جستجوی استاد راهنمای دانشگاهی

Julien Bectمشاهده پروفایل

دانشیار

• Bayesian Optimization
• Gaussian Processes
• Uncertainty Quantification
• +۴ مورد دیگر

Julien Bect is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His primary research focuses on Bayesian optimization, Gaussian processes, uncertainty quantification, and sequential design of experiments. He has collaborated extensively with researchers such as Emmanuel Vazquez and Paul Feliot, contributing to advancements in statistical modeling and computational methods for engineering and scientific applications. His work emphasizes the development of efficient algorithms for estimating probabilities of failure, excursion sets, and quantile-based inversion in complex systems. Notable contributions include methodologies for multi-fidelity computer experiments, Bayesian subset simulation, and adaptive experimental design. His research bridges theoretical foundations in statistics with practical applications in fields like reliability engineering, food safety, and electrical systems. Bect has published numerous articles in prestigious journals such as Technometrics , SIAM/ASA Journal on Uncertainty Quantification , and Bernoulli , showcasing his expertise in statistical inference and optimization. His recent work explores novel approaches in quantile set inversion and complex-valued frequency response modeling, reflecting his commitment to advancing methodologies for real-world stochastic systems.

• Forecasting
• Time Series Analysis
• Computational Statistics
• +۶ مورد دیگر

Rob Hyndman is a Professor of Statistics at Monash University , Australia, in the Department of Econometrics and Business Statistics. He is an accredited statistician with the Statistical Society of Australia and a Fellow of both the Australian Academy of Science and the Academy of the Social Sciences in Australia . As former Editor-in-Chief of the International Journal of Forecasting (2005-2018), he has shaped academic discourse in his field. Research Interests : Forecasting, time series analysis, computational statistics, anomaly detection, and exploratory data analysis with applications in energy analytics, data visualization, and hierarchical forecasting Education : Bachelor of Science (Honours) and PhD from the University of Melbourne Leadership : Director of the International Institute of Forecasters (2005-2018) His scientific contributions span business analytics, machine learning, demography, and computational statistics, with recent publications focusing on responsible forecasting, hierarchical time series modeling in emergency medical services, and advanced decomposition algorithms like MSTL. His work addresses multiple UN Sustainable Development Goals through applications in public health (forecasting pandemic cases), energy (demand forecasting), and social equity (indigenous population life expectancy estimation). He has received prestigious awards including the Moran Medal (2007, Australian Academy of Science) Pitman Medal (2021, Statistical Society of Australia) Australian Awards for University Teaching (2022) Vice-Chancellor's Award for Innovation in Learning (2020) HP Innovation Research Award (2010) Vice Chancellor's Award for Postgraduate Supervision (2008) and multiple Dean's awards. Rob has supervised over 30 PhD and Masters students while developing open-source forecasting tools and organizing annual WOMBAT workshops on business analytics.

Adam Griffinمشاهده پروفایل

پژوهشگر

• Epidemic Models
• Mathematical Statistics
• Monte Carlo Simulation
• +۴ مورد دیگر

Adam Griffin is a Researcher at the Mathematics Institute of the University of Warwick. He holds an MMath from the same institution and is currently pursuing a MASDOC PhD, supervised by Simon Spencer and Gareth Roberts. His research focuses on epidemic models, quasi-stationary distributions, Monte Carlo methods, and probability theory, with applications to influenza dynamics and neglected tropical diseases. He has contributed to advancements in resampling techniques for Sequential Monte Carlo methods, such as Regional Resampling and Combine-Split Resampling. Education: MMath (University of Warwick) Research Interests: Epidemic modeling and quasi-stationary distributions Monte Carlo simulation techniques Probability theory and stochastic processes Quantum turbulence and fluid dynamics Teaching: SO243: Practice of Quantitative Research (2015) ST202: Stochastic Processes seminar teacher (2014–15) First-year mathematics supervisor (2011–12, 2013–14) Activities: Cellist in the university symphony orchestra Member of the university folk society's ceilidh band Founder of The Night Before folk dance band

Jem Corcoranمشاهده پروفایل

دانشیار

• Applied Probability
• MCMC Methods
• Bayesian Statistics
• +۳ مورد دیگر

Dr. Jem Corcoran is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder. His research focuses on applied probability and computational statistics, with an emphasis on developing advanced Monte Carlo methods and Bayesian techniques. He specializes in MCMC (Markov Chain Monte Carlo) algorithms, perfect sampling, and stochastic simulation across disciplines such as image processing, chemical reaction networks, and econometric modeling. His work integrates theoretical rigor with practical applications, addressing challenges in rare event simulation, Bayesian network inference, and high-dimensional data analysis. Notably, he has contributed to advancements in Gibbs sampling, particle filtering, and the application of coupler methods for continuous distributions. His research also explores computational efficiency in stochastic processes and algorithmic design for complex systems. Dr. Corcoran’s scholarly contributions span over two decades, with publications on topics ranging from perfect sampling in Kac equations to Bayesian fusion of particle estimates. His methodologies have been applied in fields such as systems biology, quantum mechanics, and financial time series analysis. Despite his extensive publication record, no academic awards or grants are explicitly mentioned in the provided text.

