جست‌وجوی استاد و پژوهشگر

Ian Carrollمشاهده پروفایل

دانشیار

• Microbiome Research
• Gut-Brain Axis
• Nutritional Therapies
• +۳ مورد دیگر

Ian Carroll, PhD is an Associate Professor in the Department of Nutrition at the UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill. His research focuses on gastrointestinal biology and disease, with specific expertise in microbiome interactions. He directs the Carroll Lab and holds affiliations with the Center for Gastrointestinal Biology and Disease (CGIBD), where he serves as a Microbiome Focus Area investigator. Dr. Carroll's research centers on mechanisms through which intestinal microbiota and their metabolites influence gastrointestinal physiology, behavior, and weight regulation. His work spans microbiome dynamics in obesity, diabetes, and eating disorders , with particular emphasis on short-chain fatty acid physiology , gut-brain axis communication , and nutritional interventions . His laboratory has pioneered techniques for biological sample collection and bacterial DNA isolation from human fecal and mucosal samples. His publication trends reveal strong focus on Microbiome responses to dietary interventions (fiber, almonds, purified diets) Longitudinal biomarker discovery using interpretable machine learning Clinical applications in hypertension, bariatric surgery, and anorexia nervosa Cross-species validation through murine models Award Highlights: CGIBD Pilot and Feasibility Award (2012) Dr. Carroll maintains active collaborations across multiple disciplines including gastroenterology, immunology, and computational biology. His work integrates molecular techniques with clinical studies to develop nutritional therapies for gastrointestinal diseases. Current research examines microbial contributions to weight regulation post-bariatric surgery and mechanisms underlying microbiome-mediated blood pressure control. The Carroll Lab operates within the MBRB building facility, conducting translational research bridging basic microbial science with clinical applications in metabolic and gastrointestinal disorders.

Mark Steelمشاهده پروفایل

استاد

• Bayesian Statistics
• Econometrics
• Statistical Theory
• +۴ مورد دیگر

Mark Steel is a Professor of Statistics at the University of Warwick, Department of Statistics. He previously held Chairs in Economics at the University of Edinburgh (1998-2000) and in Statistics at the University of Kent (2000-2003). His research focuses on Bayesian statistics, including theoretical and applied work in distribution theory, Bayesian model averaging, spatial statistics, survival analysis, and stochastic volatility models. He has extensive editorial experience, serving as Editor-in-Chief of Bayesian Analysis (2022-2025) and as Head of the Statistics Department at Warwick (2014-2018). He is currently Chair-Elect of the Objective Bayes section of the International Society for Bayesian Analysis (ISBA). His research interests span Bayesian methodology, econometrics, and statistical theory, with applications to economics, survival analysis, and spatial data. He is affiliated with the Centre for Research in Statistical Methodology at Warwick and actively contributes to academic conferences. His work has been recognized through highly cited publications, including influential papers on Bayesian model averaging and non-Gaussian statistical models. Professional activities include editorial roles in top journals such as the Journal of Econometrics , Journal of the Royal Statistical Society , and Journal of Productivity Analysis . He collaborates with researchers globally and maintains an active profile on academic platforms like Google Scholar and ResearchGate.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

• Reactive flow and transport modeling
• Geochemical processes
• Groundwater dynamics
• +۵ مورد دیگر

