جستجوی استاد راهنمای دانشگاهی

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

Emilio Zagheniمشاهده پروفایل

استاد پژوهش

• Migration
• Mobility
• Population Dynamics
• +۴ مورد دیگر

Emilio Zagheni is a Director at the Max Planck Institute for Demographic Research (MPIDR) and affiliate faculty in the Department of Sociology at the University of Washington, Seattle, where he served as Training Director of the Center for Studies in Demography and Ecology. He earned a Ph.D. in Demography (2010) and an M.A. in Statistics (2008) from UC Berkeley. His research focuses on migration , mobility , and population dynamics , integrating computational methods with traditional demographic approaches. Key themes include gender disparities in migration , climate change impacts on well-being , and digital data bias correction . Recent publications analyze global academic migration patterns , temperature-related mortality , and social media's role in migration studies . His work leverages digital trace data from platforms like Facebook and LinkedIn to innovate migration measurement. President, European Association for Population Studies (EAPS) Chair, Digital Demography Panel (IUSSP, 2015-2022) Zagheni leads the MPIDR's Department of Digital and Computational Demography , which includes the Labs of Migration and Mobility and Population Dynamics and Sustainable Well-Being . His initiatives, such as the Population and Social Data Science Summer Incubator Program , foster interdisciplinary collaboration.

Jutta Gampeمشاهده پروفایل

استاد پژوهش

• Statistical Demography
• Survival Analysis
• Mortality Analysis
• +۴ مورد دیگر

Jutta Gampe serves as Head of the Laboratory of Statistical Demography at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany. Her position places her at the forefront of advanced demographic methodology development, with particular expertise in survival analysis and statistical modeling of mortality patterns. As a research leader at one of the world's premier demographic research institutions, she contributes significantly to methodological innovation in demography. Dr. Gampe's research interests center on statistical demography with specific focus on survival analysis with multiple time scales, mortality at extreme ages, and cognitive functioning in aging populations. Her work bridges theoretical statistical development with practical demographic applications, particularly in the context of human longevity and aging processes. She has made substantial contributions to methods for analyzing interval-censored data and developing smooth hazard functions for complex demographic phenomena. Analysis of her recent publications reveals a strong methodological focus with increasing sophistication in handling multiple time scales in survival models. Her research demonstrates a consistent trajectory from foundational work on mortality at extreme ages toward more complex statistical frameworks that can accommodate multiple dimensions of time in demographic processes. The interdisciplinary nature of her work is evident in collaborations spanning statistics, demography, epidemiology, and even ecological applications. Dr. Gampe leads several significant research projects including Cognitive Functioning in Middle- and High-Income Countries, Estimating Smooth Rates from Interval-Censored Data, Mortality at Extreme Ages and Potential Limits to Human Lifespan, and Multiple Time Scales in Survival and Event-History Models. Her laboratory serves as a hub for advanced statistical methodology development in demographic research, attracting numerous collaborators from around the world.

• Artificial Intelligence
• Machine Learning
• Energy Systems
• +۳ مورد دیگر

Elissaios Sarmas is a researcher in the field of AI and machine learning applications for energy systems and smart cities. He has collaborated extensively with researchers like Vangelis Marinakis, Haris Ch. Doukas, and Ioannis Papias across institutions. Research Interests: AI in Energy Sector Smart Grid Analytics Data-Driven Decision Making Demand Response Programs Energy Poverty Mitigation Climate Change Adaptation Publication Trends: His recent work focuses on ensemble AI models for energy measurement, clustering methodologies for electricity loads, and large language models in energy digital twins, covering 2023-2025. Articles span journals like IEEE Access , Applied Soft Computing , and Information Sciences .

