جست‌وجوی استاد و پژوهشگر

Bo Zhangمشاهده پروفایل

دانشیار

• Seismic data processing
• Seismic interpretation
• Machine learning
• +۳ مورد دیگر

Bo Zhang is an Associate Professor in the Department of Geological Sciences at the University of Alabama. He holds a PhD in Geophysics from the University of Oklahoma (2014), an MS from the Institute of Geology and Geophysics, Chinese Academy of Sciences (2009), and a BS from China University of Petroleum (2006). His research focuses on seismic data processing, interpretation, and machine learning applications in geophysics. Key areas include fault attribute estimation, seismic noise reduction, and reservoir characterization through integrated multidiscipline data analysis. He is affiliated with SEG and AAPG. Recent work emphasizes machine learning-driven solutions for seismic challenges, such as horizon extraction, first-arrival picking, and fault detection. His publications span high-impact journals like Geophysics , Interpretation , and IEEE Transactions . Collaborations include projects on shale brittleness evaluation, microseismic event analysis, and 3D seismic attribute integration. Ongoing research explores deep learning applications in groundwater modeling and self-supervised denoising techniques. Bo Zhang’s contributions bridge traditional seismic methods with modern AI tools, advancing exploration geology and resource evaluation. His workflow innovations, such as semi-automated horizon interpretation and VMD-based decomposition, enhance data fidelity and interpretation efficiency.

Hui Jiangمشاهده پروفایل

استاد

• Statistical Genomics
• Bioinformatics
• Computational Statistics
• +۳ مورد دیگر

Hui Jiang is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He holds a PhD in Computational and Mathematical Engineering from Stanford University (2009) and postdoctoral experience in statistical genomics at Stanford's Department of Statistics and Genome Technology Center. His research focuses on computational statistics, statistical genomics, and bioinformatics, with specialties in RNA-Seq data analysis, cancer genomics, and algorithmic development for resampling-based hypothesis testing. Dr. Jiang's work emphasizes developing statistical methods for analyzing large-scale genomic data, including asymmetric data integration, penalized modeling, and efficient algorithms for p-value estimation. His contributions include software tools for RNA-Seq analysis (e.g., rSeqNP, rSeqDiff) and collaborative frameworks for biomedical data interpretation. He teaches courses on computing with big data (BIOSTAT625) and biostatistical fundamentals (EPID701). Key research directions include statistical methods for cancer biomarker identification, high-dimensional data analysis, and optimization algorithms. His work bridges computational innovation with clinical applications, addressing challenges in precision health and precision oncology.

Andrew J. Blumbergمشاهده پروفایل

استاد

• Cancer Evolution Data Science
• Single Cell Genomics
• Tumor Heterogeneity
• +۳ مورد دیگر

Andrew J. Blumberg is a Herbert and Florence Irving Professor of Cancer Data Research at the Herbert and Florence Irving Institute of Cancer Dynamics and the Herbert Irving Comprehensive Cancer Center, as well as a Professor of Mathematics and Computer Science at Columbia University. Previously, he held positions at the University of Texas and was an NSF postdoctoral fellow at Stanford University. He received his PhD in 2005 from the University of Chicago under the supervision of Peter May and Michael Mandell. His research spans algebraic topology, topological data analysis, and computer security and privacy. Key areas include cancer evolution data science, single-cell genomics, and tumor heterogeneity. He contributes to applications of topology in genomics and collaborates on projects like the Pepper initiative at NYU focused on verifiable computing. Blumberg serves as an editor for journals including the Journal of Topology and Advances in Mathematics . He is affiliated with Columbia's Center for the Topology of Cancer Evolution and Heterogeneity. His work bridges theoretical mathematics with applied fields, addressing challenges in genomics and computational security.

• Machine Learning
• Neuroscience
• Healthcare Informatics
• +۲ مورد دیگر

Marcia Saul is a Postgraduate Researcher at Bournemouth University with research interests spanning Machine Learning , Neuroscience , and Healthcare Informatics . Her work focuses on applying computational methods to biomedical problems. Active in Neurofeedback and Brain-Computer Interface research Published in Frontiers in Psychology and Health Informatics Journal Utilizes Interbrain Synchrony and Neural Network approaches Her recent publications demonstrate expertise in Resampling Techniques , Pattern Classification , and Medical Data Analysis . She has attended training in BCIA Neurofeedback and BR41N.IO Hackathon .