Martin Andersen is an Associate Professor with the Water Research Laboratory and School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His research focuses on hydrogeology, groundwater dynamics, and surface water-groundwater interactions, with particular emphasis on coastal zone processes and reactive flow and transport modeling. Dr. Andersen's research interests span several critical areas in hydrogeology and environmental science. His work on reactive flow and transport modeling examines how chemical reactions affect water movement through geological formations. He investigates geochemical processes and groundwater dynamics in coastal zones, which is crucial for understanding saltwater intrusion and managing coastal aquifers. His research on surface water-groundwater interactions helps inform sustainable water resource management, particularly in Australia's diverse hydrological environments. Andersen has made significant contributions to understanding how climate variability affects groundwater recharge processes and the implications for water security in arid and semi-arid regions. Analysis of Dr. Andersen's recent publications reveals a strong focus on understanding complex hydrological systems through innovative methodologies. His work spans multiple disciplines including hydrogeology, climate science, biogeochemistry, and computational modeling. A key trend in his research is the investigation of groundwater dynamics in various environmental contexts - from arid zone aquifers to coastal wetlands and fractured rock systems. He employs diverse approaches including field monitoring networks, laboratory experiments, statistical modeling, and paleoclimate reconstructions to address pressing water resource challenges. His research has significant implications for sustainable groundwater management, climate adaptation strategies, and ecosystem conservation. Dr. Andersen is actively involved in several research teams and laboratories. As an Associate Director in the School of Civil and Environmental Engineering at UNSW, he contributes to the Water Research Laboratory's mission of advancing water science and engineering. His work often involves interdisciplinary collaborations across hydrology, geology, environmental science, and climate research. Through his leadership in establishing monitoring networks and conducting field experiments, he has helped build valuable infrastructure for ongoing water research in Australia.

Jan van den Brakelمشاهده پروفایل

استاد

• Survey Methodology
• Statistical Analysis
• Time Series Modeling
• +۷ مورد دیگر

Jan van den Brakel is an Extraordinary Professor of Survey Methodology at Maastricht University and a Senior Statistician at Statistics Netherlands. He holds a position in the Department of Quantitative Economics within the School of Business and Economics. His primary affiliation is with the methodology department at Statistics Netherlands, where he focuses on advancing statistical methods for official statistics. **Research Interests:** Professor van den Brakel specializes in survey methodology, with a focus on mixed-mode surveys, time series modeling, small area estimation, and discontinuity analysis. He explores topics such as sampling techniques, variance estimation, and the integration of non-probability data sources (e.g., social media) into official statistics. His work addresses challenges in labor force surveys, health surveys, and the impact of technological changes like tablet-based data collection. **Key Projects:** Current projects include mixed-frequency time series modeling for short-term statistics, accuracy assessments of non-probability web panels, and modeling discontinuities in official surveys. He has contributed to high-impact studies, such as analyzing mobility patterns during the pandemic and improving unemployment rate estimation using Bayesian models. **Awards:** His 2013 paper on factorial designs in probability samples was honored as the best journal paper by Survey Methodology in 2014. He is a member of the Advisory Committee on Statistical Methods at Statistics Canada, reflecting his international leadership in statistical methodology. **Professional Roles:** In addition to his academic role, he collaborates extensively with national statistical offices, advising on methodological challenges. His work bridges theory and practice, ensuring statistical methods are robust and applicable to real-world data collection and analysis.

Melvyn Weeksمشاهده پروفایل

مدرس ارشد

• Microeconometrics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Dr Melvyn Weeks serves as a University Senior Lecturer, Director of Teaching, and Director of Undergraduate Studies at the Faculty of Economics, University of Cambridge. His academic leadership spans curriculum development and student supervision within one of the world's leading economics departments. Specializing in Microeconometrics , Causal Inference , and Machine Learning , Weeks' research bridges advanced statistical methodology with real-world economic applications. His work prominently features in energy economics (analyzing consumer behavior in prepayment energy systems), development economics (examining microcredit impacts and religious institution effects in India), and economic growth theory (addressing model uncertainty in convergence clubs). Publication trends reveal a consistent integration of machine learning techniques with traditional econometric frameworks, particularly in causal tree estimation for heterogeneous treatment effects and robust model averaging approaches. His recent work demonstrates growing emphasis on machine learning applications in economics , causal inference methodologies , and distributional analysis across diverse economic contexts. As a doctoral supervisor, Weeks mentors PhD candidates including Christian Tien (focusing on causal inference and machine learning applications) and Cheuk Fai Ng (specializing in cluster-robust inference for high-dimensional regression). His teaching portfolio spans foundational statistical inference through advanced topics including machine learning in economics, causal inference, and research computing. Based at Clare College with office in Room 60, Weeks maintains active research collaboration through the Faculty of Economics' Econometrics Research Group, contributing to Cambridge's reputation in quantitative economic analysis.

