جست‌وجوی استاد و پژوهشگر

Rameshwar Pratapمشاهده پروفایل

دانشیار

• Algorithms in Machine Learning
• Theoretical Computer Science
• Sketching and Dimensionality Reduction
• +۵ مورد دیگر

Rameshwar Pratap is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Previously, he served as an Assistant Professor at the School of Computing and Electrical Engineering, IIT Mandi for three years. Education: Ph.D. in Theoretical Computer Science, Chennai Mathematical Institute Research Interests: His research lies at the intersection of theory and practice, focusing on extremely simple yet practical approximation algorithms with provable guarantees . Key themes include: Sketching and dimensionality reduction algorithms for tensors and similarity measures Improving speed, scalability, and accuracy of existing sketching methods Applications in machine learning: node embedding in large-scale networks, itemset mining, model compression He extensively employs techniques from matrix and tensor algebra, sampling, random projection, and randomized hashing . Publications Overview: Across 2021–2025 his work has appeared in top venues such as IEEE Globecom, Theoretical Computer Science, Acta Informatica, Information Processing Letters, Algorithmica, UAI, ICALP, Machine Learning, TKDE, and ACML. Recurring themes are randomized sketching, locality-sensitive hashing, compressed matrix multiplication, variance reduction, and subspace approximation , demonstrating both theoretical depth and practical impact. Awards & Honors: Early Career Research Grant (PM-ECRG) 2025, Anusandhan National Research Foundation (ANRF) Best Paper Award, COCOON 2020 Students & Funding: First Ph.D. student: Bhisham Dev Verma (co-advised with Prof. Manoj Thakur) graduated June 2025 MS by Research student: Punit Pankaj Dubey graduated October 2022 Currently hiring 1 Junior Research Fellow for the PM-ECRG project “Improving Similarity Search in Practice” Labs & Teams: Works within the Algorithms & Theory group at IIT Hyderabad, collaborating with national and international researchers. Erdös number is 3.

Bo Huمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Neurosurgery
• +۷ مورد دیگر

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.

• Biostatistics
• Cancer Genomics
• Statistical Methods for High-Dimensional Data
• +۴ مورد دیگر

Yuan Huang is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health . Her research focuses on statistical methods for high-dimensional data, motivated by challenges in cancer genomics and neurodegenerative diseases. She develops approaches for biomarker identification, network structure estimation, and gene-environment interaction analysis, with applications in Alzheimer’s, Huntington’s, and Parkinson’s diseases. Key Affiliations: Yale Cancer Center, Center for Brain & Mind Health, Yale Center for Analytical Sciences (YCAS) Her methodological work emphasizes integrative analysis across multiple datasets to improve reproducibility and discovery. Recent collaborations span clinical trials, genetics, and epidemiology, with a focus on addressing heterogeneity and nonlinearity in complex biomedical data. Her publications include Bayesian finite mixture models, precision matrix estimation, and advanced techniques for high-dimensional causal mediation analysis. She actively engages in translational research, linking statistical innovation to clinical and public health challenges.

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.

Laurent Bellaicheمشاهده پروفایل

استاد

• Computational Condensed Matter Physics
• Ferroelectrics
• Multiferroics
• +۷ مورد دیگر

Laurent Bellaiche is a Distinguished Professor in the Department of Physics within the College of Arts and Sciences at the University of Arkansas. His research focuses on computational condensed matter physics with emphasis on ferroelectrics, multiferroics, and semiconductor materials. He leads the Computational Condensed Matter Physics (CCMP) Group and serves as a founding member of the Smart Ferroic Materials Center. His primary research interests include: Developing first-principles methods for predicting properties of ferroelectrics and multiferroics Investigating topological defects, spin liquids, and magnetic skyrmions Studying non-equilibrium effects for neuromorphic computing applications Optimizing electro-optic, electrocaloric, and piezoelectric effects Designing antiferroelectrics for high-energy-density applications Professor Bellaiche's recent publications (2024-2025) demonstrate significant activity in topological polar structures, skyrmion engineering, strain-induced phenomena, and computational design of functional materials. His work shows strong interdisciplinary connections between condensed matter theory, materials science, and device physics with particular emphasis on emergent topological phenomena in low-dimensional systems. Scientific awards include: Twenty-First Century Professorship in Nanotechnology and Science Education NSF CAREER Awardee Bellaiche maintains active collaborations with experimental groups internationally, particularly with CentraleSupélec in France. He is involved in innovative educational initiatives including a course titled "Thinking Outside the Box: Physics, Soccer and much more" and contributes to the Soccernostalgia podcast. His research group emphasizes both fundamental theoretical advances and practical applications in next-generation electronic and energy materials.

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +۳ مورد دیگر

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

• High-dimensional probability
• Randomized algorithms
• Numerical linear algebra
• +۴ مورد دیگر

Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin) Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021) Her research focuses on randomized numerical linear algebra , mathematics of data science , and high-dimensional probability . Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints. Recent publications span topics in randomized NLA , robust solvers , tensor methods , and nonnegative matrix factorization . Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery. She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.