• Structural Biology
• Crystallography
• Enzyme Dynamics
• +۵ مورد دیگر

Gergely Katona is a Professor at the Department of Chemistry & Molecular Biology, University of Gothenburg. His research focuses on structural biology, particularly crystallographic investigations of protein dynamics, and he integrates artificial intelligence (AI) into biophysical data analysis. Chairman of the Chemistry Program Committee Main examiner for Chemistry programs Teaches courses like KEM350 and KEM360 Katona's research explores enzyme mechanisms using X-ray techniques and AI, with applications in cancer biology and autoimmune diseases. He pioneered methods to study reacting enzymes via chemical triggering and Bayesian analysis of MST/BLI/NMR data. His recent work combines structural biology with machine learning to predict protein interactions and mutation fitness, as seen in his Survivin studies. He holds a patent for peptide-targeting technology. Katona supervises doctoral, master, and bachelor students, emphasizing method development and independent research skills. He reviews applications for EMBL beamlines and the iNEXT network.

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Cassandra Pattanayakمشاهده پروفایل

مدرس ارشد

• Causal Inference
• Statistics Education
• Applied Statistics
• +۵ مورد دیگر

Cassandra Pattanayak serves as the Jack and Sandra Polk Guthman '65 Director of the Quantitative Analysis Institute and Senior Lecturer in Quantitative Reasoning and Mathematics at Wellesley College, affiliated with the Department of Quantitative Reasoning Data Science Mathematics & Statistics where she spearheads campus-wide statistical literacy initiatives. Her academic foundation includes: B.A. from Harvard University M.A. from Harvard University Ph.D. from Harvard University A leading methodologist in causal inference, Dr. Pattanayak develops rigorous tools for non-randomized studies and randomized experiments—including covariate balance diagnostics, chance imbalance corrections, and Bayesian propensity score analysis—with impactful applications in legal aid evaluation and in vitro fertilization protocols. Her dual commitment to advancing statistical methodology and pedagogy drives her mission to equip students with interdisciplinary quantitative skills through foundational concept mastery and practical research application. As Institute Director, she provides statistical consulting to cross-disciplinary researchers, coordinates faculty/student workshops, and teaches the Quantitative Analysis Institute Summer Course and a First Year Seminar on causal inference. Her STAT221 Statistical Inference course covers theoretical frameworks including maximum likelihood estimation, sufficiency, Bayesian techniques, and resampling methods, directly supporting student research across Wellesley's academic spectrum.

Qing (Wendy) Wangمشاهده پروفایل

دانشیار

• Statistics
• Data Science
• U-Statistics
• +۱۰ مورد دیگر

Qing (Wendy) Wang serves as Associate Professor of Mathematics in the Department of Mathematics & Statistics at Wellesley College, where she advises the data science major and teaches statistics courses including Introductory Statistics, Multivariate Data Analysis, and the Capstone in Data Science. Previously, she held tenure-track positions at Williams College (2012-2015) and Bentley University (2015-2016). Her academic journey began with a B.S. from Beijing Normal University, followed by graduate studies at Pennsylvania State University under the supervision of the late Dr. Bruce G. Lindsay. B.S., Beijing Normal University M.S., Pennsylvania State University Ph.D., Pennsylvania State University Professor Wang's research specializes in U-statistics, nonparametric kernel density estimation, risk and variance estimation, cross-validation, and resampling methodologies. Recent interests extend to penalization methods, model assessment tools, mediation analysis, and jackknife empirical likelihood, focusing on developing robust statistical frameworks for complex data analysis. She is deeply committed to undergraduate education, incorporating active learning strategies and interactive tools to enhance statistical comprehension. As a data science major advisor, she guides students through capstone projects emphasizing ethical data handling, statistical rigor, and computational proficiency in Python/R. Professor Wang collaborates with Wellesley's Quantitative Analysis Institute (QAI), which provides statistical consulting services and employs student interns as statistical experts through an apprenticeship model, fostering interdisciplinary research skills.