José R. Herreroمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +۳ مورد دیگر

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.

Veronika CZELLARمشاهده پروفایل

استاد

• Financial Econometrics
• Robust Statistics
• Simulation-Based Estimation
• +۴ مورد دیگر

Veronika CZELLAR is a Professor in Econometrics and Data Science at SKEMA Business School, France. She holds a PhD in Econometrics and Statistics from the University of Geneva (2006) and has held academic positions at institutions including EDHEC, EM Lyon, HEC Paris, and the University of Washington. Her research focuses on simulation-based estimation, financial econometrics, and robust statistics, with publications in top journals such as the Journal of Financial Economics and Journal of Econometrics . She teaches courses on Portfolio Management, Financial Econometrics, and Data Science. Education: PhD in Econometrics and Statistics, University of Geneva, 2006 Master in Econometrics, University of Geneva, 2002 Research Interests: Her work spans simulation-based estimation, financial econometrics, robust statistics, and applications in asset pricing and M&A analysis. Recent studies include multifractal cryptocurrency dynamics and the impact of M&A rumors on transaction values. Key Contributions: Her methods address challenges in indirect inference and robust filtering, with applications in volatility modeling and market participation analysis. She actively presents at conferences such as the Society for Financial Econometrics and SKEMA Finance Seminars. Teaching & Mentorship: Courses include Portfolio Management, Financial Econometrics, R/VBA Programming, and Data Science. While her profile does not explicitly list students, her academic roles suggest involvement in PhD/Master’s advising.

• Coding Theory
• Information Theory
• Forward Error Correction (FEC)
• +۵ مورد دیگر

Mohsen Moradi is a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at Northeastern University. His research focuses on advanced coding theory, particularly in the development of Polarization-Adjusted Convolutional (PAC) codes, forward error correction, and decoding algorithms. He explores intersections between reinforcement learning and signal processing to enhance code performance. His work emphasizes practical applications such as bounded-complexity sequential decoding, tree pruning techniques, and Monte-Carlo-based code construction. Key contributions include optimizing PAC codes through search-constrained algorithms and analyzing their performance alongside Reed-Solomon and Reed-Muller codes. Recent publications (2020–2025) highlight trends in PAC/SC/Polar code design, with a focus on computational efficiency, metric optimization, and algorithmic innovation. His research bridges theoretical insights with real-world implementation challenges in communication systems. Moradi’s explorations extend to hybrid coding systems (e.g., concatenated codes) and stochastic methods like guessing-based decoding. He collaborates with academic and industry partners to advance next-generation communication protocols requiring robust error correction at high data rates.

Ning Ningمشاهده پروفایل

استادیار

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +۴ مورد دیگر

Ning Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and the Institute for Quantum Science and Engineering. She co-founded the Stochastic Processes Seminar in the Department of Mathematics. Her research focuses on stochastic processes, Markov chains, machine learning, and quantum computing. Prior to her academic career, she worked as a software developer and database architect in Los Angeles for four years, specializing in data analysis and parallel computing. Education: B.S. in Math from Shandong University (China Math Base), M.S. in Math from the University of Southern California, and Ph.D. in Statistics and Applied Probability from UCSB. She held postdoctoral positions at the University of Washington (Applied Math) and the University of Michigan (Statistics). Research interests include stochastic algorithms, Monte Carlo methods, hidden Markov models, and quantum counterparts of classical methods. She is an Associate Editor for Bayesian Analysis (since 2025), Journal of Computational and Graphical Statistics (since 2024), and Statistics and Computing (since 2023). Recent talks and contributions include invited sessions at the Bayes Comp 2025 conference and the KAUST Stochastic Numerics Workshop. Her work spans theoretical advancements and computational innovations in statistical methods, with applications to large-scale data analysis and machine learning.

