جست‌وجوی استاد و پژوهشگر

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Samuel Kaskiمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Modelling
• Artificial Intelligence
• +۵ مورد دیگر

Samuel Kaski is a Professor of Artificial Intelligence and Machine Learning at Aalto University and the University of Manchester. He leads the Finnish Center for Artificial Intelligence (FCAI) and the Manchester Centre for AI Fundamentals (AI-FUN). His research focuses on probabilistic modeling, Bayesian inference, collaborative AI, and applications in health, biology, and user interaction. He holds roles such as Director of the ELLIS Unit Helsinki and Founding Director of the ELLIS Institute Finland. Research interests include foundational machine learning, distribution shifts, generative models, and human-AI collaboration. He has pioneered work on Bayesian experimental design, simulation-based inference, and AI-assisted drug discovery. His projects span interdisciplinary collaborations in synthetic biology, cognitive science, and healthcare. Recent publications highlight advancements in amortized Bayesian methods, robust learning, and multi-agent systems. Notable awards include the Turing AI Fellowship (2021) and the Aalto Pioneering Excellence Award (2022). Over 36 PhD students and numerous postdocs have been mentored by Kaski, with many securing faculty positions. His teams operate across institutions, emphasizing open-source tools and impactful applications. Key labs include FCAI, ELLIS Finland, and AI-FUN, fostering innovation in AI fundamentals and societal impact.

• Predictive Control
• Machine Learning
• Stochastic MPC

Lukas Hewing is a Lecturer at ETH Zürich's Department of Mechanical and Process Engineering. He received his BSc and MSc in Mechanical Engineering and Automation Engineering from RWTH Aachen University, where he was awarded the Springorum Denkmünze for his master's work. He is currently pursuing a PhD in the Intelligent Control Systems Group at ETH Zürich. His research focuses on predictive control of dynamical systems with machine learning methods and stochastic MPC. Key areas include Gaussian process-based control, autonomous racing applications, and safety-critical systems design. His work bridges control theory with practical implementations in robotics and medical devices. Hewing's publications demonstrate a strong focus on learning-based control methods applied to autonomous systems and medical technology. Recent work shows increasing applications in safety-critical domains like autonomous vehicles and ventilators, combining theoretical rigor with practical validation.

Morteza Lahijanianمشاهده پروفایل

دانشیار

• Control Theory
• Formal Methods
• Robotics
• +۱۰ مورد دیگر

Morteza Lahijanian is an Associate Professor in the Aerospace Engineering Sciences department at the University of Colorado Boulder , with a courtesy appointment in Computer Science. He received his PhD (2013), MS (2009), and BS (2005) in Mechanical Engineering and Bioengineering from Boston University and UC Berkeley, respectively. His research focuses on safe autonomy through correct-by-construction algorithmic approaches, integrating control theory and formal methods to address challenges in robotics, hybrid systems, and human-robot interaction. Education PhD, Mechanical Engineering (Control Theory and Robotics), Boston University, 2013 MS, Mechanical Engineering (Control Theory and Robotics), Boston University, 2009 BS, Bioengineering, University of California, Berkeley, 2005 Lahijanian leads the ARIA Systems Group , which develops computational frameworks for reliable and intelligent autonomy. His work emphasizes stochastic systems , temporal logic , and multi-agent planning , with applications in autonomous vehicles and safety-critical robotics. Recent research trends include Bayesian verification, deep reinforcement learning, and polynomial barrier function analysis for uncertain environments. His students, including Anne Theurkauf, Justin Kottinger, and Kandai Watanabe, have contributed to advancements in motion planning, strategy synthesis, and stochastic control. Lahijanian's group has published extensively at top venues like IEEE CDC, ICRA, and NeurIPS, though no specific scientific awards are highlighted in the provided texts.

Anna Scampicchioمشاهده پروفایل

پژوهشگر

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +۳ مورد دیگر

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

Boris Levitمشاهده پروفایل

استاد

• Non-parametric estimation
• Semi-parametric models
• Optimal design in non-parametric regression
• +۵ مورد دیگر

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

Fabrizio Leisenمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +۳ مورد دیگر

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.

