جست‌وجوی استاد و پژوهشگر

Branislav M. Randjelovicمشاهده پروفایل

دانشیار

• Applied Mathematics
• Discrete Mathematics
• Graph Theory
• +۴ مورد دیگر

Branislav M. Randjelovic is an Associate Professor at the University of Nis, holding positions at both the Faculty of Electronics (Department of Mathematics) and the Faculty of Teacher Education in Leposavic (Department of Mathematics Teaching Methods). He was elected to the title of Associate Professor at both institutions in 2024 and 2021 respectively, having previously held Assistant Professor positions since 2016. His academic journey began with graduation from the Faculty of Electronics in Niš in 1994, followed by a Master's degree in Applied Mathematics in 2002, and culminating with a PhD in Applied and Industrial Mathematics in 2015. His research spans multiple domains including applied mathematics, graph theory applications in materials science, fractal analysis of microelectronic structures, and educational assessment methodologies. He has made significant contributions to applying graph theory to model microstructure evolution in sintering processes and has pioneered the use of fractal analysis in microelectronic ceramic materials. In educational research, he focuses on mathematics education, curriculum development, and comparative studies of educational systems in the Balkan region. His recent publications demonstrate a strong interdisciplinary approach, merging mathematical techniques with applications in materials science, educational assessment, and decision-making methodologies. His work frequently employs fuzzy logic, analytic hierarchy processes (AHP), and graph theory to solve complex problems across these diverse fields. Dr. Randjelovic serves as the Director of the Institute for Quality Assessment of Education and Upbringing of the Republic of Serbia (2016-2025) and previously directed the Registration Center for Professional Development of Education Employees in Niš (2008-2012). His leadership roles reflect his significant contributions to educational policy and assessment in Serbia. He has authored 13 books, published 40 journal papers with impact factor, participated in 12 scientific projects, and contributed to 102 national and 17 international education projects. His extensive work demonstrates a unique bridge between advanced mathematical research and practical educational applications.

• Neuroimaging
• MRI Segmentation
• Brain Structure Analysis
• +۵ مورد دیگر

Dr. Pierre-Louis Bazin is a Researcher in the Department of Neurophysics at the Max Planck Institute for Human Cognitive and Brain Sciences. He holds a PhD from INRIA Rocquencourt (France) and conducted postdoctoral research at Brown University and Johns Hopkins University in the US. His work focuses on advanced neuroimaging techniques, including high-resolution MRI parcellation, quantitative MRI, and subcortical structure analysis. Key projects involve developing automated segmentation tools (e.g., Nighres), exploring cerebellar and subcortical anatomy, and investigating neurodegenerative markers using multi-modal approaches. Education 1995-1998: Electrical Engineering Diploma, SUPELEC (France) 1998-2001: PhD in Computer Vision & Graphics, INRIA Rocquencourt (France) 2001-2003: Postdoc in Computer Vision & Archaeology, Brown University (USA) 2003-2006: Postdoc in Neuroimaging, Johns Hopkins University (USA) Research Interests High-field MRI (7T+) applications in structural and functional brain mapping Development of neuroimaging software tools (e.g., Nighres) Subcortical and cerebellar morphometry in health and disease Quantitative MRI techniques for histology correlation Connectivity analysis in psychiatric disorders (e.g., schizophrenia) Awards Royal Netherlands Academy of Arts and Sciences (KNAW) membership (2020, 2024) Grants & Labs : Directs the Neurophysics lab at MPI-CBS. Active in large-scale projects like the Amsterdam Ultra-high field adult lifespan database (AHEAD). Collaborates on tools like MP2RAGEME for multi-parametric MRI. Labs/Teams : Leads neuroimaging method development and clinical translation efforts in Leipzig, Germany.

