جست‌وجوی استاد و پژوهشگر

Marie-Christine Dükerمشاهده پروفایل

استادیار

• high-dimensional statistics
• time series analysis
• functional data analysis
• +۱۱ مورد دیگر

Marie-Christine Düker is an Assistant Professor in the Department of Statistics and Data Science at Friedrich-Alexander University (Germany). Her research focuses on high-dimensional statistics, time series analysis, functional data analysis, and extreme value theory with applications in economics, psychology, chemistry, and ecology. Previously, she was a postdoctoral associate at Cornell University's Department of Statistics and Data Science under David Matteson. She earned her PhD in Mathematics from Ruhr-University Bochum under Herold Dehling and spent part of her doctoral studies at the University of North Carolina at Chapel Hill with Vladas Pipiras. Current Position: Assistant Professor, Department of Statistics and Data Science, Friedrich-Alexander University Previous Academic Affiliation: Postdoctoral Associate, Cornell University Education: PhD in Mathematics, Ruhr-University Bochum; Part-time research at University of North Carolina Research Interests: Her work spans high-dimensional time series under long-range dependence and nonstationarity, discrete data modeling, nonlinear dynamics, dimension reduction, and change-point analysis. Applications include econometrics, neuroscience, chemical data analysis, and ecological forecasting. Recent Publications: Her 2025-2024 work covers Hilbert space-valued linear processes, kernel estimation for nonlinear dynamics, confidence interval approximations, and latent Gaussian count time series. Earlier papers address simultaneous diagonalization, long-run variance matrices, and transition rate estimation challenges. Contact: marie.dueker@fau.de

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +۲ مورد دیگر

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

René Westerholtمشاهده پروفایل

استادیار

• Spatial Analysis
• Place-Based Research
• Geospatial Statistics
• +۳ مورد دیگر

René Westerholt is a Juniorprofessor (Assistant Professor) at the Department of Spatial Planning of TU Dortmund University, Germany. His research focuses on statistical methods for spatiotemporal and place-based analysis, including spatial autocorrelation, hotspot detection, and spatial regression. He also specializes in formalizing place-based information and its integration with urban analytics. Education : Ph.D. in Geography (2013-2018), Heidelberg University M.Sc. Geoinformatics (2010-2012), Osnabrück University B.Sc. Geoinformatics (2007-2010), Osnabrück University Research Themes : Spatial analysis (user-generated data, spatial weights, autocorrelation) Place-based formalization (perception, geometric footprints) Empirical geographical studies (urban heat exposure, mobility services) Teaching : Digital Geographies, Spatial Statistics, Computer Science for Planners, and Urban Analytics at TU Dortmund University. He has also taught nationally and internationally in German and English. Awards : Dissertation Award Geoinformatics (2019) CDRC Data Challenge First Prize (2018) Young Researchers Award (2014) Contact : Office at TU Dortmund University, Room GBIII/318. Email: rene.westerholt@tu-dortmund.de

