جست‌وجوی استاد و پژوهشگر

• Anesthesiology
• Neurophysiology
• Neurocritical Care
• +۳ مورد دیگر

Prof. Gerhard Schneider is a Professor of Anesthesiology at the Technical University of Munich (TUM) within the TUM School of Medicine and Health. His work focuses on neurophysiological aspects of anesthesia, particularly EEG-based monitoring, aging effects in anesthesia, and neurodegenerative disease biomarkers. He has contributed to understanding cerebral blood flow dynamics, nociception responses under anesthesia, and optimizing clinical guideline adherence in critical care settings. Research Interests: EEG monitoring during anesthesia and sedation Neurological correlates of anesthesia-induced states Cognitive reserve and brain frailty assessment Age-related anesthesia outcomes Immunological and hemodynamic interactions during surgery Key Research Trends: His recent work emphasizes EEG spectral analysis to differentiate brain states (e.g., consciousness, anesthesia depth) and identify biomarkers for postoperative cognitive disorders. Studies on sevoflurane and propofol effects reveal substance-specific impacts on cerebral blood flow and CSF dynamics. Collaborative projects apply informatics tools (FHIR/OMOP) to improve clinical guideline adherence in ICU environments. Awards: No specific awards listed in the provided texts. Grants & Advising: No explicit grants or student advisement details are included in the text. His research is funded through institutional and collaborative grants. Labs/Teams: Associated with TUM's anesthesiology and neurocritical care research groups, but no specific lab names are mentioned.

Wojciech Pieczynskiمشاهده پروفایل

استاد

• Signal Processing
• Image Segmentation
• Statistical Modeling
• +۳ مورد دیگر

Prof. Wojciech Pieczynski is a Professor at Telecom SudParis, affiliated with the SAMOVAR department. His research focuses on statistical signal processing, hidden Markov models, and their applications in image segmentation, data analysis, and transportation systems. He has published extensively on topics including semi-Markov chains, triplet Markov models, and hybrid machine learning approaches. His work integrates advanced statistical methodologies with real-world challenges such as traffic state prediction, non-stationary data analysis, and biomedical activity classification using wearable sensors. Key contributions include innovations in filtering techniques, model order reduction, and unsupervised learning algorithms.

Tobias Weichمشاهده پروفایل

استاد

• Spectral Theory
• Hyperbolic Dynamics
• Geometric Analysis
• +۳ مورد دیگر

Prof. Dr. Tobias Weich is a Professor (W3) at Paderborn University's Institute of Mathematics, within the Faculty of Computer Science, Electrical Engineering and Mathematics. He also serves as Vice Dean and leads the Emmy-Noether Group. His research focuses on spectral analysis, hyperbolic dynamics, geometric analysis, and quantum computing. Weich holds a doctorate from Philipps University Marburg (2014) and completed degrees in Physics and Mathematics at Philipps-Universität Marburg and Université Joseph Fourier, Grenoble. His key projects include the TRR 358 collaboration on spectral theory and geodesic flows, as well as the PhoQC initiative in photonic quantum computing. Awards include the 2019 Emmy Noether Grant from the DFG. Weich currently teaches advanced courses like 'Geometrische und Harmonische Analysis' and chairs the Mathematics Institute (2023–2025). His work bridges pure mathematics (e.g., spectral correspondences, temperedness of symmetric spaces) and applied domains (e.g., quantum unitary decomposition). He actively engages in research management, including leadership roles in the CRC/TRR 358 and PhoQS initiatives.

