جست‌وجوی استاد و پژوهشگر

Dmitri Netchaevمشاهده پروفایل

دانشیار

• Physical Oceanography
• Numerical Modeling
• Coastal Dynamics
• +۳ مورد دیگر

Dr. Dmitri Netchaev is an Associate Professor at the University of Southern Mississippi (USM), based at the Stennis Space Center. He specializes in physical oceanography and numerical modeling, teaching advanced courses such as MAR 667: Waves and Tides, MAR 670: Coastal Phys. Oceanography, and MAR 675: Data Assimilation into Ocean Models. His research focuses on coastal dynamics, numerical methods, and hydrodynamic processes in curvilinear coordinate systems. Education: PhD in Physical Oceanography from the Shirshov Institute of Oceanology (1989) and MS from the Moscow Institute of Physics and Technology (1983). He is fluent in English (Professional level) and Russian (Native). Recent research emphasizes ensemble-based numerical model linearization, wind and Loop Current impacts on sea level gradients in the Mississippi Bight, and tensor analysis of oceanographic differential operators. His work bridges theoretical advancements in mathematical physics with applied coastal modeling challenges. No scientific awards are explicitly listed in the profile. Teaching spans USM's Marine Science and Hydro programs, with courses tailored for graduate students in coastal processes and numerical methods.

Johan Uganderمشاهده پروفایل

دانشیار

• Social Networks
• Social Systems
• Causal Inference
• +۵ مورد دیگر

Johan Ugander is an Associate Professor of Management Science & Engineering (MS&E) at Stanford University, affiliated with the School of Engineering. He is also a member of the Social Algorithms Lab (SOAL), the Institute for Computational & Mathematical Engineering (ICME), and the Center for Computational Social Science. Prior to Stanford, he held a postdoctoral position at Microsoft Research and was affiliated with Facebook's Data Science team. He received his Ph.D. in Applied Mathematics from Cornell University (2014), with additional degrees from the University of Cambridge, Lund University, and Deep Springs College. His research focuses on algorithmic and statistical frameworks for analyzing social networks, causal inference, and large-scale data. Key areas include graph theory, machine learning, and network experimentation. He has developed methodologies for seed set expansion, causal inference under network interference, and scalable computational tools for social systems analysis. Ugander teaches courses such as Networks (MS&E 135), Social Algorithms (MS&E 231), and Data Privacy & Ethics (MS&E 234). He has advised numerous Ph.D. students and postdocs in areas spanning computational social science, network science, and machine learning. His work has been supported by NSF, ARO, Cisco, and the Hellman Foundation. Notable contributions include foundational studies on social contagion, graph partitioning, and the analysis of Facebook's social graph structure. He is on sabbatical at Yale University for 2024–25.

Mina Jamshidi Idajiمشاهده پروفایل

پژوهشگر

• AI for Healthcare
• Computational Pathology
• Ophthalmology
• +۳ مورد دیگر

Mina Jamshidi Idaji is a Postdoctoral Researcher at the Machine Learning Group, BIFOLD – Berlin Institute for the Foundations of Learning and Data, Technical University of Berlin. Her research focuses on AI-driven healthcare applications, including computational pathology, ophthalmology, and biomedical sensing. She holds a Dr.-Ing. (PhD) in Machine Learning from TU Berlin and the Max Planck Institute CBS, Leipzig (2022), an M.Sc. in Biomedical Engineering from Sharif University of Technology (2016), and dual B.Sc. degrees in Electrical Engineering and Mathematics from Isfahan University of Technology (2014). Her research interests span AI for healthcare, computational pathology, and biomedical engineering. Recent work includes developing explainable AI frameworks for histopathology (xMIL), EEG/MEG simulation tools (MEEGsim), and methods for nonlinear interaction decomposition (NID). She has contributed to open-source projects like Harmoni and xMIL, addressing challenges in neural data analysis and clinical AI applications. Mina’s publications highlight advancements in brain-computer interfaces (BCI), motor imagery studies, and neuroimaging techniques. Her work bridges machine learning and clinical neuroscience, emphasizing translational research in healthcare AI.