• Structural Geology
• Seismology
• Tectonics
• +۵ مورد دیگر

Professor Ake Fagereng is a Professor in Structural Geology at the School of Earth and Environmental Sciences, Cardiff University. He has been with Cardiff University since 2014, initially as a Lecturer in Structural Geology (2014-2017), then as a Reader in Structural Geology (2017-present), and currently as a Professor. Prior to Cardiff, he was a Lecturer/Senior Lecturer in the Department of Geological Sciences at the University of Cape Town (2010-2014). He also holds an honorary position as a Research Fellow in the Department of Geological Sciences at the University of Cape Town (2014-present). Professor Fagereng's primary research focuses on how the Earth deforms and how this deformation is recorded in rocks. His work integrates field investigations of active and ancient deformation structures with microstructural analysis and geochemical techniques to understand: Seismic styles in subduction zones (from creep through slow slip events to giant earthquakes) Rift formation processes and the role of pre-existing structures Causes of large earthquakes far from plate boundaries Interplay between fluids (water and melt) and deformation processes His recent research has been significantly supported by an ERC-funded MICA project, which explores geological processes controlling fault slip speed and varied behavior of faults accommodating tectonic deformation in the Earth's crust. The project investigates why some faults experience slow earthquakes while others generate damaging seismic events, addressing critical questions of societal importance regarding earthquake hazard. Professor Fagereng has received numerous prestigious awards for his contributions to geoscience, including: Outstanding Young Scientist Award from the European Geosciences Union (2016) International Visiting Fellow at Kyoto University, Japan (2016) Excellent Reviewer Award from Earth, Planets, and Space (2015) Editor's Citation for Excellence in Refereeing from the American Geophysical Union (2013) President's Award from the National Research Foundation, South Africa (2013) As a dedicated educator and Fellow of the Higher Education Academy, Professor Fagereng teaches across undergraduate programs at Cardiff University, specializing in structural geology, tectonics, and geodynamics. He has served as Director of Fieldwork from 2015 to 2018 and regularly leads field courses in Cornwall, North Wales, Spain, and Cyprus. His research portfolio demonstrates consistent contributions to understanding earthquake processes, fault mechanics, and tectonic deformation through extensive international collaborations and fieldwork in diverse tectonic settings worldwide.

Hugo Georges Victor Lavenantمشاهده پروفایل

استادیار

• Optimal Transport
• Calculus of Variations
• Convex Analysis
• +۳ مورد دیگر

Hugo Georges Victor Lavenant serves as Assistant Professor in the Department of Decision Sciences at Bocconi University, Milan, where he has held a faculty position since 2020. Previously, he completed a postdoctoral fellowship at the University of British Columbia (2019-2020) under the Pacific Institute of Mathematical Sciences and earned his PhD in Mathematics from Université Paris-Sud (2016-2019) under Filippo Santambrogio's supervision. His academic foundation includes: PhD in Mathematics, Université Paris-Sud (2016-2019) Studies at École Normale Supérieure (2012-2016) covering mathematics, physics, history, and philosophy of science Classes préparatoires in mathematics and physics (2010-2012) Lavenant's research centers on optimal transport theory and its applications across mathematical disciplines. He investigates geometric structures in Wasserstein spaces, develops numerical methods for dynamical optimal transport, and bridges theoretical advances with Bayesian statistics. His work demonstrates particular innovation in trajectory inference for biological data and dependence measures for random measures, connecting pure mathematics with computational statistics. Recent publications reveal accelerating interdisciplinary impact, with 2024-2025 works extending optimal transport to machine learning (kernel methods, variational inference) and data science (opinion dynamics, single-cell analysis). This trajectory shows increasing methodological sophistication in handling measure-valued mappings and non-smooth geometries while maintaining computational tractability. Award recognition includes: Pacific Institute of Mathematical Sciences Postdoctoral Fellowship Lavenant actively mentors early-career researchers through formal advising relationships and collaborative projects. He currently supervises two PhD candidates (George Kanchaveli and Francesco Mascari, co-advised with Marta Catalano) and has guided Master's students including Mathis Hardion and Niccolò Bargellini. His teaching portfolio spans advanced analysis, optimization, and real analysis courses at Bocconi, reflecting his commitment to mathematical rigor in education. He operates within Bocconi's Decision Sciences ecosystem while maintaining international collaborations with researchers at UBC, Université Paris-Sud, and statistical groups worldwide. Current projects focus on entropy-based transport methods and geometric approaches to nonparametric statistics, positioning his work at the intersection of theoretical mathematics and data-driven applications.