• Statistics
• Probability
• Markov Chain Monte Carlo
• +۶ مورد دیگر

Professor Yves Atchade is a faculty member at the Department of Mathematics and Statistics at Boston University, holding the Duan Family Faculty Fellow of Data Science position. He leads the FORMES research group focused on statistical methods for environmental sciences. His work bridges probability, high-dimensional computation, and applied environmental modeling. Current projects include large-scale Bayesian inference , deep learning for rainfall prediction in West Africa , and inverse problems in remote sensing . Collaborations with institutions like ENSEA, Abidjan and the TAHMO Foundation drive environmental sensor network deployments in Côte d'Ivoire. His research emphasizes MCMC algorithm design and unbiased Monte Carlo methods , with applications spanning environmental science, machine learning, and high-dimensional statistics. Recent publications explore unbiased MCMC , cyclical sampling , and Bayesian PINNs . Scientific contributions include: Unbiased MCMC with Couplings (2020) High-dimensional Bayesian Computation (2018) Efficiency bounds in semiparametric models (2020) Adaptive MCMC Confidence Intervals (2016) He actively mentors students and postdocs, with methodological developments in Markov chain theory , stochastic optimization , and Monte Carlo variance reduction . The FORMES group welcomes inquiries from prospective researchers via email at atchade@bu.edu .

Jian Caoمشاهده پروفایل

استادیار

• Computational Statistics
• Gaussian Process Regression
• Spatial Statistics
• +۴ مورد دیگر

Jian Cao is an Assistant Professor in the Department of Mathematics at the University of Houston, specializing in Computational and Spatial Statistics. He earned his Ph.D. in Statistics from KAUST under Dr. Marc Genton and completed postdoctoral work at Texas A&M University under Dr. Matthias Katzfuss. His research focuses on scalable Gaussian Process (GP) regression methods, including Vecchia approximations, truncated multivariate normal distributions, and applications in climate and agricultural science. Education includes a B.S. in Mathematics from University of Science and Technology of China (2014), a Master in Finance from Shanghai Jiaotong University (2016), and a Ph.D. in Statistics from KAUST (2020). Research interests emphasize computational efficiency in statistical modeling, with contributions to scalable algorithms for high-dimensional data. Key themes include Gaussian processes, multivariate normal probabilities, and low-rank matrix methods. His work bridges theory and application, addressing challenges in environmental and agricultural data science. Notable awards include the Al-Kindi Statistics Student Research Award (2020) and Best Student Paper (2019). His publications span journals like the Journal of the American Statistical Association and conferences such as ICML, focusing on scalable statistical methods and software development (e.g., tlrmvnmvt package). Teaching responsibilities include courses on statistics for the sciences and inferential statistics at the University of Houston. His software contributions prioritize high-performance computing solutions for large-scale geospatial and statistical problems.

Mónica Fernandez Bugalloمشاهده پروفایل

استاد

• Statistical Signal Processing
• Bayesian Analysis
• Sequential Monte Carlo Methods
• +۴ مورد دیگر

Dr. Mónica Fernandez Bugallo is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University. She holds a PhD in Computer Science and Engineering from University of A Coruña, Spain, where she received Awards for Best Student and Best PhD Dissertation. Her research focuses on statistical signal processing with emphasis on Bayesian analysis, sequential Monte Carlo methods, and stochastic optimization applied to multiuser communications, smart antenna systems, and vehicle positioning. Dr. Bugallo teaches courses in digital communications, stochastic processes and information theory. She has initiated several STEM education programs focused on engaging underrepresented groups and served as the inaugural faculty director of the Women In Science and Engineering (WISE) Honors program. She currently chairs the IEEE Signal Processing Society Education Committee.

Michael Fuمشاهده پروفایل

استاد

• Simulation Modeling
• Operations Research
• Financial Engineering
• +۵ مورد دیگر

Michael Fu is the Smith Chair of Management Science at the Robert H. Smith School of Business, University of Maryland, with joint appointments in the Institute for Systems Research and the Department of Electrical and Computer Engineering. He holds a Professor rank and specializes in simulation optimization, stochastic processes, and financial engineering. His research bridges theoretical advancements and practical applications in supply chain management, healthcare systems, and artificial intelligence. His academic accolades include INFORMS College on Simulation's Outstanding Publication Award (1998, 2019), IEEE Fellow (2007), and INFORMS Simulation Society's Distinguished Service Award (2018). He has held editorial roles at journals such as Management Science and Operations Research , and served as Program Director at the National Science Foundation (2010–2012, 2015). Research Focus: Simulation optimization, stochastic control, and Monte Carlo methods applied to healthcare, finance, and manufacturing. Key Contributions: Co-author of seminal works like Conditional Monte Carlo and Simulation-Based Algorithms for Markov Decision Processes . Recent Trends: Integrating AI techniques (e.g., Monte Carlo Tree Search) with classical stochastic optimization frameworks. Fu's interdisciplinary work spans multiple domains, including kidney transplantation logistics, financial risk modeling, and pandemic response strategies (e.g., real-time COVID-19 decision support systems). He has advised numerous research initiatives funded by NSF, SEMATECH, and Air Force Office of Scientific Research, emphasizing practical applications of stochastic methods.