Frank Konietschkeمشاهده پروفایل

استاد

• Medical biometrics
• Resampling methods
• Nonparametric statistics
• +۴ مورد دیگر

Prof. Dr. Frank Konietschke is Acting Director of the Institute of Biometry and Clinical Epidemiology at Charité – University Medicine Berlin and the Berlin Institute of Health (BIH) . He leads the Working Group Statistical Methods of Translation and Early Clinical Trials . Professor for Statistical Methods of Translation and Early Clinical Trials since 2019 Previous Tenure Track Professor at University of Texas at Dallas (2014-2018) Holds doctorate in Mathematics from Georg-August University Göttingen (2009) Research Expertise: Specializes in nonparametric statistical methods with focus on: Resampling techniques and ranking methods for factorial designs Diagnostic studies and longitudinal data analysis High-dimensional data and statistical translation Applications in cardiology, neurology, and radiology Scientific Contributions: • 121 publications including methodological advances in: Nonparametric Behrens-Fisher problem Group sequential designs Multiple contrast tests under heteroscedasticity • Developed R packages for statistical analysis (rankFD, nparLD) Awards and Recognition: Betty and Gifford Johnson Award (2017) Julia Williams van Ness Award (2017) Gustav-Adolf Lienert Award (2012)

• Applied Physics
• Thermodynamics
• Energy efficiency
• +۵ مورد دیگر

Gilles Courret is an Associate Professor at the School of Engineering and Management of the Canton of Vaud (HEIG-VD) in Yverdon-les-Bains, Switzerland. His primary areas of expertise include Applied Physics, Thermodynamics, Energy Efficiency, Plasma Technologies, Plasma Chemistry, and Lighting Design. With a career spanning over two decades, Professor Courret has made significant contributions to the fields of plasma physics, energy storage, and sustainable building technologies, particularly focusing on environmentally friendly solutions. Professor Courret's research interests cover several critical areas: Plasma physics and applications, especially atmospheric plasma for disinfection and lighting technologies Energy efficiency and storage solutions, including innovative approaches to storing renewable energy Thermodynamic systems and magnetocaloric refrigeration Daylighting systems and sustainable building technologies Data compression algorithms for efficient information storage His recent work has increasingly focused on practical applications of plasma technology for public health, particularly in response to the COVID-19 pandemic. Courret has developed atmospheric plasma sources as alternatives to traditional hand disinfection methods, investigating both radio frequency and direct current plasma approaches. His research demonstrates that cold atmospheric plasma can achieve significant disinfection on various surfaces while being safe for skin application when properly controlled. The technology uses only electricity and air, potentially replacing or complementing current hand disinfection methods and mitigating economic burdens during public health crises. Throughout his career, Courret has maintained a strong focus on environmentally friendly technologies. Early in his career, he worked on innovative daylighting systems based on non-imaging optics (anidolic systems) to improve natural lighting in buildings. More recently, he has explored energy storage solutions that avoid the environmental impacts of traditional batteries, developing pneumatic storage systems with 60-70% efficiency. His work on translucent honeycomb solar collectors demonstrates his commitment to integrating renewable energy solutions into building design.

Salvador Aguilar Ruizمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Salvador Aguilar Ruiz is a Full Professor of Data Analytics at Pablo de Olavide University's School of Engineering, where he teaches courses including Data Mining, Big Data, and IT Management in both undergraduate and postgraduate programs. His academic position is within the Department of Sports and Computer Science, where he maintains an active research profile with over 250 publications and more than 105,000 reads on ResearchGate. With over two decades of experience, Professor Aguilar Ruiz specializes in Data Mining and Machine Learning, with significant contributions to biclustering algorithms, protein structure prediction, and genomic data analysis. His research bridges computer science with bioinformatics, developing methods for analyzing complex biological data while advancing fundamental machine learning techniques for high-dimensional datasets. His recent work focuses on addressing challenges in imbalanced multiclass data classification, explainable AI, and novel evaluation metrics for machine learning models. The professor has developed several innovative approaches including the IMCP Curve for performance visualization and class-specific feature selection techniques that enhance model interpretability while maintaining predictive power. Outstanding Ph. D. Award, Computer Science, University of Seville Founding Editor-in-Chief of Biodata Mining Journal (2008-2014) Professor Aguilar Ruiz previously served as Founding Dean of the School of Engineering at Pablo de Olavide University from 2005 to 2015 and has held research positions at the University of Massachusetts Boston. His international collaborations span institutions in Japan (National Institute of Genetics) and Italy (University of Bologna), reflecting his global research impact in data analytics and bioinformatics.