Katharina Jochemkoمشاهده پروفایل

دانشیار

• Geometric Combinatorics
• Discrete Geometry
• Algebraic Combinatorics
• +۳ مورد دیگر

Katharina Jochemko is an Associate Professor in the Department of Mathematics at KTH Royal Institute of Technology. She holds a PhD (summa cum laude) from Freie Universität Berlin and a Diplom in Mathematics (with distinction) from LMU Munich. Her research focuses on geometric and algebraic combinatorics, discrete geometry, and the geometry of polynomials, with recent explorations into applications in artificial intelligence. She leads multiple research projects funded by prestigious organizations including the Wallenberg AI, Autonomous Systems and Software Program and the Swedish Research Council. Her academic contributions include groundbreaking work on Ehrhart polynomials, lattice polytopes, and syzygy bundles, as evidenced by her extensive publication record and the Best Paper Award 2018 from TU Wien. She actively supervises PhD students and postdocs, with current advisees including Jon Pål Hamre and Aryaman Jal. She serves as an associate editor for Beiträge zur Algebra und Geometrie and represents the Swedish Mathematical Society at institutional levels. Her teaching includes advanced courses in enumerative combinatorics and graph theory at KTH. She co-organizes international research initiatives like the Polytopes and Friends conference and the MaP-Trop seminar series on matroids and tropical geometry. Her research spans foundational work in geometric combinatorics with interdisciplinary applications.

Daniela Saadehمشاهده پروفایل

پژوهشگر ارشد

• Cosmology
• Astrophysics
• Gravity Theories
• +۵ مورد دیگر

Dr. Daniela Saadeh is a Research Fellow at the Institute of Cosmology & Gravitation , part of the Faculty of Technology at the University of Portsmouth. Her research focuses on testing theories of gravity through observational data, including studies of cosmic isotropy and modified gravity models. She has contributed to groundbreaking work demonstrating the universe's isotropy and advancing numerical methods for gravity screening. Research Interests: Her work spans cosmology and astrophysics, with a particular emphasis on theoretical and observational intersections. Key areas include modified gravity theories, Vainshtein screening mechanisms, cosmic microwave background analysis, and numerical simulations. She has also explored applications in materials science and biophysics, such as crystal growth and thermal emission studies. Publications Trends: Her recent articles (2021–2025) emphasize computational methods for gravity testing (e.g., emulators and finite-element analysis) and nonlinear dynamics in modified gravity. Earlier work (2009–2018) includes foundational studies on isotropy and interdisciplinary topics like protein aggregation. Labs/Teams: She is affiliated with the Institute of Cosmology & Gravitation, collaborating on projects involving international teams and advanced cosmological modeling tools.

• Neurotrauma
• Traumatic Brain Injury
• Functional Magnetic Resonance Imaging
• +۳ مورد دیگر

Zhifeng Kou is an Associate Professor in the Department of Bio-Medical Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on advanced neuroimaging techniques, particularly in understanding neurotrauma, traumatic brain injury (TBI), and brain network dynamics. Key areas include functional MRI, susceptibility weighted imaging, and arterial spin labeling methods. He has led projects such as the 2015 Federal Grant-funded study on assessing brain tissue viability post-TBI using susceptibility mapping. His work integrates clinical applications, such as 3D augmented reality for surgical planning, and AI-driven models like the Segment Anything Model (SAM) for brain tumor segmentation. Collaborations span institutions globally, with notable contributions to studies like the characterization of microstructural brain abnormalities in cirrhotic patients. His research has been featured in journals like World Neurosurgery and Brain Imaging and Behavior , and he has contributed to textbooks on TBI neuroimaging. Kou’s work bridges biomedical engineering with clinical neuroscience, emphasizing translational applications in diagnostics and surgical planning.

• Magnetoencephalography (MEG)
• MRI
• Traumatic Brain Injury
• +۳ مورد دیگر

Dr. Mingxiong Huang is a Professor in the Department of Radiology at the University of California, San Diego (UCSD), and an Adjunct Professor in UCSD's Department of Electrical and Computer Engineering. He also serves as a career research health scientist and physicist at the VA San Diego Healthcare System, and Co-Director of the UCSD MEG Center. He holds a PhD in Biomedical Physics from Northeastern University and completed postdoctoral training in MEG at Los Alamos National Laboratory. His expertise lies in advanced MEG source-imaging techniques, integrating MEG with structural/functional brain imaging (e.g., DTI), and studying neurological and psychiatric disorders like TBI, PTSD, and schizophrenia. Education: B.S. Physics (Nankai University, 1985), M.S. Applied Physics (Nankai University, 1988), M.S. Physics (Northeastern University, 1993), Ph.D. Biomedical Physics (Northeastern University, 1995), Postdoc in MEG (Los Alamos National Lab, 1997). Research focuses on MEG applications in neurological disorders, including resting-state signals and evoked responses to sensory stimuli. Over 90 peer-reviewed publications highlight his work on MEG-based biomarkers for TBI, PTSD, and schizophrenia. Key awards include the 2013 Distinguished Investigator Award from the US Academy of Radiology Research. His research has advanced MEG imaging methods, such as fast L1 minimum-norm source modeling and dual-core beamformers. Collaborations span military and civilian patient studies, emphasizing translational imaging for TBI diagnosis and cognitive remediation. The UCSD MEG Center plays a pivotal role in his work.

