جستجوی استاد راهنمای دانشگاهی

Patricio G. Herbstمشاهده پروفایل

استاد

• Mathematics Education
• Geometry Education
• Teacher Professional Development
• +۳ مورد دیگر

Patricio G. Herbst is a Professor in the Marsal Family School of Education and the Department of Mathematics at the University of Michigan. He holds dual roles as a professor of education and mathematics, bridging both the College of Literature, Science, and the Arts and the School of Education. His research focuses on understanding the professional work of mathematics teachers and university instructors, with an emphasis on teacher knowledge, decision-making, and the design of online learning environments. Key areas of interest include geometry education, mathematical proof, and the development of technologies such as Anotemos and LessonDepict for learning from video and storyboarding. Herbst has led numerous grants and projects, including 'GRIP: Mathematics Grasping the Rationality of Instructional Practice,' which explores geometry teaching and teacher training. He has also contributed to initiatives like the 'LessonSketch' platform and 'GeT Support' for teacher professional development. His work spans international collaborations and curriculum development efforts in secondary mathematics education. Herbst’s research publications emphasize the integration of theoretical frameworks with practical classroom applications, particularly in geometry instruction and teacher cognition. His book, *The Learning and Teaching of Geometry in Secondary Schools: A Modeling Perspective*, underscores his commitment to advancing pedagogical models in mathematics education. He is affiliated with the GRIP lab, a research group dedicated to studying geometry, reasoning, and instructional practices. His contributions to mathematics education include designing interventions, analyzing teacher rationality, and advancing the use of multimedia tools in teacher training.

Donca Steriadeمشاهده پروفایل

استاد

• Phonology
• Morphology
• Historical Linguistics
• +۵ مورد دیگر

Donca Steriade serves as the Class of 1941 Professor of Linguistics in the Department of Linguistics and Philosophy at the Massachusetts Institute of Technology (MIT), where she also holds the position of Communications Director for the linguistics section. Her research centers on phonology, morphology, and historical linguistics with specialized focus on Greek and Latin. Steriade has pioneered theoretical frameworks including the Perceptibility map (P-map) for understanding phonological markedness, while her morphological work explores paradigm uniformity, morphomes, and cyclicity across diverse languages. She integrates empirical data from classical languages with cutting-edge phonological theory. Steriade's recent publications (2015-2025) reveal consistent exploration of quantitative meters in Greek/Latin poetry, morphophonological cyclicity in Romanian, and paradigm structure in Sanskrit reduplication. Her work demonstrates sophisticated interplay between phonological constraints and morphological processes, often revealing universal patterns through cross-linguistic analysis of classical and modern languages. As Communications Director, she actively shapes the department's academic outreach and scholarly discourse within the linguistics community.

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

Shahrzad Esmaeiliمشاهده پروفایل

استاد

• Metallic Biomaterials
• Phase Transformations
• Surface Modifications
• +۷ مورد دیگر

Shahrzad Esmaeili is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. She holds a PhD in Materials Engineering from the University of British Columbia (2002), and master’s and bachelor’s degrees in Materials Science and Engineering from Shiraz University (1988, 1980). Her research focuses on processing-structure-property relationships in light alloys, metallic biomaterials, and additive manufacturing. She has expertise in phase transformations, surface modifications, and multi-length scale characterization. Notably, she received an Early Researcher Award from the Ontario Ministry of Research and Innovation. Her work bridges experimental and computational methods to study microstructural phenomena in aluminum and magnesium alloys. Recent publications emphasize non-isothermal annealing, precipitation hardening, and bio-structure fabrication. Education: PhD, Materials Engineering, University of British Columbia (2002) MSc, Materials Science and Engineering, Shiraz University (1988) BSc, Materials Science and Engineering, Shiraz University (1980) Research Interests: Her work integrates experimental and modeling approaches to study nanostructured materials, including metallic biomaterials and light alloys. Key areas include: Precipitation hardening mechanisms in Al-Mg-Si and Mg-Zn alloys Surface functionalization via laser-assisted deposition Additive manufacturing of porous titanium bio-structures Thermal-mechanical processing of aluminum composites Publications: Over 100 peer-reviewed articles span microstructural analysis, alloy behavior under thermal treatments, and biomedical applications. Recent trends focus on non-isothermal processing effects, microalloying strategies, and advanced surface modification techniques. Awards: Early Researcher Award (Ontario Ministry of Research and Innovation) Grants & Collaboration: Her research involves interdisciplinary collaborations, though specific grants are not detailed here. She leads studies on novel processing routes for high-performance alloys and biomaterials. Labs/Teams: Active in materials characterization and computational modeling groups at the University of Waterloo, focusing on multi-scale material analysis.