Shuichi Suzukiمشاهده پروفایل

استاد

• Neurointerventional Surgery
• Neuroimaging
• MRI Artifacts
• +۷ مورد دیگر

Shuichi Suzuki, MD, PhD, is a Professor of Clinical Neurological Surgery and Professor in Neurology at the University of California, Irvine School of Medicine. He serves as Medical Director of Neurointerventional Surgery and Director of the CAST Neuroendovascular Fellowship Program, highlighting his leadership in clinical, educational, and research domains within neurosurgery. His research interests focus on advanced neuroimaging techniques, particularly 3T MRI applications in patients recovering from subarachnoid hemorrhage who have implanted titanium aneurysm clips. His work investigates MRI artifact characterization across various imaging sequences and the feasibility of reconstructing white matter pathways near metallic implants. The available publication demonstrates expertise in quantitative neuroimaging analysis, including planimetric contouring, artifact volume measurement, and deterministic tractography. His research bridges clinical neurosurgery with advanced imaging physics and computational neuroscience, contributing to improved study design in post-SAH recovery research. Scientific Affiliations: Professor of Clinical Neurological Surgery, UCI School of Medicine Professor, Neurology, UCI School of Medicine Medical Director, Neurointerventional Surgery Director, CAST Neuroendovascular Fellowship Program Research Focus Areas: Neurointerventional Surgery Neuroimaging Physics MRI Artifact Quantification Diffusion Tensor Imaging Functional MRI in Neurological Recovery

Cezar Oniciucمشاهده پروفایل

استاد

• Biharmonic Maps
• Harmonic Maps
• Riemannian Geometry
• +۳ مورد دیگر

Roles & Affiliations Prof. Cezar Oniciuc is a Professor at the Department of Geometry , Faculty of Mathematics , Alexandru Ioan Cuza University of Iasi . He serves as Vice-Dean of the Faculty of Mathematics since 2012 and held editorial roles including Editor-in-Chief of Analele Stiintifice ale Universitatii "Al.I. Cuza" Iasi, Matematica . Education BSc: Mathematics, Faculty of Mathematics, 1991–1996 MSc: Mathematics, 1996–1997 PhD: "Tangency and Harmonicity Properties" (2002), supervised by Prof. V. Oproiu Habilitation: "Biharmonic Submanifolds in Space Forms" (2012) Research Interests Focuses on biharmonic and harmonic maps , Riemannian geometry , and differential geometry . Notable contributions include studies on biharmonic submanifolds, biconservative surfaces, and geometric variational problems. His work often involves analysis of hypersurfaces in space forms and pseudo-Riemannian manifolds. Recent Research Trends Recent articles explore conformal biharmonic hypersurfaces , polyharmonic maps , and unique continuation properties in geometric contexts. He investigates properties of surfaces in spheres, complex space forms, and homogeneous spaces, emphasizing geometric analysis and variational problems. Awards & Recognition PhD thesis received Ottimo distinction (highest honor) and Cum Laudae from Romania's Ministry of Education Supervised PhD students with exceptional achievements, including Simona Nistor's Excellent (Summa Cum Laude) distinction Teaching & Academic Service Teaches Differential Geometry , Manifolds , and Computational Geometry . Served on Romania's National Council of Scientific Research evaluation board (2011–2013). Organized Geometry Day conferences and regularly conducts seminars on advanced geometric topics.

Alan William Doughertyمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Medical Robotics
• +۳ مورد دیگر

Dr. Alan William Dougherty is a Lecturer at the School of Computing and Data Science, University of Hong Kong. He holds an MEng in Electronic Engineering from King’s College London (2013) and a PhD from Hong Kong Polytechnic University (2019), where he received the prestigious Hong Kong PhD Fellowship. His professional background includes engineering roles in compiler design for edge AI on FPGAs, autonomous robotic systems, and computer vision applications such as human emotion classification and 3D mesh generation from single images. Affiliations: School of Computing and Data Science (HKU) Research Interests: Computer Vision, Machine Learning, Medical Robotics, Edge AI, Autonomous Systems His research spans interdisciplinary areas including immersive online learning systems, human pose estimation using transformers, and computational material science for energy applications. Notable contributions include the SAILS platform for young learners and advanced catalyst discovery methodologies. He integrates practical engineering challenges with theoretical research to address real-world problems. Dr. Dougherty has been recognized for his work with awards such as the Hong Kong PhD Fellowship. His teaching philosophy emphasizes bridging theory and practice, fostering critical thinking in emerging tech fields.