André Nusserمشاهده پروفایل

پژوهشگر

• Algorithms and Complexity
• Computational Geometry
• Dynamic Time Warping
• +۴ مورد دیگر

André Nusser is a CNRS Researcher affiliated with the COATI group at Inria Center, Université Côte d’Azur. His work focuses on algorithms, computational geometry, and metric space analysis, particularly around the Fréchet distance, dynamic time warping, and graph hyperbolicity. Current Position: CNRS Researcher at Inria Université Côte d’Azur Primary Research Areas: Algorithms & Complexity, Computational Geometry, Metric Space Analysis Research Focus: André's research explores geometric optimization problems, trajectory analysis, and fine-grained complexity. Key contributions include advancements in dynamic time warping under translation, diameter computation for intersection graphs, and efficient algorithms for polygon partitioning. Awards & Recognition: Recipient of the Best Paper Award at VLDB 2014, Honorable Mention at AAAI 2014, and Best Poster Runner-up at SIGSPATIAL 2019. His work on Hausdorff distance and graph hyperbolicity has been invited to special issues. Academic Collaborations: Co-author on over 20 publications across conferences like STOC, SoCG, SODA, ESA, and journals including ACM Transactions on Algorithms and Discrete & Computational Geometry. Collaborates with leading researchers in computational geometry and algorithms. Software Advocacy: Actively promotes free software in research, highlighting tools like Ipe, Xournal++, Zotero, and Zulip for scientific productivity and collaboration.

Yassine El Maazouzمشاهده پروفایل

مدرس

• Algebraic Geometry
• Non-Archimedean Geometry
• Commutative Algebra
• +۲ مورد دیگر

Yassine El Maazouz is a Harry Bateman Postdoctoral Scholar in the Division of Physics, Mathematics and Astronomy at the California Institute of Technology (Caltech), with a faculty mentor appointment under Professor Matilde Marcolli. He holds instructor positions for graduate mathematics courses including MA132C (Topics in Algebraic Geometry) and MA140B (Probability). Previously, he completed his PhD in Statistics at UC Berkeley after studying pure and applied mathematics at École Polytechnique. Education: PhD in Statistics, UC Berkeley (2019-2023) Master's in Pure and Applied Mathematics, École Polytechnique (2016-2019) Research Interests: His work bridges algebraic/non-Archimedean geometry, commutative algebra, combinatorics, and mathematical physics. Primary investigations include: p-adic algebraic manifolds, isotropic vector gram matrices, tropical invariants, spinor-helicity varieties, and combinatorial probability models. Research consistently demonstrates innovative applications of algebraic methods to problems in theoretical physics and statistical mechanics. Publication Trends: Recent articles (2019-2025) show strong focus on: (1) Algebraic structures in high-energy physics (scattering amplitudes, Coulomb branches), (2) Computational methods for p-adic manifolds and tropical geometry, (3) Combinatorial probability models (Bernoulli polynomials, stochastic processes), and (4) Matrix algebras derived from valuation theory. Work frequently appears in top journals including SIAM Journal on Applied Algebra, Selecta Mathematica, and Combinatorics/PROBABILITY Computing. Awards: Harry Bateman Postdoctoral Scholarship (Simons Foundation) Teaching & Academic Service: At Caltech, teaches graduate courses in algebraic geometry and probability theory. Previously instructed statistics courses at UC Berkeley (2019-2022). Co-organizes the Information, Geometry & Physics Seminar featuring cutting-edge interdisciplinary research. Maintains extensive lecture notes on number theory, probability, and representation theory.

José Alejandro Samperمشاهده پروفایل

استادیار

• Algebraic Combinatorics
• Geometric Combinatorics
• Topological Combinatorics
• +۵ مورد دیگر

Dr. José Alejandro Samper is an Assistant Professor of Mathematics at Pontificia Universidad Católica de Chile (PUC Chile), specializing in algebraic, geometric, and topological combinatorics. His research focuses on simplicial complexes, matroids, polytopes, and convex bodies, with a particular interest in the implications of linear orderings on matroids. Current affiliation: Mathematics Department, PUC Chile Previous positions: Postdoctoral fellow at Max Planck Institute for Mathematics in the Sciences (MPI), postdoc at ICERM and University of Miami PhD: University of Washington under Isabella Novik His research spans combinatorial structures with deep connections to algebraic geometry and topology, including studies on shellability, higher chordality, and matroidal relaxations. Recent publications highlight interactions between matroid theory, polytopes, and convex geometry, with a focus on topological constraints and Hopf algebraic structures. Dr. Samper co-organizes key mathematical events such as the Cibercoloquio Latinoamericano de Matemáticas and the Club de Matemáticas UC. He is accessible via email at jsamper@uc.cl.