Marcus T Ciceroneمشاهده پروفایل

استاد

• Coherent Raman Spectroscopy
• Biological Imaging
• Dynamics of Liquids and Amorphous Solids
• +۳ مورد دیگر

Professor Marcus T Cicerone is a faculty member at the Georgia Institute of Technology in the School of Chemistry & Biochemistry . His career spans positions at the National Institute of Standards and Technology (2001-2019) and a visiting teaching professorship at Brigham Young University . He received his Ph.D. in 1994 from the University of Wisconsin–Madison under Mark Ediger. Research Interests : Development and application of broadband coherent Raman scattering (BCARS) microscopy for biological imaging, understanding dynamic heterogeneity in liquids and glasses , and creating nanometer-sized sugar-glass encapsulation methods for protein stabilization in drug delivery. His work bridges physics, chemistry, and biotechnology through innovative spectroscopic techniques. Scientific Awards : Fellow of American Physical Society Johnson & Johnson Director’s Research Award Two Department of Commerce Bronze Medals 2015 Washington Academy of Sciences Physical & Biological Sciences Award 2017 Arthur S. Flemming Award 2022 Moderna Grant for ambient-stable RNA vaccines 2025 Quadrant-i Grant for nucleic acid delivery Recent Publications demonstrate his leadership in coherent Raman imaging, glassy material dynamics, and protein stabilization. His 2025 Nature Photonics paper on dynamic percolation in liquids and 2024 Advanced Materials work on lipid nanocapsules highlight cross-disciplinary impact.

Yuè Liمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Population Genetics
• +۴ مورد دیگر

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

Brenda Ogleمشاهده پروفایل

استاد

• Cardiac Tissue Engineering
• 3D Bioprinting
• Stem Cell Differentiation
• +۶ مورد دیگر

Brenda Ogle is a Professor in the Department of Biomedical Engineering at the University of Minnesota's College of Science and Engineering. She leads the System Regeneration Lab, where her research focuses on cardiac tissue engineering, stem cell differentiation, and advanced 3D bioprinting technologies. Her work bridges multiple disciplines including stem cell biology, extracellular matrix science, and engineering principles to develop novel approaches for cardiovascular regeneration. Dr. Ogle's research interests primarily center on understanding the mechanisms that govern stem cell fate, particularly in the context of the cardiovascular system. Her lab is pioneering 3D bioprinting for cardiac tissue engineering, creating complex model systems that go beyond simple geometric shapes. Key areas of investigation include the role of extracellular matrix proteins in guiding stem cell differentiation, the development of novel tools for analyzing stem cell behavior, and the delivery of stem cells or associated progeny to the body. Her work has led to breakthroughs in creating patch-like structures with micron-scale features that support cardiac cell organization and can be adhered to failing hearts. Dr. Ogle's research has resulted in significant scientific contributions, including the development of unique bioink formulations coupled with multiphoton-based 3D printing to create chambered heart structures based on digital templates. These engineered tissues can sustain flow profiles and exhibit pressure-volume dynamics characteristic of the native heart, making them valuable for studying cardiac disease progression and testing drug efficacy. Her work has received recognition including an NIH R01 award for Epicardial Regulation of Myocardial Function and being named a BMES Fellow. NIH R01 Awarded, Epicardial Regulation of Myocardial Function BMES Fellow (2021) Dr. Ogle mentors a diverse team of researchers including postdoctoral associates, graduate students, and undergraduate researchers. Her lab has produced numerous PhD graduates who have gone on to successful careers in academia and industry. Current research projects in her lab include heart organoid formation using hiPSC-derived cardiomyocytes, investigation of hypertrophic cardiomyopathy mechanisms, cardiomyocyte maturation studies, and development of ECM-based bioinks for cardiac constructs. The lab is also working on creating integrated platforms for high-throughput cardiac organoid production and developing models to study the impact of radiation exposure on cardiac function.