Gerhard Starkeمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
• +۷ مورد دیگر

Gerhard Starke is a Professor of Numerical Mathematics at the Faculty of Mathematics, University of Duisburg-Essen. He leads the Numerical Mathematics workgroup, focusing on the development and analysis of finite element methods for solving complex mathematical and mechanical problems. His research spans multiple areas of computational mathematics with applications in solid mechanics and optimization. Professor Starke's research interests center around numerical analysis of finite element methods , reconstruction-based a posteriori error estimators , and mixed finite element methods . His work has significant applications in shape optimization , solid mechanics , hyperelasticity , plasticity , and frictional contact problems . His research combines theoretical mathematical analysis with practical computational approaches to solve challenging engineering problems. Analysis of Professor Starke's recent publications (2017-2025) reveals a strong focus on stress-based methods, shape optimization, and advanced finite element formulations. His work demonstrates a consistent progression from fundamental numerical analysis to increasingly complex applications in solid mechanics. Key trends include the development of constrained first-order system least mean approximation for shape optimization, superconvergent DPG approximations in linear elasticity, and stress-based methods for variational inequalities in contact mechanics. Professor Starke has secured significant third-party funding for his research, including current projects funded by the German Research Foundation (DFG) within Priority Programs 1748 and 1962. These projects focus on stress approximation in hyperelastic material models and stress-based methods for variational inequalities in solid mechanics. His research collaborations span multiple institutions including Humboldt University Berlin, University of Duisburg-Essen's Institute of Mechanics, and international partners in Switzerland. Head of Numerical Mathematics workgroup at University of Duisburg-Essen Principal investigator for multiple DFG-funded projects since 1999 Supervisor for numerous doctoral students including Laura Hetzel, Henrik Schneider, and Kemal Suntay Extensive international collaborations with researchers across Europe The Numerical Mathematics workgroup led by Professor Starke maintains active collaborations with mechanics researchers at the University of Duisburg-Essen and international institutions. The group has participated in significant research initiatives including the DFG Research Training Group 615 and multiple Priority Programs. Current work focuses on applying advanced numerical methods to challenging problems in computational solid mechanics and shape optimization.

• Deep Learning
• Bayesian Statistics
• Nonparametric Methods
• +۳ مورد دیگر

Zuofeng Shang is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on advanced statistical methodologies, including Bayesian statistics, nonparametric learning, and deep learning applications for complex data. He has received multiple National Science Foundation grants for projects addressing statistical guarantees in massive data analysis, sleep disorder management, and scalable learning algorithms. Shang's work spans theoretical foundations (e.g., minimax optimality, spline smoothing) and applied domains (e.g., financial data integration, ODE calibration via neural networks). His collaborations include interdisciplinary projects with medical researchers to improve obstructive sleep apnea management. Notable contributions include developing fast tensor completion algorithms and deep learning frameworks for functional data analysis. He has authored over 45 peer-reviewed publications and actively participates in academic outreach, as evidenced by media coverage of his research advancements in statistical methodologies.

Peter Symondsمشاهده پروفایل

استاد

• Algebra
• Geometry
• Group Theory
• +۵ مورد دیگر

Peter Symonds is a Professor of Mathematics at the University of Manchester's School of Mathematics. His research focuses on the intersection of algebra and geometry, particularly involving group actions, representation theory, and cohomology. He actively supervises postgraduate students, including Matthew Antrobus, Jiacheng Tang, and Greg Kendall. His work explores stable categories for infinite groups, group actions on rings and varieties, and cohomological properties of profinite groups. He contributes to academic communities through participation in the Manchester Algebra Seminar, Manchester Geometry, Topology, and Mathematical Physics Seminar, and the Transpennine Topology Triangle (TTT). His research has been published in leading journals such as the Journal of the American Mathematical Society and Advances in Mathematics. Peter's advising spans over two decades, with notable alumni including Rudradip Biswas (PhD 2021) and John MacQuarrie (PhD 2009). His research emphasizes theoretical frameworks with applications in algebraic geometry and representation theory.

Adelina MANEAمشاهده پروفایل

مدرس

• Differential Geometry
• Riemannian Geometry
• Mathematical Education
• +۳ مورد دیگر

Adelina MANEA is a Lecturer at the Department of Mathematics and Informatics, Faculty of Mathematics and Computer Science, Transilvania University of Brașov. Her research focuses on differential geometry, particularly metallic Riemannian structures, gauge theory on contact metric manifolds, and geometric structures on tangent bundles. She also investigates mathematics education, including training methods for Olympic students and student profiling in mathematics competitions. Her work spans theoretical and applied aspects of geometry with interdisciplinary connections to mathematical physics and number theory. Research interests prominently feature differential geometry with a focus on Riemannian manifolds, para-Norden structures, and geometric integrability. She explores both pure mathematical topics like cohomology groups and foliation theory, as well as applied areas such as harmonic numbers and integer sequences. Her educational publications address pedagogical strategies for high-achieving students in mathematics competitions. Publications span diverse geometric topics including metallic structures, contact metric manifolds, and jet bundle theory. Over 20 articles since 2001 reflect sustained contributions to geometry and education. No scientific awards are mentioned. Adelina MANEA’s advising and grant activities remain unspecified in available records. She is affiliated with the Department of Mathematics and Informatics at Transilvania University, contributing to both research and teaching responsibilities.