Xiaowei Chenمشاهده پروفایل

دانشیار

• Earthquake Rupture
• Induced Seismicity
• Subsurface Structure
• +۳ مورد دیگر

Xiaowei Chen is an Associate Professor in the Department of Geology and Geophysics at Texas A&M University. His research focuses on observational seismology, with an emphasis on earthquake rupture processes, induced seismicity, subsurface structure analysis, and applications of distributed acoustic sensing (DAS). He holds a PhD from the University of California, San Diego (2013), and has held prior academic positions including the Stubbeman-Drace Presidential Professorship at the University of Oklahoma (2020). His work integrates field observations, dense seismic arrays, and advanced computational methods to address critical questions in crustal dynamics and seismic hazard assessment. **Education:** PhD, University of California, San Diego, 2013 MS, University of California, San Diego, 2010 BS, University of Science and Technology of China, 2007 **Research Interests:** Chen investigates how anthropogenic activities influence fault behavior, interactions between seismic and aseismic slip, and factors controlling earthquake rupture characteristics. He studies these phenomena in tectonically active regions (e.g., western US, Japan) and intraplate settings (e.g., Oklahoma), leveraging DAS technology for high-resolution subsurface imaging. Recent projects include forecasting induced seismicity via machine learning and analyzing pore-pressure diffusion mechanisms in Oklahoma. **Awards:** Stubbeman-Drace Presidential Professor, University of Oklahoma (2020) Editor's citation for excellence in refereeing, JGR-Solid Earth (2018) **Advising & Grants:** While no formal advisees are listed, Chen’s collaborative research involves multidisciplinary teams addressing induced seismicity, fault dynamics, and crustal structure. His work is supported by grants from agencies like the USGS and SCEC. **Labs & Teams:** Engages with the Texas A&M Seismology Group and collaborates with institutions like the University of Oklahoma and the University of California system on projects involving seismic array deployments and DAS applications.

Abbas Edalatمشاهده پروفایل

استاد

• Computer Science
• Mathematics
• Exact Computation
• +۷ مورد دیگر

Abbas Edalat is a Professor of Computer Science and Mathematics at Imperial College London, and an Adjunct Professor at the Institute for Research in Fundamental Sciences, Tehran. He leads two research groups: Algorithmic Human Development and Continuous Data-Types and Exact Computation. His work spans computational mathematics, psychotherapy models, and exact real-number computation. Notably, he received the LICS 2017 Test-of-Time Award for foundational contributions to logic in computer science. Research interests include self-attachment psychotherapy, computational differential calculus, topology, and bisimulation in probabilistic systems. He has pioneered exact computation frameworks for real numbers, geometry, and dynamical systems, with applications in neuroscience and artificial intelligence. Professional activities include keynote talks at conferences like IJCNN and workshops on psychotherapy in Iran and the UK. Teaching includes advanced courses on dynamical systems, quantum computing, and computational techniques. He advises PhD students globally and chairs initiatives like the Science and Arts Foundation to expand educational access in developing nations. His interdisciplinary work bridges mathematics, computer science, and clinical psychology.