• Industrial Engineering
• Maintenance Optimization
• Industry 4.0/Digital Transformation
• +۶ مورد دیگر

Aitor Goti Elordi is a Professor in the Department of Mechanics, Design and Industrial Management at the Faculty of Engineering, University of Deusto. His extensive research portfolio spans industrial engineering, maintenance optimization, Industry 4.0 implementation, and sustainable manufacturing practices. He leads multiple EU-funded and industry-collaborative research projects focused on digital transformation in manufacturing sectors. His research interests center around the intersection of industrial engineering and digital transformation, with particular focus on maintenance optimization using evolutionary algorithms, predictive maintenance systems, and the development of future skills requirements for evolving industrial sectors. His work bridges theoretical research with practical industrial applications, particularly in the Basque manufacturing ecosystem. Analysis of his recent publications reveals a strong trend toward interdisciplinary research addressing Industry 4.0 challenges across multiple sectors. His work consistently focuses on practical applications of data science and AI in industrial contexts, with growing emphasis on sustainability and circular economy principles. A distinctive pattern in his research is the development of competency frameworks to identify future skills requirements across various industrial sectors including steelmaking, renewable energy, and supply chain logistics. Professor Goti Elordi leads multiple significant research projects including SUSTASKILLS (2023-2025) focused on industrial symbiosis skills, REshaping Supply CHAins for Positive social impact (2022-2025), and Real-time acoustic sensorS and artificial Intelligence appLications (2023-2026). His work has secured funding from the European Commission, Basque Government, and major industrial partners including SIDENOR, NEMAK, and ETXE-TAR. He actively supervises student projects, particularly in the area of machinery redesign and additive manufacturing applications. His teaching and research integrate practical industrial experience with academic rigor, emphasizing the development of both technical and transversal skills needed for future industrial challenges.

• Poverty and Economic Mobility
• Social Justice
• Housing Policy
• +۴ مورد دیگر

Dr. Sowmya Balachandran serves as Assistant Professor in the Department of Urban Planning and Community Development at the School for the Environment, University of Massachusetts Boston. Her transnational research examines poverty risk and economic mobility through housing policy and community development frameworks. Her academic credentials include: PhD in Regional Planning from University of Illinois at Urbana Champaign M.S. in Urban and Regional Planning from CEPT University Ahmedabad, India B.S. in Architecture from Maharaja Sayajirao University, Vadodara, India Dr. Balachandran investigates power structures in marginalized communities, state assistance in affordable housing production, and access to credit/housing markets. Her mixed-methods approach integrates ethnographic studies with spatial-statistical analysis to advance social justice in planning theory. Current research explores non-profit intermediary networks and inclusionary housing dynamics across diverse metropolitan contexts. Recent publications analyze spatial opportunity translation, non-profit homeownership networks, and inclusionary housing outcomes. These works demonstrate consistent focus on policy implementation gaps and equity-centered community development across transnational settings. She directs two active research initiatives: one assessing fair housing reforms' impact on tax credit developments in Dallas-Fort Worth, and another evaluating San Antonio human service organizations' readiness for integrated transportation systems serving disabled/elderly populations. With 15+ years professional experience, Dr. Balachandran co-founded Bangalore-based Alchemy Urban Systems (specializing in land use planning, disaster reconstruction, and water management) and established India's first Dell Foundation-backed housing facilitation center in Ahmedabad.

Curtis Bakerمشاهده پروفایل

استاد

• Visual Perception
• Neurophysiology
• Psychophysics
• +۱۲ مورد دیگر

Curtis Baker is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site, and a Professor in the Department of Ophthalmology and Visual Sciences at McGill University. His research focuses on understanding human visual perception , particularly low-level neural mechanisms that underpin everyday visual processing of figure-ground and local depth relationships through cues like contrast, texture, and motion . Institutional Affiliation: RI-MUHC, McGill University Academic Rank: Professor His lab employs human psychophysics , electrophysiology , optical imaging , and computational modeling to study how early visual processing detects and utilizes complex cues. Key research areas include receptive field dynamics , second-order boundary perception , and machine learning applications in visual neuroscience. Recent publications highlight his work on convolutional neural networks for receptive field estimation, Y-like neuronal responses in human vision, and texture regularity models using wavelet analysis. Collaborations span computational neuroscience , optical imaging , and depth perception mechanisms. Students and researchers in his lab include graduate students in Integrated Program in Neuroscience , Physiology , and Biomedical Engineering , alongside alumni contributing to neurophysiology and computational biology projects.