Laurie S. Stevisonمشاهده پروفایل

دانشیار

• Evolutionary Genomics
• Speciation
• Recombination
• +۱۰ مورد دیگر

Laurie S. Stevison is an Associate Professor in the Department of Biological Sciences at Auburn University, where she also serves as Coordinator for the Graduate Certificate in Computational Biology. Her research is centered on evolutionary genomics, using model systems such as primates and fruit flies to investigate speciation, recombination, and introgression. Education: Postdoctoral Fellow – University of California San Francisco, Institute for Human Genetics (2011–2015) Ph.D. – Duke University, Biology (2011) M.A. – Rice University, Ecology and Evolutionary Biology (2007) B.S. – Centenary College of Louisiana, Biophysics (2005) Her research interests include evolutionary genomics, speciation, recombination, population genetics, comparative genomics, and bioinformatics . She employs population genetic approaches to study how chromosomal inversions reduce recombination in hybrids, thereby contributing to species maintenance despite gene flow. Her work also explores recombination rate plasticity in response to environmental factors like temperature and maternal age. The selected publications reflect a strong trend in comparative and population genomics , with a focus on recombination rate evolution across species, hybridization in primates and Drosophila, and the role of structural variation in speciation. Her research spans taxa including great apes, gibbons, baboons, and Drosophila, contributing to broader understanding of genomic architecture and evolutionary processes. Scientific Contributions: Participation in the Great Ape Genome Project First evidence of higher Neanderthal ancestry in East Asians vs. Europeans Development of fine-scale recombination maps in multiple species Pioneering work on temperature-induced recombination plasticity Dr. Stevison advises graduate students and leads the Stevison Lab, which is actively involved in projects linking climate change to genetic variation in Drosophila robusta . She teaches courses in computational biology and bioinformatics at both undergraduate and graduate levels. Her lab uses both direct (genetic crosses) and indirect (linkage disequilibrium) methods to estimate recombination rates, and is expanding research to non-great ape primates.

• Statistics
• Machine Learning
• Geoinformatics
• +۴ مورد دیگر

Matthias Schlögl is a Researcher at the Institute for Alpine Natural Hazards (Department of Landscape, Water and Infrastructure) within the University of Natural Resources and Life Sciences, Vienna (BOKU) . With a doctoral degree in technical sciences (Dr. nat. techn.) from BOKU (2015), he specializes in statistical analysis and geospatial modeling of natural hazards. Research Focus : Statistical modeling of extreme weather events Machine learning applications for hazard assessment Climate change impact on Alpine geohazards Exposure analysis for infrastructure Multi-hazard spatiotemporal risk assessment Recent publications (2023-2025) span landslide susceptibility modeling, wildfire vulnerability indices, debris flow analysis, and climate change impacts on Alpine hazards. He contributes to projects funded by the European Union , Austrian Research Promotion Agency (FFG) , and national ministries. Community engagement includes peer-review activities for journals like Natural Hazards and Earth System Sciences , Journal of Hydrology , and Remote Sensing .

Jon Leeمشاهده پروفایل

استاد

• Mathematical Optimization
• Combinatorial Optimization
• Integer Programming
• +۳ مورد دیگر

Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group. Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations. His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications. Lee has received notable recognition including: INFORMS Computing Society Prize (2010) Fellow of INFORMS (since 2013) As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field. Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.

Lieven Vandenbergheمشاهده پروفایل

استاد

• Optimization
• Systems and Control
• Signal Processing
• +۳ مورد دیگر

Lieven Vandenberghe is a Professor at the University of California, Los Angeles (UCLA) with joint appointments in the Electrical and Computer Engineering Department and the Department of Mathematics . He received his PhD in Electrical Engineering from K.U. Leuven, Belgium, in 1992. Vandenberghe has held visiting professor positions at K.U. Leuven and the Technical University of Denmark, and his career at UCLA began in 1997 after postdoctoral work at K.U. Leuven and Stanford University. His research focuses on optimization , systems and control , and signal processing . He co-authored the influential textbook Convex Optimization (2004) with Stephen Boyd and edited the Handbook of Semidefinite Programming (2000). His recent publications emphasize large-scale convex optimization algorithms, first-order methods, and applications to power systems and signal processing. Key contributions span semidefinite programming , operator splitting , and chordal graph techniques for sparse optimization. He has supervised numerous PhD and MS students, including Xin Jiang (2022), Jinchao Li (2015), and Martin Skovgaard Andersen (2011). Vandenberghe also developed software tools like CVXOPT and CHOMPACK for convex and sparse matrix optimization.

• Theoretical Physics
• Active Matter
• Soft Matter
• +۳ مورد دیگر

Professor Abhinav Sharma is affiliated with the University of Augsburg in the Faculty of Mathematics, Natural Sciences, and Technology , specifically the Institute of Physics under the Theoretische Physik II group. His academic journey includes a PhD thesis from Technische Universiteit Eindhoven in 2011. University : University of Augsburg School : Faculty of Mathematics, Natural Sciences, and Technology Department : Institute of Physics Academic Rank : Professor Abhinav Sharma's research focuses on theoretical physics , particularly in active matter , non-equilibrium systems , and soft matter . His work explores the dynamics of active Brownian particles , polymer transport in active baths , and nonlinear mechanics of fibrous networks . His recent publications (2023-2025) highlight advancements in odd-diffusive systems , chiral particles , and activity-gradient driven transport . These studies span disciplines like physics , chemical engineering , and biophysics , with a strong emphasis on statistical mechanics and nonlinear dynamics . As an advisor, he mentors PhD students including Bhavesh Valecha , Pietro Luigi Muzzeddu , and Iman Abdoli . He collaborates extensively with researchers like Joseph Brader , Fred MacKintosh , and Ralf Metzler . Sharma is part of the Theoretische Physik II lab, contributing to understanding active transport , polymer mechanics , and criticality in complex systems .