Iason Papaioannouمشاهده پروفایل

استاد مدعو

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +۵ مورد دیگر

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

• Financial Mathematics
• Stochastic Processes
• Risk Management
• +۳ مورد دیگر

Prof. Dr. Francesca Biagini is a full Professor at the Department of Mathematics, University of Munich (LMU Munich) , leading the Stochastics and Financial Mathematics working group. She serves as Vice President for International Affairs and Diversity at LMU Munich since October 1, 2019, and as President of the Bachelier Finance Society (2022–2023). She is also a Correspondent of the Deutsche Aktuarvereinigung (DAV) and a member of the Executive Board of the Munich Risk and Insurance Center (MRIC) since 2017. Her research focuses on stochastic processes in financial markets , particularly asset price bubbles , default risk modeling , and robust hedging under model uncertainty. Recent work includes deep learning applications to bubble detection and non-linear affine processes for market dynamics. She actively contributes to academic leadership through teaching and publications, including 15+ recent articles on topics like liquidity-induced bubbles, machine learning calibration, and systemic risk transfer equilibrium. Her workgroup collaborates on quantLab initiatives and DAV certificate programs .

Mingyi Hongمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Mingyi Hong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , where he leads the OptimAI-Lab . His work bridges optimization theory , machine learning , and signal processing , with a focus on foundation models like LLMs and diffusion models. Education : Not explicitly mentioned in the text Current Projects : NSF grants on bilevel optimization, LLM unlearning, and inverse reinforcement learning Research Themes : Bilevel Optimization : Applications in LLM alignment, unlearning, and wireless systems LLM Safety : Unlearning, alignment with human feedback, robustness Diffusion Models : Inference-time alignment, adversarial training Distributed Optimization : Privacy-preserving algorithms, federated learning Recent Publications highlight trends in LLM unlearning (BLUR, LUME), optimization theory (Barrier Functions, νSAM), and diffusion models (Direct Noise Optimization). His group has secured NSF , AWS , Cisco , and Open Philanthropy grants. Scientific Recognition : IEEE Fellow (2025) SPS Best Paper Award (2022, 2021, 2018) Doctoral Dissertation Fellowship (2024) IBM Pat Goldberg Memorial Award (2022) He mentors PhD students like Siliang Zeng and Xinwei Zhang , and collaborates with institutions including Michigan State University , Amazon , and NIH on projects spanning UHF MRI technology to climate-smart agriculture .

Martin Hebartمشاهده پروفایل

استاد

• Visual Perception
• Computational Cognitive Neuroscience
• Quantitative Psychiatry
• +۵ مورد دیگر

Martin Hebart is a Professor for Computational Cognitive Neuroscience and Quantitative Psychiatry at Justus Liebig University Giessen and an Independent Max Planck Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His work bridges cognitive neuroscience, computer science, and psychology to explore visual perception, object recognition, and computational models of brain function. PhD in Psychology from Bernstein Center for Computational Neuroscience Berlin (2014) M.Sc. and B.Sc. in Neuro-cognitive Psychology from Ludwig Maximilian University Munich His research integrates psychophysics , neuroimaging (fMRI, MEG), and machine learning to decode how visual input transforms into stable object representations and how these insights inform psychiatric conditions like hallucinations. Articles highlight his focus on computational models , neural network alignment , and large-scale behavioral-neuroimaging datasets (e.g., THINGS-data). His group’s work spans from basic visual cognition to translational applications in psychiatry. Scientific awards include postdoctoral fellowships from the National Institute of Mental Health (2016) and Alexander von Humboldt Foundation (Feodor Lynen, 2016), alongside doctoral and study scholarships. He leads a multidisciplinary team at the intersection of JLU Giessen’s Medical Department and MPI, mentoring students in visual neuroscience , AI-driven modeling , and clinical applications .