David Hyten, Jr.مشاهده پروفایل

استاد

• Plant Genetics
• Soybean Genomics
• Molecular Breeding
• +۴ مورد دیگر

David Hyten, Jr. serves as the Haskins Professor of Plant Genetics within the Department of Agronomy & Horticulture at the University of Nebraska-Lincoln, where he leads research advancing soybean genomics and breeding methodologies for global food security. Education: Ph.D., University of Maryland, 2005 M.S., University of Tennessee, 2002 B.A., Southern Illinois University, 1999 Research Focus: Dr. Hyten's program integrates high-throughput genotyping with quantitative genetics to dissect complex traits including disease resistance (cyst nematode, stem borer), seed composition (protein/oil), and yield stability. His work pioneers genomic selection pipelines and recombination hotspot analysis to accelerate breeding cycles, with emphasis on translating genomic discoveries into practical cultivar development through marker-assisted selection and predictive modeling. Publication Trends: Recent outputs (2021-2025) reveal evolving emphasis from foundational genomics (recombination mapping, QTL identification) toward applied breeding solutions, including cost-effective DNA extraction methods, pedigree-based imputation techniques, and community strategic planning for soybean genomics. His work increasingly addresses genotype-environment interactions and stability of key traits across diverse production systems.

Abhijit Palمشاهده پروفایل

دانشیار

• Geometric Group Theory
• Relatively Hyperbolic Groups
• Mapping Class Groups

Abhijit Pal is an Associate Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur. He holds a PhD from the Indian Statistical Institute, Kolkata (2011), an M.Sc in Pure Mathematics from Calcutta University (2004), and a B.Sc with Mathematics Honours from Calcutta University (2002). His office is located at FB-575, IIT Kanpur. His research focuses on Geometric Group Theory , with specific interests in Relatively Hyperbolic Groups and Mapping Class Groups . His work bridges geometric topology and algebraic structures, exploring hyperbolic extensions and boundary mappings. Recent publications (2010–2013) center on Cannon-Thurston maps, Teichmüller quasigeodesics, and relative hyperbolicity, contributing to advancements in low-dimensional topology and group dynamics.

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +۳ مورد دیگر

Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .

Ricardo Dahisمشاهده پروفایل

استادیار

• Politics
• Environment
• Development Economics
• +۳ مورد دیگر

Ricardo Dahis is an Assistant Professor in the Department of Economics at Monash University's Faculty of Business and Economics. His research spans the intersection of Politics, Environment, and Development Economics, with particular focus on Brazilian political economy, public finance, and institutional analysis. Dahis has established himself as a prominent researcher with publications in top economics journals including the Review of Economics and Statistics and the Journal of Public Economics. His research interests encompass political economy, development economics, environmental policy, public finance, and Brazilian institutional analysis. Dahis frequently examines how political institutions shape economic outcomes, with particular attention to corruption, electoral behavior, deforestation in the Amazon, and the impact of technological change on social outcomes. His methodological approach combines rigorous econometric analysis with innovative data collection techniques, often leveraging administrative data from Brazilian government sources. Dahis's publication record shows a consistent focus on Brazilian political economy with increasing attention to environmental issues and technological impacts. His recent work demonstrates sophisticated use of natural experiments and quasi-experimental designs to address causal questions in political economy. The articles reveal a researcher deeply engaged with both theoretical questions in economics and practical policy implications, particularly for developing countries. Dahis is actively involved in academic mentorship, offering supervision hours for students and postdocs. His teaching materials suggest engagement with both undergraduate and graduate instruction in economics. Co-founder of Data Basis (Base dos Dados), a nonprofit startup making high-quality data universally accessible in Brazil Affiliated with SoDa Labs and CDES research groups Active participant in the academic community with presence on Google Scholar, GitHub, and ORCID Dahis maintains an open door policy for academic discussions and offers specialized consultations for Monash University students through scheduled appointments. His work with Data Basis demonstrates a commitment to building research infrastructure that benefits academics, journalists, policymakers, and developers across Brazil.