Chin-Hui Leeمشاهده پروفایل

استاد

• Speech Recognition
• Machine Learning
• Signal Processing
• +۲ مورد دیگر

Dr. Chin-Hui Lee is a Professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering. He holds a doctorate in Electrical Engineering from the University of Washington and previously held positions at Bell Laboratories where he became a Distinguished Member of Technical Staff. His research spans speech processing, machine learning, and quantum sensing. Major research areas include deep learning approaches for speech enhancement and recognition, multimodal audio-visual systems, quantum metrology, and applications of generalized Gaussian distributions in signal processing. Recent publications reflect his interdisciplinary research, covering topics from neural network architectures for speech separation to quantum sensing techniques and clinical oncology trials. His work integrates principles from computer science, electrical engineering, and quantum physics.

Mark Pittمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Speech Perception
• Computational Cognitive Modeling
• +۴ مورد دیگر

Dr. Mark Pitt is an Associate Professor and Associate Chair of Cognitive Psychology at The Ohio State University's Department of Psychology within the College of Arts and Sciences. He holds a PhD from Yale University (1989), an MS from Yale (1987), and a BA in Psychology from UCLA (1984, cum laude). His research focuses on two primary areas: Spoken Language Understanding (e.g., mechanisms of speech perception and the Buckeye Speech Corpus) and Computational Cognitive Modeling (e.g., Bayesian optimization, adaptive design methods). He collaborates extensively with Emeritus Professor Jay Myung on optimizing experimental designs and statistical inference techniques. Key awards include the Distinguished Professor title (2019), the Joan N. Huber Faculty Fellowship (2007), and the Hatcher Memorial Award (2004). His work spans interdisciplinary applications, from neuroscience to materials science. The Language Perception Laboratory under his leadership advances studies on auditory attention and speech processing. Recent publications emphasize Bayesian methods in decision-making, risk assessment, and optimizing experimental protocols. Education : Yale University (PhD 1989, MS 1987), UCLA (BA 1984). Affiliations : Ohio State's Department of Psychology, Language Perception Lab. Grants/Support : Extensive funding history via fellowships and collaborative grants.

Vincent Vuمشاهده پروفایل

دانشیار

• High-Dimensional Data Analysis
• Statistical Learning
• Statistical Computing
• +۳ مورد دیگر

Vincent Vu is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He holds a PhD from the University of California, Berkeley (2009). His research focuses on high-dimensional statistics, statistical machine learning, and their applications in neuroimaging, particularly fMRI. He previously worked as a software engineer in Silicon Valley for over six years, blending computational and statistical expertise. His expertise includes statistical computing, multivariate analysis, and computational sufficiency. He has been funded by the NSF and received the 2012 AISTATS Best Paper Award. He serves as an associate editor for the Journal of the American Statistical Association and The American Statistician. His work bridges statistical theory and computation, addressing challenges in high-dimensional data analysis. Collaborations involve neuroscientific applications, combining algorithmic innovation with real-world problem-solving.

Xinyi Xuمشاهده پروفایل

استاد

• Bayesian Statistics
• Model Uncertainty and Selection
• Decision Theory
• +۴ مورد دیگر

Xinyi Xu is a Professor of Statistics and Vice Chair for Graduate Studies at The Ohio State University's Department of Statistics. She is affiliated with the Institute for Population Research and serves as an Associate Editor for Bayesian Analysis . Her research focuses on Bayesian hierarchical modeling, decision theory, and high-dimensional data analysis with applications in biostatistics, sociology, and finance. Education Ph.D. in Statistics, University of Pennsylvania (2005) M.A. in Statistics, University of Pennsylvania (2003) B.S. in Mathematics, University of Science & Technology of China (2001) Research Interests Bayesian hierarchical modeling Statistical decision theory Model selection and averaging Applications in biostatistics, sociology, and finance Grants National Science Foundation grants (DMS-2015552, DMS-1613110, DMS-1209194) National Institute of Child Health grants National Center for Research Resources grants Labs/Teams Collaborates with the Translational Data Analytics center at Ohio State and interdisciplinary experts.