Kaiming Biمشاهده پروفایل

استادیار

• Infectious Disease Modeling
• Epidemic Forecasting
• Public Health Decision-Making
• +۱۲ مورد دیگر

Kaiming Bi, Ph.D., is an Assistant Professor in the Department of Management, Policy & Community Health at the University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health . As an affiliated member of the Center for Health Care Data , his research bridges quantitative methods and public health, focusing on infectious disease modeling , epidemic forecasting , and data-driven health policy . B.S. in Mathematics from Northeastern University (2015) Ph.D. in Industrial Engineering from Kansas State University (2020) Postdoctoral training at University of California San Diego School of Medicine (2020-2021) and University of Texas at Austin (2021-2024) His methodological expertise spans mathematical modeling , machine learning , and optimization , applied to diverse public health challenges including respiratory infections , vector-borne diseases , STIs , and the opioid epidemic . Recent work includes modeling SARS-CoV-2 Omicron subvariants , population immunity dynamics , and multi-pathogen burden projections for the 2023-2024 US winter season. Dr. Bi has received prestigious accolades such as the Pencis Best Researcher Award (2021) and IISE Best Paper (2018). He previously taught graduate courses in Integer Programming , Information Systems , and Industrial Simulation at Kansas State University. Currently leading the Big-data and Infectious Disease Modeling Lab (BI Lab) , he seeks STEM-motivated PhD students to develop computational solutions for epidemic control.

Adam J Rothmanمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Covariance Estimation
• Multivariate Analysis
• +۲ مورد دیگر

Adam J Rothman is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities campus, specializing in high-dimensional statistical methodologies. His research focuses on covariance estimation, multivariate analysis, and developing innovative regression frameworks for complex data structures. His primary research interests include High-Dimensional Statistics, Covariance Estimation, Multivariate Analysis, and Statistical Machine Learning. Rothman develops penalized likelihood methods and shrinkage estimators to address challenges in matrix-valued predictors, categorical responses, and large covariance matrices, with applications spanning scientific domains requiring scalable high-dimensional analysis. Rothman's recent publications (2019-2024) demonstrate consistent innovation in high-dimensional regression and classification. Key trends include covariance matrix regularization, sufficient dimension reduction techniques, and likelihood-based approaches for categorical multivariate responses. His work emphasizes computational efficiency and theoretical guarantees for datasets where variables exceed sample sizes. He has secured major National Science Foundation funding as Principal Investigator for two projects: Sufficient Dimension Reduction of High-Dimensional Data (2011-2015) and New methods for multivariate analysis in high dimensions (2015-2021). These grants supported foundational work in dimension reduction and covariance estimation, advancing methodologies for modern statistical challenges.