• Reinforcement Learning
• Online Learning Algorithms
• Exploration-Exploitation Dilemma
• +۷ مورد دیگر

Csaba Szepesvári is a Professor in the Department of Computing Science at the University of Alberta and a Senior Staff Research Scientist at DeepMind, leading the Foundations team. He holds the Canada CIFAR AI Chair and is a Fellow at Amii. His research focuses on reinforcement learning (RL), online learning, and theoretical foundations of sequential decision-making. Key areas include exploration-exploitation strategies, Markov Decision Processes, and stochastic control. He has co-authored over 225 publications, including seminal works like Bandit Algorithms (2020) and Algorithms for Reinforcement Learning (2010). His academic affiliations include the Reinforcement Learning & Artificial Intelligence Lab (RLAI) at the University of Alberta and editorial roles at journals like Mathematics of Operations Research and the Journal of Machine Learning Research. Awards include the ECML/PKDD Test of Time Award (2016) and recognition for contributions to ICML, UAI, and other conferences. His work bridges theoretical insights with practical applications in AI, robotics, and automated decision systems. Research interests span efficient RL algorithms, statistical guarantees for offline learning, and the intersection of optimization and uncertainty quantification. Collaborations with institutions like DeepMind emphasize foundational advances in AI, while his teaching spans graduate and undergraduate courses in machine learning and algorithms. Key achievements include pioneering contributions to exploration strategies in bandits and RL, developing PAC-Bayes bounds for neural networks, and advancing understanding of double descent phenomena in overparameterized models. His lab (RLAI) is a hub for cutting-edge research in RL theory and applications.

Chris Wigginsمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Computational Biology
• +۱۲ مورد دیگر

Chris Wiggins is an Associate Professor of Applied Mathematics and Systems Biology at Columbia University, with affiliations in the Department of Statistics. He is based in the School of Engineering and Applied Science and conducts interdisciplinary research at the intersection of data science, machine learning, and biological systems. His work bridges computational modeling with applications in genomics, single-molecule biophysics, digital health, and disinformation studies. His research interests include: Data Science and its societal implications Machine learning for biological networks and gene regulation Bayesian and statistical inference in single-molecule experiments Privacy-aware data analysis and algorithmic ethics Modeling in digital health, including menstrual cycle prediction and cancer drug screening Information-theoretic approaches to network structure and dynamics The trends in his recent publications reveal a growing focus on the ethical and societal dimensions of data science, as seen in works like How Data Happened and An Agenda for Disinformation Research , while maintaining strong technical contributions in biomedical modeling, particularly in cancer and women’s health. His work combines rigorous statistical methods with real-world data challenges. Notable scientific contributions include foundational work on ARACNE for gene regulatory network inference, Bayesian modeling of single-molecule dynamics, and methodological advances in dose-response modeling. His publications span top journals in bioinformatics, computational biology, and data science. He has advised doctoral students in applied mathematics and computational biology, though specific names are not listed in the provided text. His research has been supported by interdisciplinary grants, particularly in systems biology and data science initiatives. He is actively involved in shaping the philosophy and pedagogy of data science as a field. Chris Wiggins is associated with research centers at Columbia that focus on applied statistics, probability, and data science. He contributes to seminars and educational programs that promote quantitative reasoning across disciplines, particularly in the context of modern data-driven challenges.

Nianqiao Juمشاهده پروفایل

استادیار

• Statistical Inference
• Markov Chain Monte Carlo
• Bayesian Inference
• +۳ مورد دیگر

Nianqiao Ju is an Assistant Professor of Statistics at Purdue University's Department of Statistics within the College of Science. They hold a B.A. in Mathematics and Physics from Wellesley College (2016) and a Ph.D. in Statistics from Harvard University (2021). Their research focuses on computational methods for statistical inference, particularly Markov Chain Monte Carlo (MCMC) techniques and Bayesian inference from privatized data. Notable work includes developing privacy-preserving statistical methods and analyzing infectious disease transmission dynamics. Research interests span computational statistics, data privacy, MCMC algorithms, and applications in epidemiology. Their articles explore topics like SOMA samplers, privacy-aware Bayesian inference, and agent-based modeling of disease spread. Ju has received the IMS Hannan Graduate Student Travel Award (2020). Current research emphasizes bridging statistical methodology with computational challenges in data privacy and large-scale epidemiological models. Office: Math 508 | Phone: 765-494-0021