• Multisensory Perception
• Self-Motion Perception
• Spatial Orientation
• +۵ مورد دیگر

Ksander de Winkel serves as Project Leader of the Motion Perception and Simulation research group within the Department of Human Perception, Cognition and Action at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, a position he has held since March 2017. His role involves leading both fundamental and applied research on self-motion perception, with direct applications to motion simulation and autonomous vehicle technologies. His academic credentials include a Ph.D. in Multisensory Perception of Spatial Orientation and Self-Motion from Utrecht University (2013), an M.Sc. in Applied Cognitive Psychology earned cum laude (2008), and a B.Sc. in Psychology, all completed at Utrecht University. Additional training includes specialized coursework in Applied Bayesian Statistics and Mathematical Statistics. De Winkel's research program centers on how the brain integrates sensory information during self-motion, with particular focus on causal inference mechanisms when sensory cues conflict. His experimental work measures perceptual thresholds, investigates motion sickness triggers, and develops computational models of multisensory integration. This fundamental research directly informs applied projects including motion cueing algorithm development, simulator fidelity assessment, and solutions for autonomous vehicle passenger comfort. His recent publications (2017-2018) demonstrate consistent focus on causal inference in spatial orientation and heading perception, with studies appearing in high-impact journals including Scientific Reports, Journal of Vision, and PLoS ONE. These works reveal how individual differences and sensory discrepancies affect motion perception, providing critical insights for engineering applications. Scientific recognition includes: Young Researcher Grant from ESA Scientific Committee (2012) He actively supervises graduate researchers from multiple institutions including University of Twente, University of Tübingen, and Delft University of Technology, with several students graduating cum laude . His research group maintains active collaborations with automotive and simulation industries to translate theoretical findings into practical motion solutions. Current projects address autonomous driving side-effects and next-generation motion cueing systems. The Motion Perception and Simulation group operates within the Department of Human Perception, Cognition and Action, conducting psychophysical experiments using motion platforms, virtual reality systems, and neuroimaging techniques to advance understanding of self-motion perception.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Mario Fritz is a leading academic at the CISPA Helmholtz Center for Information Security and Saarland University , with a focus on Trustworthy Information Processing . His work sits at the intersection of AI, Machine Learning, Security, and Privacy, addressing challenges in foundation models, health data, and adversarial robustness. Key Projects : ELLIOT (Multi-Modal Foundation Models), ELSA (Secure AI), PriSyn (Synthetic Health Data), AIgency (Generative AI in Cybersecurity), HMSP (Medical Security) Research Themes : AI/ML security, privacy-preserving techniques, causal modeling, healthcare applications, and ethical AI Recent Publications : Focus on LLM sampling, causal inference, model stealing, and privacy-aware document analysis Collaborations : European Laboratory for Learning and Intelligent Systems (ELLIS), BMBF-funded initiatives, GHGA (Human Genome Archive) Academic Leadership : As a Professor , he coordinates large-scale EU projects and mentors emerging researchers in AI ethics and security.

