Recognition 2010: Royal Statistical Society’s Guy Medal DeGroot Prize for Nonparametric Hidden Markov Models Onassis Foundation Scholar Grants & Outreach Directed the Barcelona Data Science Center (2016–2021) Outreach activities: Talks for high-school students, policymakers, and the European Commission Editorial roles in top journals: Biometrika , Journal of Uncertainty Quantification Labs & Teams Current affiliation with Bocconi’s Department of Decision Sciences and involvement in interdisciplinary data science initiatives.

• Operations Research
• Game Theory
• Optimization
• +۳ مورد دیگر

Julio González Díaz holds the position of General Coordinator of Doctorate and USC at the University of Santiago de Compostela , within the Department of Statistics, Mathematical Analysis and Optimization . His work focuses on advancing mathematical methodologies in optimization, game theory, and operations research, with notable contributions to polynomial optimization, energy network modeling, and algorithmic development. He oversees doctoral programs and contributes to academic governance at the university level. His research interests span game theory (especially cooperative and non-cooperative frameworks), optimization techniques (including global and polynomial optimization), and applied modeling in energy systems and supply chain networks. He has developed algorithms like RAPOSa for polynomial optimization and explored applications such as gas transmission networks and portfolio design. Publications emphasize computational efficiency in optimization, fairness in resource allocation , and interdisciplinary applications of game theory. His work bridges theoretical foundations with practical implementations, as seen in his contributions to open-source solvers and algorithmic advancements. He has received institutional recognition, including an Excellence Award in doctoral program administration. His academic leadership includes coordinating master's and PhD programs in statistical techniques and operations research, fostering educational innovation and international collaborations.