Francisco Charte Ojedaمشاهده پروفایل

دانشیار

• Machine Learning
• Multilabel Classification
• Autoencoders
• +۶ مورد دیگر

Francisco Charte Ojeda is a Titular de Universidad (Associate Professor) at the University of Jaén, Spain, working in the Department of Computer Science within the Faculty of Experimental Sciences. He is affiliated with the Andalusian Inter-University Institute in Data Science and Computational Intelligence and leads research in the Intelligent Systems and Data Mining group. His academic journey includes earning a doctorate from the University of Granada with a thesis on hybrid flexible computing methods for multilabel classification. His research interests span machine learning with particular emphasis on multilabel classification, autoencoders, feature learning, time series forecasting, and dimensionality reduction. Professor Charte Ojeda has developed several R packages including mldr, mldr.datasets, ruta, and predtoolsTS that have become valuable tools in the machine learning community. His work often focuses on solving practical challenges in data science through innovative algorithmic approaches and software implementations. Analysis of his recent publications reveals a strong focus on autoencoder architectures, multilabel learning techniques, and time series forecasting methods. His research demonstrates a consistent pattern of developing practical software tools alongside theoretical contributions, bridging the gap between academic research and real-world applications. The trend shows increasing emphasis on explainable AI, ensemble methods, and efficient implementations for big data environments. Professor Charte Ojeda has been involved in multiple research projects funded by Spanish national grants (PID2019-107793GB-I00/AEI, TIN2015-68854-R). His collaborative network includes prominent researchers in the field such as Antonio J. Rivera, Francisco Herrera, and María J. del Jesús. He maintains an active presence in academic communities through his personal website (fcharte.com), GitHub repositories, and contributions to open-source software. His educational contributions include textbooks on programming languages, operating systems, and computational tools, reflecting his commitment to both research and teaching excellence.

Andrew Woodمشاهده پروفایل

استاد

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +۵ مورد دیگر

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

• High Energy Physics
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Ella Cole is a researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, focusing on High Energy Physics in her PhD studies. She joined DAMTP in October 2024 under the supervision of Prof. Maria Ubiali. Education: Integrated Master’s degree in Physics from Durham University Internship at DESY Zeuthen, contributing to experimental ATLAS group research Her research applies neural networks to analyze Parton Distribution Functions (PDFs), which are critical for high-energy proton collider calculations. She explores methods to enhance PDF accuracy and precision, potentially investigating Beyond Standard Model (BSM) physics via Effective Field Theory. Previously, she developed the 'Cell resampling' algorithm to improve Monte Carlo event generation efficiency and optimized migration matrix bin widths during her internship. She is affiliated with DAMTP’s Theoretical High Energy Particle Physics Group and is located in Room B1.15.

Nezamoddini-Kachouie Nezamoddinمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Nezamoddini-Kachouie Nezamoddin is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology , with an affiliate appointment in the Department of Electrical Engineering and Computer Science , both within the College of Engineering and Science . Since joining Florida Tech in 2012, he has built an interdisciplinary research program bridging engineering, medicine, and biology. Education & Training BASc – Electrical & Computer Engineering MASc – Systems Design Engineering, University of Waterloo, 2008 PhD – Biomedical Engineering Postdoctoral Fellow – Harvard Medical School & Harvard School of Public Health, 2010-2012 Research Interests Dr. Kachouie’s work sits at the confluence of statistical modeling , machine learning , artificial intelligence , image processing , pattern recognition , and biostatistics . He leverages these tools to tackle grand challenges in cancer research , public health , and climate change , producing actionable insights from complex, high-dimensional data. Publication Trends With more than 120 peer-reviewed papers spanning 2003-2025, his recent output emphasizes deep learning (U-Net, CNNs), Bayesian spatiotemporal models , and generalized additive models applied to glacier recession , wildlife population monitoring , sea-level rise , and cancer genomics . These works collectively advance both methodological innovation and high-impact societal applications. Student Mentorship & Grants He has mentored 80+ undergraduates who presented at venues such as NCUR, JSM, and AMS. Seven PhD students have graduated under his supervision and now hold academic positions. He has served as PI of the NSF REU Site “Statistical Models with Applications to Geoscience” (2020-2024) and as mentor for the NSF Biomath REU Site (2015-2017). Currently, his group comprises 2 post-docs, 6 PhD, 2 Master’s, and 7 undergraduate researchers. Contact & Resources Email: nezamoddin@fit.edu Office: Crawford 335 (inside 328) Phone: (321) 674-7485 Profiles: Google Scholar , Research Website