Ilgin Gulerمشاهده پروفایل

دانشیار

• Transportation Engineering
• Intelligent Transportation Systems
• Traffic Signal Control
• +۸ مورد دیگر

Ilgin Guler is an Associate Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University’s College of Engineering. Her research focuses on intelligent transportation systems, traffic operations, infrastructure sustainability, and equitable mobility solutions. She contributes to interdisciplinary projects such as the Equitable Optimization of Transportation Systems and Services addressing climate and sustainability challenges. Her research interests span Transportation Engineering , Intelligent Transportation Systems , Traffic Signal Control , Connected Vehicles , Pavement Management , and Transit Signal Priority . She applies advanced techniques including deep learning and optimization to improve urban and rural transportation systems. Her work emphasizes safety, equity, and sustainability in infrastructure planning and operations. The recent publications highlight a strong trend in integrating machine learning with classical traffic theory, enhancing infrastructure resilience through data-driven maintenance, and evaluating shared mobility impacts on safety and equity. Her research bridges civil engineering with data science, focusing on real-world applications in Pennsylvania and beyond. Scientific Awards: No awards explicitly mentioned in the provided text. She advises graduate students in transportation engineering and has been involved in multiple research projects, including seed-funded initiatives from the Institute for Energy and the Environment (IEE). While specific grant amounts and student names are not listed, her collaborative work with faculty like Vikash Gayah and Anik Sengupta indicates active mentorship and team leadership. Her research is associated with initiatives such as the Equitable Communities and the Built Environment and leverages tools from connected vehicle technology, impact-echo testing, and generative AI for infrastructure assessment.

• Machine Learning
• Computational Science
• Artificial Intelligence
• +۳ مورد دیگر

Xi Yu is a Researcher at the Computational Science Initiative of Brookhaven National Laboratory, with expertise in Machine Learning, Artificial Intelligence, and Computational Science. Previously, they served as a Postdoctoral Research Associate (2022–2025) and Research Intern at Mitsubishi Electric Research Laboratories (2021). Ph.D. in Electrical Engineering, University of Florida (2022) M.S. in Electrical Engineering, University of Florida (2019) Their research spans domain generalization, adversarial robustness, information theory, and coral image segmentation, with a focus on applying neural networks and entropy functional methods to diverse challenges in AI and environmental science. Recent publications highlight work on AI training energy efficiency, CLIP-based image-text alignment, and information bottleneck theory. Notable awards include the NYU Tandon Faculty First Look Fellowship (2024) and recognition at ICDM (2021).

Oleh Melnykمشاهده پروفایل

استاد مهمان

• Mathematical Imaging
• Phase Retrieval
• Numerical Analysis
• +۴ مورد دیگر

Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.

Damien Fournierمشاهده پروفایل

پژوهشگر

• Computational Helioseismology
• Solar Physics
• Inverse Problems
• +۲ مورد دیگر

Damien Fournier is a Scientist at the Max Planck Institute for Solar System Research in the Department of Solar and Stellar Interiors. He joined the institute in 2017 following postdoctoral work at Georg-August-University Göttingen (2012-2016) and an assistant associate professor position at Aix-Marseille University (2011-2012). He holds a Ph.D. in Applied Mathematics from Aix-Marseille University (2008-2011) and an engineering degree from ENSIMAG with a focus on modeling and scientific computation. His research specializes in computational helioseismology , with dual focus areas: Forward modeling of wave propagation dynamics in solar interiors Inverse problem resolution for reconstructing interior perturbations from surface seismic measurements Secondary interests include Reynolds stress analysis, noise estimation methodologies, and Pinsker estimators for flow inversions. Recent publications (2014-2022) demonstrate strong emphasis on solar wave mechanics and inversion techniques, with 73% focused on helioseismology fundamentals. Dominant themes include: Solar Rossby wave dynamics under rotational effects (26% of recent works) Computational advancements in wave equation solutions and kernel development (33%) Meridional flow structure and convection zone dynamics (20%) He contributes to academic training through co-supervision of bachelor/master/PhD candidates. Research collaborations include ongoing projects with INRIA groups in Pau and Bordeaux focusing on finite-element modeling of solar wave equations.