Yao Liمشاهده پروفایل

دانشیار

• Applied Probability
• Applied Dynamical Systems
• Numerical Analysis
• +۴ مورد دیگر

Yao Li is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Mathematics from Georgia Institute of Technology (2012) and previously served as a Courant Instructor at New York University’s Courant Institute. His research focuses on applied probability, dynamical systems, numerical analysis, and their intersections with machine learning, neuroscience, and statistical mechanics. He has contributed to understanding stochastic dynamics, invariant measures, and the mathematical foundations of machine learning. His work bridges theoretical mathematics with interdisciplinary applications in biology, physics, and engineering. Education : Ph.D. in Mathematics, Georgia Institute of Technology, 2012 Courant Instructor, Courant Institute of Mathematical Sciences, New York University Research Interests : Dr. Li’s research spans applied probability, dynamical systems, and numerical analysis. Key areas include: Machine learning and data-driven computational methods Neuroscience modeling (spiking neural networks, cortical processing) Nonequilibrium statistical mechanics (thermodynamic laws, energy exchange models) Stochastic processes and their ergodic properties His work emphasizes rigorous mathematical analysis combined with numerical simulations, addressing challenges in high-dimensional stochastic systems. Teaching & Advising : He teaches advanced courses such as Numerical Analysis, Applied Math Project Seminar, and Statistics. While specific student advisees are not listed, his research group likely engages graduate students in interdisciplinary projects. His courses integrate theoretical concepts with real-world applications, reflecting his research focus on computational and applied methods. Labs/Teams : Dr. Li’s research is conducted within the Department of Mathematics and Statistics at UMass Amherst, collaborating with institutions like the Courant Institute and Georgia Tech. His work often involves interdisciplinary teams addressing problems at the intersection of mathematics, physics, and biology.

Ali Yousefiمشاهده پروفایل

دانشیار

• Neuroscience Data Analysis
• Biomedical Engineering
• Brain-Computer Interfaces (BCI)
• +۶ مورد دیگر

Dr. Ali Yousefi is an Associate Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. His research focuses on developing statistical and computational methods for analyzing neuroscience data, particularly in linking neural activity to biological/behavioral signals. Key areas include model identification, Bayesian analysis, and real-time neural decoding for applications like brain-computer interfaces and closed-loop stimulation systems. Education: B.S. (Electrical Engineering, Iran University of Science & Technology, 1998), M.S. (Electrical Engineering, Sharif University of Technology, 2000), Ph.D. (Electrical Engineering, University of Southern California, 2014). Postdoctoral training at Harvard Medical School (2019) and Boston University (2019). Research Interests: Neural data analysis frameworks Dynamic neural ensemble modeling Closed-loop brain stimulation systems Bayesian statistical inference High-dimensional data decoding Labs/Teams: Principal Investigator of Yousefilab, focused on neurotechnology and BCI development. Active in interdisciplinary collaborations combining engineering, neuroscience, and machine learning. Key Contributions: Developed methodologies for neural signal decoding, including Bayesian Gaussian process models and latent variable techniques. Pioneered real-time cognitive state prediction and closed-loop systems for enhancing cognitive control in humans.

• Nanoscale Transport Phenomena
• Fluid Mechanics
• High-Performance Computing
• +۵ مورد دیگر

Dr. Gerald Wang is an Assistant Professor in Civil and Environmental Engineering at Carnegie Mellon University with courtesy appointments in Chemical Engineering and Mechanical Engineering. He leads the M5 Lab (Mechanics of Materials via Molecular and Multiscale Methods), focusing on nanoscale mechanics using computational approaches to solve civil engineering challenges related to water-energy systems, material resilience, and sustainable polymers. Education: Ph.D. in Mechanical Engineering and Computation, MIT (2019) S.M. in Mechanical Engineering, MIT (2015) B.S. in Mechanical Engineering, Mathematics & Physics, Yale University (2013) His research integrates statistical physics, fluid mechanics, and high-performance computing to investigate nanoscale structural and transport phenomena. Key areas include climate-resilient infrastructure, energy-water nexus solutions, and nanoscale thermal transport in materials. Recent work explores molecular-scale separation processes and recyclable polymer design using advanced simulation techniques. Publication trends demonstrate consistent focus on nanoscale transport mechanisms, computational method development, and interdisciplinary applications from materials science to urban systems. Articles frequently bridge molecular dynamics with macro-scale engineering problems. Awards: Scott Institute Seed Grant for clean energy research CMU Celebration of Education Award for teaching excellence Current projects include NSF-funded work on nanoscale slip phenomena and polymer upcycling collaborations. The M5 Lab develops open-source simulation tools and maintains active industry partnerships in advanced materials.

Rafał Weronمشاهده پروفایل

استاد

• Forecasting
• Agent-based modeling
• Computational economics
• +۴ مورد دیگر

Rafał Weron is a Full Professor at Wrocław University of Science and Technology, where he has held leadership roles since 2015, including Head of the Department of Operations Research and Business Intelligence and Chairman of the Scientific Discipline Council for Management and Quality Sciences. His expertise spans electricity price forecasting, computational economics, and risk management, with significant contributions to probabilistic forecasting methods. As a globally recognized scholar, he has received prestigious awards such as the Hugo Steinhaus Prize (2018) and the Tao Hong Award (2017). Key Affiliations : Wrocław University of Science and Technology; Polish Academy of Sciences (Statistics and Econometrics Committee); Polish Mathematical Society. Research Trends: Weron's work focuses on electricity price forecasting, leveraging machine learning and statistical models to enhance accuracy and reliability. His publications emphasize probabilistic forecasting frameworks, quantile regression, and hybrid modeling techniques, reflecting a commitment to methodological rigor and practical applications in energy markets. Scientific Awards: Top 1% globally ranked economist (IDEAS/RePEc, 2013-2022) World's Top 2% Most Widely Cited Scientist (2019-2021) 'Hugo Steinhaus' Prize (2018) Tao Hong Award (2017) Emerald Citation of Excellence (2017) Minister of Science & Higher Education Prize (2016) Commission of National Education Medal (2016)

Ayesha Aliمشاهده پروفایل

استاد

• Graphical Markov models
• Pollination networks
• Multivariate statistics
• +۷ مورد دیگر

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

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.

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.