Minglun Gongمشاهده پروفایل

استاد

• Visual Computing
• Computer Graphics
• Computer Vision
• +۶ مورد دیگر

Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

Życzkowski Karolمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Chaos
• Random Matrices
• +۷ مورد دیگر

Karol Życzkowski is a Professor of Physics at the Smoluchowski Institute of Physics, Jagiellonian University, Cracow, Poland, and also holds a position at the Center for Theoretical Physics, Polish Academy of Sciences, Warsaw. He has held visiting positions at the Perimeter Institute for Theoretical Physics (2004/05) and previously served as Director of the Quantum Information Center in Gdańsk (2019-2023). Since January 2023, he has been President of the Cracow Branch of the Polish Academy of Sciences. Education Ph.D. in Theoretical Physics (1987, Jagiellonian University) Habilitation in Physical Sciences (1994, Jagiellonian University) Życzkowski’s research spans Quantum Information Theory , with a focus on quantum entanglement, geometry of quantum states, random matrices, and operator theory. His work bridges theoretical physics, mathematics, and interdisciplinary applications, including voting systems (e.g., Jagiellonian Compromise) and scientometrics. He has authored or co-authored over 150 papers and the influential monograph Geometry of Quantum States (Cambridge University Press, 2006, second edition 2017). His recent publications emphasize quantum thermodynamics (e.g., Jarzynski equality for stochastic maps), entanglement detection via uncertainty relations, and mathematical structures in quantum theory. Collaborations span 134 scientists from 19 countries, and his work has been cited over 6500 times (Google Scholar) with an H-index of 40. Scientific Awards Award of Rector of Jagiellonian University (2013) for work on quantum information foundations Award of Rector of Jagiellonian University (2007) for the monograph Geometry of Quantum States Prime Minister Award (1995) for habilitation thesis Humboldt Fellowship (1989), Fulbright Fellowship (1997/98) Życzkowski has supervised 5 Ph.D. and 16 Master’s theses, with 3 Ph.D. students currently in progress. He coordinated 9 research grants, participated in 10 others, and led the Polish node of a German SFB-Transregio project. He serves on editorial boards for journals including Open Systems & Information Dynamics and Journal of Physics A . He co-organized workshops (including Oberwolfach and Marseille) and founded research networks like the European Research Network Krzyżowa Initiative for Quantum Information. His Jagiellonian Quantum Information Team (JQIT) and involvement in the Mark Kac Complex Systems Research Centre highlight his collaborative leadership.

Mihyun Kangمشاهده پروفایل

استاد

• Combinatorics
• Discrete Probability
• Algorithms
• +۳ مورد دیگر

Mihyun Kang is a Professor in the Institute of Discrete Mathematics at Graz University of Technology (TU Graz), leading the Combinatorics Group. She holds significant academic positions and has been recognized with a Heisenberg Fellowship (German Research Foundation) and a Friedrich Wilhelm Bessel Research Award (Alexander von Humboldt Foundation). Her research focuses on combinatorics, discrete probability, and algorithms, with a specialization in random graph theory. She contributes to editorial boards, including Random Structures & Algorithms . Her research interests emphasize high-dimensional graphs, percolation processes, and structural properties of random graphs. She explores topics such as phase transitions, graph enumeration, and algorithmic applications in combinatorial problems. Her work bridges theoretical foundations with practical applications in algorithm design and probabilistic modeling. Dr. Kang’s scientific achievements include pioneering studies on hypergraphs, bootstrap percolation, and the evolution of random graph processes. Her recent publications highlight advancements in understanding graph components, connectivity thresholds, and universality phenomena in random structures. She collaborates widely, contributing to conferences and international initiatives like the SFB “Discrete random structures: enumeration and scaling limits.” Her professional activities include advising doctoral students and supervising research projects at TU Graz. She leads the Combinatorics Group, which hosts the Graz Combinatorics Seminar and actively engages in academic outreach. Her contributions extend to textbook authorship, including Diskrete Mathematik für die Informatik , and she maintains an active presence in discrete mathematics education and research.