Johannes Resinمشاهده پروفایل

پژوهشگر

• Computational Statistics
• Statistical Analysis
• Data Science
• +۳ مورد دیگر

Johannes Resin serves as a Researcher at Goethe University Frankfurt , specifically within the Department of Computational Statistics . His role involves advanced computational statistical research, though no explicit details on teaching or administrative responsibilities are provided. Universitätstraße 69 60325 Frankfurt am Main Phone: +49 6221 – 533 – 285 Fax: +49 6221 – 533 – 298 Research Interests Computational Statistics Statistical Analysis Data Science Machine Learning Quantitative Analysis Statistical Modeling

Emilija Perkovicمشاهده پروفایل

استادیار

• Causal Inference
• Graphical Models
• Probabilistic Graphical Models
• +۳ مورد دیگر

Emilija Perkovic serves as the Dorothy Gilford Early Career Endowed Professor in Mathematical Statistics within the Department of Statistics at the University of Washington. She joined the department in Autumn 2018 as an Acting Assistant Professor and advanced to a tenure-track Assistant Professor position in Autumn 2020, establishing herself as a core faculty member in statistical methodology. Her academic foundation includes: B.Sc. in Mathematics from the University of Belgrade (2012) M.Sc. in Statistics from ETH Zürich (2014) Ph.D. in Statistics from ETH Zürich (2018) under Professor Marloes Maathuis Perkovic's research pioneers causal inference methodologies through probabilistic graphical models, focusing on intuitive frameworks for causal identification and estimation from observational data. Her work bridges theoretical statistics with practical applications, particularly in covariate adjustment techniques and Markov equivalence class analysis. She actively integrates expert knowledge to refine causal models, making complex inference accessible for real-world data challenges where experimental intervention is impossible. Her publication record reveals a concentrated evolution in causal graph theory, with recent preprints advancing adjustment criteria for diverse graph structures (MPDAGs, MAGs, ancestral graphs). Key trends include developing sound/complete identification rules, efficient estimation techniques under linearity, and minimal effect enumeration in equivalence classes—collectively enhancing robustness in observational causal analysis. Her scientific recognition includes: Best Poster Award, Semantic Statistics (SEMSTAT), Statistical Network Science Workshop (2017) While specific advising details and grant histories remain unreported in source materials, Perkovic's research trajectory indicates active mentorship through co-authored preprints with junior collaborators like Sara LaPlante and F. Richard Guo. Her Dorothy Gilford Endowed Professorship likely supports ongoing methodological innovation in causal statistics. No laboratory or team structures are explicitly referenced in her professional profile, suggesting independent or collaborator-driven research within the Statistics Department framework.

• Music Theory
• Music Cognition
• Popular Music Analysis
• +۵ مورد دیگر

David Temperley is a Professor of Music Theory at the Eastman School of Music (University of Rochester), where he has been teaching since 2000, progressing from Assistant Professor to Associate Professor and finally to Professor in 2015. His academic career also includes visiting positions at the University of Pennsylvania, Ohio State University, Columbia University, and New York University. He holds a Ph.D. in Music Theory from Columbia University (1996), an M.A. in Composition from Columbia University (1992), and a B.A. in History from Swarthmore College (1985). Dr. Temperley's research spans multiple areas at the intersection of music theory, cognition, and computational modeling. His work focuses on: Music cognition and perception Computational models of musical structure Analysis of popular music, particularly rock Relationships between music and language Rhythm and meter in various musical traditions Corpus-based approaches to music analysis His research demonstrates a consistent interest in applying quantitative and computational methods to understand musical structures and processes. He has pioneered corpus studies in music theory, examining large collections of musical works to identify patterns and principles that might not be evident from individual case studies. His work often bridges theoretical music analysis with empirical cognitive science approaches. Dr. Temperley has received several prestigious awards including the Society for Music Theory Emerging Scholar Award (2003), the University of Rochester Bridging Fellowship (2009), and the University of Rochester Provost's Multidisciplinary Award (2012). These awards recognize his innovative interdisciplinary approach that connects music theory with cognitive science and computational modeling. As an educator, Dr. Temperley has taught a wide range of courses in music theory, analysis, counterpoint, and music cognition at both graduate and undergraduate levels. His teaching reflects his research interests, emphasizing both traditional theoretical approaches and contemporary empirical methods. He has supervised numerous graduate students in their research, particularly in areas combining music theory with cognitive science. Dr. Temperley is also an active composer with numerous performances of his works, including chamber music for various instrument combinations and piano compositions. His compositional work informs his theoretical research and vice versa, creating a productive dialogue between creative practice and scholarly inquiry.