Tanushree Royمشاهده پروفایل

استادیار

• Cybersecurity
• Fault diagnostics
• Socio-technical systems
• +۴ مورد دیگر

Dr. Tanushree Roy serves as an Assistant Professor in the Department of Mechanical Engineering at Texas Tech University's Whitacre College of Engineering and is an Affiliate Faculty member at the National Wind Institute. Her research pioneers resilient human-centric smart city infrastructures through the integration of control theory, mathematical modeling, and machine learning to address critical challenges in safety, security, and resource optimization for urban systems. Her academic foundation includes: Ph.D. in Mechanical Engineering from The Pennsylvania State University (2022) M.S. in Mathematics from University of Central Florida (2015) M.E. in Electrical Engineering from Indian Institutes of Engineering Science and Technology, India (2011) B.Tech in Applied Electronics and Instrumentation from Maulana Abul Kalam Azad University of Technology, India (2009) Dr. Roy's research centers on cybersecurity , fault diagnostics , and socio-technical systems with specialized applications in smart transportation networks and battery energy storage systems. She develops innovative frameworks that merge human-centric sensing with technical measurements to combat cyberattacks and physical faults in cyber-physical-social systems, emphasizing safety-critical resilience for urban citizens. Her methodology uniquely combines model-based control with data-driven machine learning to address challenges like social data integrity, human behavior modeling, and multi-scale anomaly characterization. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in cyberattack detection for connected vehicles, thermal fault tolerance in battery systems, and socio-technical traffic modeling. Key technical approaches include Koopman operator theory for secure estimation, control barrier functions for safety certification, and redundancy-based data fusion techniques. These works consistently bridge theoretical control systems with practical smart city implementation, demonstrating strong interdisciplinary connections between transportation engineering, energy systems, and cybersecurity. No scientific awards are documented in the provided information. Dr. Roy actively mentors three PhD students—Sanchita Ghosh (since 2022), Faysal Ahamed, and Soumyoraj Mallick (both since 2024)—alongside undergraduate researcher Mercedes Hernandez. Her research is executed through the Smart Human-centric Automation Resilience (SHARE) Lab, which has secured projects including the secure autonomous mobility testbed and participates in workforce development via Texas Tech's Engineering Research Internship Experience (ERIE) program for high school students. The SHARE Lab operates at the intersection of transportation and energy systems, maintaining two primary research thrusts: resilient human-centric transportation networks and safeguarding battery energy storage infrastructure. Current projects include SUMO-based cyberattack validation for connected vehicle platoons, self-learning voltage estimation under sensor attacks, and thermal fault-tolerant battery management. The lab maintains active collaborations with national conferences (ACC, CCTA) and industry partners to advance real-world implementation of resilient smart city technologies.

Georg Gottwaldمشاهده پروفایل

استاد

• Dynamical Systems
• Geophysical Fluid Dynamics
• Climate Modeling
• +۵ مورد دیگر

Professor Georg Gottwald is a distinguished academic in the School of Mathematics and Statistics at the University of Sydney, where he has been a faculty member since 2002, progressing from Lecturer to his current position as Professor since 2013. He also holds a Visiting Professor position at the University of Surrey in the UK since 2013. His extensive research career spans dynamical systems theory, geophysical fluid dynamics, and the intersection of machine learning with complex systems. Professor Gottwald's research focuses on dynamical systems theory as an abstract formalism for studying systems evolving in time and space. His work has significant applications across diverse fields including climate modeling, biological systems, and complex networks. He is particularly known for developing methods for model reduction of complex dynamical systems, stochastic modeling approaches, and the application of machine learning techniques to dynamical systems. His research aligns with the Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Climate and Environmental Change, Data and Decisions, and National Security. His most recent publications demonstrate a strong trajectory toward integrating machine learning with dynamical systems theory, particularly in developing stable generative models, learning dynamical systems with random feature maps, and combining data assimilation with machine learning for forecasting. His work spans pure mathematical theory to practical applications in climate science, finance, and biological systems, showing remarkable breadth while maintaining deep mathematical rigor. Future Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2014 Australian Research Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2015 (declined) Australian Research Fellowship, 'Geometric methods in geophysical fluid dynamics', Australian Research Council, 2004-2009 Professor Gottwald has successfully supervised numerous PhD and Master's students who have gone on to academic and industry positions worldwide. His current research group includes postdocs and PhD students working on machine learning for dynamical systems, stochastic model reduction, physics-informed machine intelligence, and tensor methods for scientific machine learning. He has secured multiple ARC Discovery Project grants and has been involved in significant international collaborative research projects. He is actively involved with the Sydney Dynamics Group, which he co-founded in 2007, fostering collaboration between the University of Sydney and UNSW. Professor Gottwald maintains strong editorial commitments as Associate Editor for Geophysical and Astrophysical Fluid Dynamics, SIAM Journal of Applied Dynamical Systems, and Journal of Computational Dynamics, and serves on the Editorial Advisory Board for Chaos and the Editorial Board for Physical Review E. His professional activities demonstrate leadership in the dynamical systems community through organizing workshops, seminars, and special journal issues.

Jared Tannerمشاهده پروفایل

استاد

• Compressed Sensing
• Matrix Completion
• Signal and Image Processing
• +۷ مورد دیگر

Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.