• Algebra
• Group Theory
• Brace Theory
• +۳ مورد دیگر

Vicent Perez Calabuig is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia. His academic career is centered on advanced algebraic structures with a focus on group theory and brace theory. His primary research interests include Algebra , Group Theory , Brace Theory , Yang-Baxter Equation , and Finite Groups . His work investigates structural properties of groups, semigroups, and braces through the PERMUT research group, which examines arithmetical and structural properties with applications to theoretical mathematics. Current research emphasizes nilpotent braces, subnormality criteria, and solutions to the Yang-Baxter equation. Analysis of his 15 most recent publications (2024-2025) reveals a concentrated focus on left braces and their variants, with significant contributions to understanding nilpotency classes, subnormality in finite groups, and connections to the Yang-Baxter equation. His work bridges abstract algebra with theoretical physics applications, particularly in quantum algebra and integrable systems. His doctoral research was completed at the University of Valencia in 2018 under the supervision of Dr. Adolfo Ballester-Bolinches, with a thesis titled A reduction theorem for the generalised rhodes' type ii conjecture .

Naonori Uedaمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Artificial Intelligence
• Physics-Informed Deep Learning
• +۵ مورد دیگر

Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

Yakun Sophia Shaoمشاهده پروفایل

دانشیار

• Computer Architecture
• Specialized Accelerators
• Heterogeneous Architecture
• +۴ مورد دیگر

Yakun Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She holds a Ph.D. (2016) and M.S. (2014) in Computer Science from Harvard University, alongside a B.E. in Electrical Engineering from Zhejiang University, China. Her research focuses on computer architecture , particularly domain-specific accelerators , heterogeneous systems , and agile VLSI design methodologies . Key research centers: Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), SpeciaLIzed Computing Ecosystems (SLICE) Her work explores hardware-software co-design for efficiency in AI and robotics, including projects like Simba (chiplet-based AI accelerators) and Virgo (GPU matrix units). Notable tools developed include WIICA (workload characterization) and Chipyard (SoC frameworks). Selected honors include: 2024 CRA-WP Anita Borg Early Career Award 2023 NSF CAREER Award 2022 IEEE TCCA Young Computer Architect Award 2022 Intel Rising Star Faculty Award She teaches courses including EECS 151 (Digital Design & ICs) and EECS 251A (Advanced Digital Design).

• Computational chemistry
• Molecular modeling
• Drug discovery
• +۵ مورد دیگر

Marcus Weber serves as Head of the Computational Molecular Design research group within the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB), which operates in close affiliation with Freie Universität Berlin. His interdisciplinary work spans computational mathematics, molecular modeling, drug discovery, and unexpected connections to Egyptology, demonstrating the broad applicability of mathematical approaches across diverse scientific domains. Dr. Weber's research interests focus on the mathematical foundations of molecular simulation and drug design: Developing advanced Markov state models for complex molecular systems Creating computational methods for efficient drug discovery Applying machine learning techniques to molecular dynamics Modeling pH-dependent receptor-ligand interactions Exploring metastable dynamics in biological systems Bridging mathematical approaches with Egyptological research His recent publications reveal a sophisticated integration of computational mathematics with practical pharmaceutical applications, particularly in opioid receptor research. The work demonstrates how mathematical modeling can identify pH-dependent drug candidates that maintain efficacy while reducing side effects. His research group has developed innovative algorithms like ISOKANN for learning Koopman eigenfunctions and has made significant contributions to understanding molecular transition rates and metastable dynamics. Dr. Weber leads multiple significant research projects including 'Drug Candidates as Pareto Optima in Chemical Space,' 'HPC and ML for Drug Discovery,' and interdisciplinary collaborations connecting mathematical approaches with Egyptology. His work on 'Mathematics and Egyptology' and 'Ancient Egyptian' demonstrates the unexpected breadth of mathematical applications. His research has direct implications for developing safer opioid medications and understanding molecular behavior in complex environments.