Frederick Soltمشاهده پروفایل

استاد

• Political Economy
• Comparative Politics
• Public Opinion
• +۳ مورد دیگر

Frederick Solt is a Professor in the Department of Political Science at the University of Iowa, specializing in comparative political behavior, public opinion, and political economy. His research focuses on how economic inequality shapes citizen attitudes and behaviors across nations, with significant contributions to cross-national methodology. His primary research interests include income inequality dynamics , comparative public opinion , and computational methods for cross-national research . Solt pioneered the Standardized World Income Inequality Database (SWIID), providing comparable inequality metrics for global research, and leads the Dynamic Comparative Public Opinion (DCPO) Lab which generates Bayesian estimates of opinion trends across thousands of surveys. Recent publications reveal consistent focus on democratic support measurement, inequality's impact on protest participation, and gender egalitarianism across cultures. His work demonstrates methodological innovation through latent variable modeling and advanced estimation techniques for cross-national comparability. Solt maintains active research leadership through the DCPO Lab and SWIID database project, developing tools that enable rigorous analysis of global inequality and opinion trends. His collaborative work spans multiple continents with frequent co-authorship patterns indicating strong international research networks.

• Ecology
• Theoretical Biology
• Population Dynamics
• +۶ مورد دیگر

Dr. Ricardo Martinez Garcia is a researcher affiliated with the CASUS - Center for Advanced Systems Understanding at the Helmholtz Center Dresden-Rossendorf (HZDR) . His work focuses on spatial ecology, population dynamics, and theoretical biology, addressing pattern formation in ecosystems and animal movement behaviors. Specialization: Spatial ecology, mathematical modeling, and eco-evolutionary dynamics Key Contributions: Frameworks for wildlife-vehicle collision risk, nonlocal interaction models, vegetation pattern analysis, and ecosystem resilience studies Contact: Email | Phone: +49 3581 37523 105 | Office: Conrad-Schiedt-Straße 20, Görlitz His recent research emphasizes spatial heterogeneity in ecological systems, with publications analyzing wildlife-vehicle collisions, vegetation patterns, and movement dynamics across fragmented landscapes. He bridges individual-level mechanisms with ecosystem-scale outcomes using computational and mathematical models.

Thomas Geyerمشاهده پروفایل

مدرس ارشد

• Behavioral Neuroscience
• Cognitive Neuroscience
• Biomedical Neuroscience
• +۶ مورد دیگر

Thomas Geyer is a Senior Lecturer in Experimental Psychology and Academic Director at LMU Munich's Department of Psychology , specializing in the interplay between cognitive and neurobiological mechanisms of statistical learning. His research focuses on how humans manage sensory overload through automatic learning processes that enhance object recognition and search efficiency. Primary Research Focus: Behavioral & Cognitive Neuroscience Secondary Focus: Biomedical Neuroscience Tertiary Focus: Theoretical Neuroscience & Technical Applications Geyer employs behavioral methods (reaction times, eye tracking) alongside neuroimaging (EEG, fMRI) to explore attentional capture, scene memory, and cross-modal interactions in visual search. His work bridges theoretical neuroscience with practical applications in human perception and memory systems. Selected Research Trends: Statistical learning in complex environments, neurobiological underpinnings of selective attention, and integration of multi-sensory contextual cues. Notable Contributions: Pioneering studies on cross-trial priming effects, N1pc polarity reversal mechanisms, and cross-modal attentional guidance through contextual memory. Collaborations with renowned researchers like Hermann J. Müller and Joseph Töllner highlight his impact in experimental psychology. Former Students: Dr. Efsun Annac Abhilash Dwarakanath Dr. Bernhard Schlagbauer Contact: geyer@lmu.de | Phone: +49 (0) 89 / 2180 5216