• Probability Theory
• Random Planar Maps
• Scaling Limits
• +۷ مورد دیگر

Nicolas Curien is a Professor in the Probability and Statistics Team at Université Paris-Saclay, where he leads the M2 "Mathematics of Randomness" program and heads the ERC-funded project "SuPerGRandMa". His office is located in Building 307, Orsay. Curien has been a faculty member since 2014, following positions as a CNRS researcher at LPMA (Paris) and teaching assistant at ENS Paris. He completed his PhD under Jean-François Le Gall in 2011. Curien's research spans probability theory , random geometry , and statistical physics , with focus areas including: Scaling limits of random planar maps and hyperbolic geometries Percolation theory on random lattices Growth-fragmentation processes and branching structures Metric properties of stochastic trees and surfaces His work frequently combines combinatorial probability with continuous stochastic processes to analyze phase transitions and universal behavior. Analysis of Curien's recent publications reveals consistent themes: rigorous treatment of scaling limits in random geometries (especially maps and trees), critical phenomena in percolation models, and probabilistic aspects of hyperbolic surfaces. Approximately 80% of his 2021-2024 papers involve phase transitions or universality classes , with methods drawing from Lévy processes, peeling explorations, and multi-scale analysis. Significant awards recognizing his contributions include: ERC Consolidator Grant (SuPerGRandMa, 2023) Prix Marc Yor (2022) Prix Jacques Herbrand (2019) Rollo Davidson Prize (2015) He was elected Junior Member of the Institut Universitaire de France in 2016. Curien maintains an extensive research group, having supervised over 20 PhD students and postdoctoral researchers since 2013. Notable former students include Thomas Budzinski (CNRS researcher), Alice Contat (CNRS researcher), and Cyril Marzouk (Assistant Professor at École Polytechnique). His ERC project currently supports 4 early-career researchers. He collaborates internationally with labs including LPMA (Paris), University of Vienna, and EPFL. Curien also participates in the gastronomic societies "Tasteurs de Fourmes du Cantal" and "Gousteurs de Kirsch de Fougerolles".

• Fuzzy Sets
• Intuitionistic Fuzzy Sets
• Data Analysis
• +۶ مورد دیگر

Alžbeta Michalíková serves as Associate Professor at Matej Bel University's Faculty of Arts, Department of Computer Science. Her academic profile spans mathematical foundations and applied computational research with significant contributions to fuzzy logic systems. Her research focuses on Fuzzy Sets and Intuitionistic Fuzzy Sets with applications in Computer Vision, Biology, and Medicine. She investigates mathematical properties of these structures and explores integrations with Neural Networks and Evolutionary Algorithms. Recent work emphasizes smart city applications, air quality analysis, and digital education tools. Her publication trends show evolution from theoretical fuzzy mathematics (2005-2015) to applied AI systems (2020-present), particularly in environmental monitoring and transportation systems. Key themes include uncertainty modeling, image classification, and real-time data processing for urban applications. Erasmus+ KA2 projects on air pollution technologies (2022-2024) KEGA projects for digital educational tools (2020-2025) VEGA research on fuzzy models (2011-2017) APVV projects on scientific outreach She teaches Mathematics for Computer Scientists, Numerical Methods, and specialized Fuzzy Sets courses. Her technical infrastructure includes ORCID (0000-0002-0568-9279), SCOPUS (24448471500), and ResearchGate profiles. Current work focuses on digital identification keys for mycology education and AI-driven smart city analytics.

Kasun Fernando Akurugodageمشاهده پروفایل

مدرس

• Probability Theory
• Dynamical Systems
• Statistics
• +۵ مورد دیگر

Dr. Kasun Fernando Akurugodage is a Lecturer in Mathematics at Brunel University London, within the College of Engineering, Design and Physical Sciences. He is a member of the EPSRC Mathematical Sciences Early Career Forum, blending expertise in probability theory, dynamical systems, and machine learning. Education: PhD in Mathematics (2018), University of Maryland; BSc in Mathematics, University of Colombo. Research Interests: He focuses on statistical properties of dynamical systems, Markov processes, and higher-order asymptotics for limit theorems, employing spectral theory and techniques from probability, stochastic analysis, and statistics. Recent work explores machine learning applications to dynamical systems and interdisciplinary research opportunities. Publications span limit theorems, Edgeworth expansions, large deviations, and robust normalizing flows, with a 2025 paper on unbounded observables in ergodic theory. His work bridges theoretical and applied mathematics, including outreach initiatives in Canada, Sri Lanka, and the U.S.