David Kaiserمشاهده پروفایل

استاد

• History of Science
• Physics
• Cosmology
• +۴ مورد دیگر

David Kaiser is the Germeshausen Professor of the History of Science in MIT's Program in Science, Technology, and Society, Professor of Physics in MIT's Department of Physics, and Associate Dean for Social and Ethical Responsibilities of Computing (SERC) in the Schwarzman College of Computing. He holds an A.B. in Physics from Dartmouth College and dual Ph.D.s in Physics and History of Science from Harvard University. His research bridges history and physics: he investigates the Cold War's impact on physics development, and explores early-universe cosmology, particle physics, gravitation, and quantum foundations. He co-designed the 'Cosmic Bell' quantum entanglement experiments, featured in the PBS documentary Einstein's Quantum Riddle . Recent publications emphasize interdisciplinary trends—quantum history and primordial black holes dominate—with physics articles focusing on inflationary models and quantum tests, while history works analyze scientific societal impacts. Awards highlight his dual expertise: MacVicar Faculty Fellow (MIT's top teaching honor, 2012) APS Fellow (2010) and LeRoy Apker Award (1993) Book prizes: Physics World Book of the Year (2012) and Best of Physics (2020) History of Science Society's Davis Prize (2013) and Pfizer Prize (2007) He leads a cosmology research group, advises graduate students (Perkins Award, 2012), and directs initiatives on computing ethics as MIT Press Editorial Board Chair.

Alessandra Silvestriمشاهده پروفایل

استاد

• Cosmology
• Theoretical Physics
• Dark Energy
• +۳ مورد دیگر

Alessandra Silvestri is a Professor of Theoretical Physics at the Leiden Institute of Physics (LION) within Leiden University's Faculty of Science. Her research focuses on cosmology, particularly on testing fundamental physics through cosmological observables, with an emphasis on modified theories of gravity and dark energy dynamics. Affiliation: Leiden Institute of Physics (LION), Faculty of Science, Leiden University Research Interests: Cosmology, Modified Gravity, Dark Energy, Large-scale Structure, Gravitational Waves Cosmology Her group is actively involved in the ESA Euclid mission and has initiated a Gravitational Waves Cosmology program with GRAPPA (UvA). She leads the development of the EFTCAMB and EFTCosmoMC software for analyzing dark energy and modified gravity theories against cosmological data. Scientific Awards: ERC Consolidator Grant (2024): Putting gravity to the test on cosmological scales Notable Past Members: Matteo Martinelli, Yashar Akrami, Simone Peirone, Georgios Papadomanolakis, Francesca Gerardi, and others who have transitioned to academic or industry roles. For detailed information on her research, publications, and collaborations, visit her lab's website at the Instituut Lorentz for Theoretical Physics.

• Quantum Information Science
• Quantum Computing
• Quantum Algorithms
• +۵ مورد دیگر

Jordi Tura Brugués is an Associate Professor at the Leiden Institute of Physics (LION), Leiden University, and a Principal Investigator in the Applied Quantum Algorithms group. His research focuses on quantum algorithms, entanglement theory, and near-term quantum devices. Quantum Information Science Quantum Computing Tensor Networks Quantum Machine Learning He has received notable awards including the Heineken Young Scientists Award (2022) and the Young Scientist Prize (2022). His academic genealogy includes supervision of PhD candidates like Eloïc Vallée and Albert Aloy López. His work is supported by an ERC Starting Grant and previously by a Marie Skłodowska-Curie fellowship. Beyond academia, he engages in programming, algorithm competitions, and orchestral violin performance.

Daniel Grierمشاهده پروفایل

استادیار

• Quantum Complexity Theory
• Quantum Computing
• Quantum Algorithms
• +۳ مورد دیگر

Daniel Grier is an Assistant Professor at the University of California, San Diego in the Computer Science and Engineering and Mathematics departments. His research focuses on quantum complexity theory , particularly near-term quantum computing paradigms and proving quantum advantage over classical systems. PhD in Computer Science from MIT under Scott Aaronson Postdoc at the Institute for Quantum Computing (University of Waterloo) B.S. in Computer Science and Mathematics from University of South Carolina His work spans quantum algorithms , complexity theory , and quantum simulation , with notable contributions to BosonSampling , Clifford circuits , and classical shadow tomography . He has developed open-source tools like gridCHP++ , a C++ stabilizer simulator for planar quantum circuits. Publications highlight collaborations with researchers including Scott Aaronson , David Gosset , and Luke Schaeffer , covering topics such as quantum query complexity , quantum simulation efficiency , and quantum-classical separations .

Feliks Nüskeمشاهده پروفایل

استاد پژوهشی

• Data-driven modeling
• Molecular simulation
• Koopman operator theory
• +۶ مورد دیگر

Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.