John Hughesمشاهده پروفایل

دانشیار

• Biostatistics
• Spatial Statistics
• Bayesian Methods
• +۴ مورد دیگر

John Hughes is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. He holds a PhD in Statistics from Penn State University and has over 29 years of experience in academia, with previous appointments at institutions like the University of Minnesota and the University of Colorado. His research focuses on methodological advancements in statistical modeling for dependent data, Bayesian methods, and statistical computing. His interdisciplinary work spans environmental health, bioimaging, magnetic resonance safety, and vaccine hesitancy. He has developed numerous software packages for R and Perl, including copCAR and batchmeans. Education: PhD in Statistics, Penn State University MS in Statistics, Penn State University MS in Applied Computer Science, Frostburg State University BS in Mathematics and Computer Science, Frostburg State University Teaching: Courses include Advanced R Programming, Biostatistics, Population Health Data Science, and Computational methods. His research interests emphasize spatial and spatiotemporal data analysis, with applications to public health and medical imaging. He has consulted for organizations such as the Minnesota Center for Chemical and Mental Health and Temple University. His software contributions include packages like krippendorffsalpha for agreement measurement and copCAR for spatial regression modeling. Recent work focuses on improving statistical inference methods, analyzing vaccination refusal patterns, and developing frameworks for copula-based agreement coefficients. His articles highlight innovations in Bayesian computation, spatial epidemiology, and nonparametric statistics. Dr. Hughes leads academic initiatives in biostatistics and health data science, fostering interdisciplinary collaborations and advancing statistical methodologies for real-world applications.

• Image Processing
• Computer Vision
• Earth Observation
• +۴ مورد دیگر

Gabriele Facciolo is a Professor at the Image Processing Group within ENS Paris-Saclay. His research focuses on advanced image processing techniques, computer vision, and remote sensing applications, with an emphasis on satellite imagery analysis. He is an active member of the ELLIS society, contributing to interdisciplinary research in computational imaging and geospatial technologies. His work bridges theoretical advancements and practical applications, including video restoration, super-resolution techniques, methane plume detection, and large-scale dataset development for environmental monitoring. Recent contributions highlight innovations in Gaussian splatting for Earth observation, GPU-accelerated stereo pipelines, and efficient annotation methods. His research often addresses challenges in low-latency systems, multispectral data fusion, and automated environmental monitoring. Key publications include breakthroughs in satellite image processing, methane detection algorithms, and novel datasets for shadow and visibility estimation. His methodologies are widely recognized for their impact on geospatial analysis and real-world applications in environmental science and industrial monitoring.

• Materials Chemistry
• Biomaterials
• Sustainability
• +۳ مورد دیگر

Newell Washburn is an Associate Professor in the Departments of Chemistry and Biomedical Engineering at Carnegie Mellon University, with a courtesy appointment in Materials Science and Engineering. He is affiliated with the Mellon College of Science and leads the Washburn Group, focusing on sustainable materials chemistry and machine learning applications. His work emphasizes reducing environmental impact through innovations in biobased polymers, cement systems, and advanced manufacturing. Education: Ph.D. in Chemistry (University of California, Berkeley, 1998); B.S. in Chemistry (University of Illinois at Urbana-Champaign, 1993). Prior roles include Research Chemist at NIST (2000–2004) and Post-Doctoral Associate at the University of Minnesota (1998–2000). Research interests span materials sustainability, polymer formulation, and machine learning-driven design. Key projects include developing hierarchical machine learning algorithms for rapid material optimization, biobased polyurethanes, and low-carbon cement alternatives like LC3. His work bridges laboratory experimentation with computational modeling, preparing students for interdisciplinary challenges. Recent articles highlight advancements in cement rheology modeling, autonomous laboratory networks, and drug design via diffusion models. Awards include the Covestro Global Science Award (2020) and the Coulter Foundation Translational Research Award (2010). Labs/Teams: The Washburn Group collaborates across disciplines, integrating chemistry, engineering, and data science to address global sustainability goals.

• 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 .