Find Academic Supervisors

• Structural Biology
• Crystallography
• Enzyme Dynamics
• +5 more

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 ShaliziView profile

Associate Professor

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +15 more

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 PattanayakView profile

Senior Lecturer

• Causal Inference
• Statistics Education
• Applied Statistics
• +5 more

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) WangView profile

Associate Professor

• Statistics
• Data Science
• U-Statistics
• +10 more

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 KonietschkeView profile

Professor

• Medical biometrics
• Resampling methods
• Nonparametric statistics
• +4 more

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
• +5 more

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 RuizView profile

Professor

• Data Mining
• Machine Learning
• Bioinformatics
• +4 more

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 OjedaView profile

Associate Professor

• Machine Learning
• Multilabel Classification
• Autoencoders
• +6 more

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 WoodView profile

Professor

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +5 more

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
• +3 more

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.

Matúš MaciakView profile

Associate Professor

• nonparametric regression
• change-points
• structural breaks
• +5 more

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8

Nezamoddini-Kachouie NezamoddinView profile

Associate Professor

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +7 more

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

Kees VuikView profile

Professor

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +4 more

Kees Vuik is a Professor at the Delft Institute of Applied Mathematics within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on developing advanced numerical methods for complex engineering and scientific simulations across multiple domains including energy systems, environmental engineering, and computational physics. His research interests center on Numerical Analysis and Scientific Computing, with specialized expertise in Computational Fluid Dynamics, Reservoir Simulation, and Wave Propagation. He develops innovative preconditioning techniques for large-scale linear systems and creates high-fidelity models for subsurface flow, flood prediction, and energy conversion systems. Recent work integrates graph-based frameworks for electrolyzer modeling and advanced triangulation methods for point cloud processing. Analysis of his 2025 publications reveals a strong interdisciplinary focus on sustainable energy solutions (hydrogen/CO2 storage), environmental resilience (flood modeling), and computational efficiency (parallel preconditioning). His work consistently bridges theoretical numerical methods with practical engineering applications, particularly in energy transition technologies and climate-related simulations. Professor Vuik's scientific contributions have earned significant recognition: Officer of the Order of Orange-Nassau (2021) Professor of Excellence award (Leermeesterprijs) from TU Delft (2022) He has supervised 49 students across bachelor, master, and PhD levels, with notable successes including undergraduate research published in leading medical journals. His collaborative network spans 21 documented activities including industrial partnerships with Fujitsu Laboratories and leadership in academic societies, indicating substantial research funding and mentorship impact. As chair of the Bataafsch Genootschap der Proefondervindelijke Wijsbegeerte (2023-2024) and active member of the Delft Institute of Applied Mathematics, he contributes to both foundational mathematical research and real-world applications through international collaborations and industrial knowledge exchange.

Caroline FossatiView profile

Professor

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +2 more

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

• Actuarial Science
• Stochastic Processes
• Mathematical Finance
• +5 more

Xiaoming Liu is an Associate Professor in the Department of Statistical and Actuarial Sciences at Western University's Faculty of Social Science. His research focuses on actuarial science, stochastic processes, and mathematical finance, particularly in mortality modeling and interest rate risk analysis. Ph.D., University of Toronto (2008) Research interests include stochastic mortality modeling, regime-switching dynamics, and guaranteed annuity option pricing. His work integrates comonotonicity theory, Markov processes, and probability measure transformations to address correlated financial and demographic risks. Notable awards include the Associateship of the Society of Actuaries (ASA, 2016). He has supervised graduate students in actuarial research and published extensively in top-tier journals like Insurance: Mathematics and Economics and Stochastics.