David E. Breenمشاهده پروفایل

استاد

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +۱۲ مورد دیگر

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

Victor Galitskiمشاهده پروفایل

استاد

• Quantum Chaos
• Theoretical Physics
• Quantum Information Science
• +۷ مورد دیگر

Victor Galitski is a Professor of Physics at the University of Maryland and a Fellow of the Joint Quantum Institute (JQI). He holds two PhDs in applied mathematics and condensed matter physics, joined UMD in 2002 as a postdoc, and became a faculty member in 2005. His research focuses on theoretical physics and quantum information science, with notable contributions to quantum chaos, superconductivity, and topological materials. He co-founded Aspen Quantum Consulting and ScienceCast.org, and serves as an Honorary Professor at Monash University and an editor for Annals of Physics . Galitski’s educational efforts include authoring ' Exploring Quantum Mechanics ' (Oxford Press) and teaching a Coursera MOOC on quantum physics (enrolled by 200,000+ students). His research interests span quantum spin glasses, hydrodynamic turbulence, Floquet topological insulators, and cavity quantum electrodynamics. Over 20 former students and postdocs now hold academic or industry leadership roles. Key scientific contributions include studies on Many-Body Quantum Chaos, quantum spin ice in Rydberg atom arrays, and the interplay of symmetry breaking in vertex models. His work frequently explores connections between quantum systems and machine learning, such as neural networks’ analogies to spin glass behavior. Galitski’s affiliations include leadership roles in the Ultra-Quantum Matter Simons Collaboration, the Institute for Robust Quantum Simulation, and the Aspen Center for Physics. He has published extensively on topics like cavity-enhanced superconductivity and quantum ergodicity, with recent work addressing universal speed limits in quantum systems and interacting anomaly effects in thermal transport.

Tina Pericaمشاهده پروفایل

استادیار

• Biochemistry
• Systems Biology
• Protein Biochemistry
• +۵ مورد دیگر

Prof. Tina Perica is an Assistant Professor (tenure-track) in the Department of Biochemistry at the University of Zurich, joining in July 2021. Her research focuses on understanding how biochemical properties of proteins encode systems-level functions in signal transduction and gene regulation. Her lab integrates protein biochemistry, biophysics, functional genomics, and computational biology to study cellular regulation mechanisms. Education: B.Sc. in Biology, University of Zagreb (2008) Ph.D. in Biochemistry, University of Cambridge (2013) Postdoctoral training at University of California, San Francisco (2013–2021) Research Interests: Systems Biochemistry Allosteric Regulation in GTPases and Kinases Genotype-to-Phenotype Mapping Functional Genomics and Computational Biology Her team explores how molecular mechanisms of proteins interact within cellular networks to regulate complex processes, aiming to predict therapeutic effects and disease mutations. Lab Activities: Develops experimental and computational tools to map functional interactions in signaling pathways Focuses on targeted perturbations of proteins to study systems-level effects Emphasizes interdisciplinary approaches blending biochemistry with systems biology

Jing Fuمشاهده پروفایل

دانشیار

• Nanoengineering
• Biomedical Applications
• Focused Ion Beam (FIB) Technology
• +۷ مورد دیگر

Dr. Jing Fu is an Associate Professor in the Department of Mechanical & Aerospace Engineering at Monash University. He holds a PhD in nano/microfabrication processes for biomedical applications from Pennsylvania State University (2008). His research focuses on nanoengineering tools, particularly Focused Ion Beam (FIB) technology for imaging and manipulating single cells. He is a principle scientist in collaborative projects with CSIRO MCN and Australian Synchrotron, exploring nanomaterial dynamics in immune cells. His expertise spans FIB/SEM/TEM, cryogenic environments, and multidisciplinary biomedical engineering. Education: M.Eng/Ph.D., Pennsylvania State University, USA (2008). Postdoctoral Fellowship at NIH (2008–2010). Joined Monash Faculty of Engineering in 2010. Research Interests: 3D visualization of HIV viral entry, compositional mapping of mammalian cells, and FIB-driven correlative imaging. Projects include '3D Cryo-FIBSEM Imaging Facility' (2015–2017) and 'Targeting NDM-producing superbugs' (2013–2015). Current collaborations involve tooth enamel evolution studies (2025–2028). Key Contributions: Over 90 publications, including work on graphene encapsulation for APT, ion beam fabrication of nanostructures, and polymyxin antibiotic efficacy. His research aligns with UN SDGs for health and innovation. Grants & Awards: ARC and NHMRC funding for projects on superbug targeting and imaging facilities. Active in multidisciplinary teams addressing biomedical challenges.

Elisa Perroneمشاهده پروفایل

استادیار

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +۴ مورد دیگر

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.