Serdar Bozdagمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۵ مورد دیگر

Serdar Bozdag is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, with a joint appointment in Mathematics. He serves as Director of the Center for Computational Life Sciences, focusing on interdisciplinary research at the intersection of computational methods and biological systems. His research expertise spans machine learning applications in bioinformatics, multi-omics data integration, and network biology. Key areas include developing algorithms for disease biomarker discovery, integrating genomic, transcriptomic, and clinical data through graph neural networks, and creating computational tools for precision medicine. Notable contributions include frameworks like CanMod2 for co-regulatory module inference and CTDPathSim for pathway-based similarity analysis. Bozdag’s work bridges computer science and life sciences, addressing challenges in Alzheimer’s disease diagnosis, cancer systems biology, and plant-microbe interactions. He has pioneered methods for analyzing dynamic biological networks and temporal data patterns, with applications in drug response modeling and electronic health record analysis. His lab develops open-source software tools widely used in computational biology research. Current projects emphasize AI-driven approaches for multi-modal healthcare data fusion, predictive modeling of disease progression, and scalable solutions for big biological data. His research has implications for improving diagnostic accuracy, personalized treatment strategies, and understanding complex biological systems.

Laura Nicole Silversteinمشاهده پروفایل

استادیار

• Convex Geometry
• Discrete Geometry
• Ehrhart Theory
• +۱ مورد دیگر

Laura Nicole Silverstein is an Assistant Teaching Professor in the Department of Mathematics at Rutgers University. Her research explores convex and discrete geometry, focusing on tensor valuations in Ehrhart theory and combinatorial aspects of algebraic statistics. She has taught courses including Elementary Mathematics from an Advanced Standpoint, Precalculus, and Discrete Mathematics at institutions like Brooklyn College and NYU. Recent publications advance tensor polynomial theories applicable to lattice polytopes. Her scholarly activities include invited presentations at Joint Mathematics Meetings and international conferences on geometric combinatorics. Research stays include collaborations at TU Berlin on discrete geometry problems.

David Tewodroseمشاهده پروفایل

استاد

• Geometric Analysis
• Metric Measure Spaces
• Ricci Curvature
• +۴ مورد دیگر

David Tewodrose is a Professor in the Department of Mathematics and Data Science at the Vrije Universiteit Brussel (Belgium). His research focuses on geometric analysis, particularly studying metric measure spaces obtained as Gromov-Hausdorff limits of Riemannian manifolds under curvature constraints. He leads the Odysseus Programme titled Geometric and Analytic Properties of Metric Measure Spaces with Spectral Curvature Constraints, with Applications to Manifold Learning , funded by FWO (2023–2028). This program explores geometric evolution problems, spectral optimization, and manifold learning algorithms. He teaches differential geometry and measure theory in the VUB Bachelor program, and Riemannian geometry in the VUB-UAntwerpen Master program. Previously, he instructed analysis courses in the International Bachelor Ygrec program and supervised student blog projects on societal data science issues. His research team includes PhD candidates Nicolo' Cont and Manuel Dias , along with postdoctoral researcher Susovan Pal . His work bridges geometric analysis with applications in data science, emphasizing curvature constraints and spectral properties.

Hsiao-Chun Wuمشاهده پروفایل

استاد

• Statistical Learning
• Digital Signal Processing
• Wireless Communications
• +۹ مورد دیگر

Hsiao-Chun Wu is a Professor and holder of the Michel B. Voorhies Professorship at Louisiana State University (LSU), affiliated with the Division of Electrical & Computer Engineering within the School of Electrical Engineering and Computer Science. He earned his Ph.D. in 1999 from the University of Florida. His research focuses on statistical learning, embedded algorithms, digital signal processing, and wireless communications. Key areas include optimization techniques for detection and estimation, image/speech processing, and networked systems design. He has contributed to advancements in sensor deployment strategies, tensor-based signal processing, and machine learning applications for posture recognition and multimedia classification. Notable technical interests span computational photography, robust multichannel decorrelation, and energy-efficient data collection in wireless sensor networks. His work often integrates tensor analysis and graph convolutional networks to address challenges in high-dimensional data processing. Dr. Wu's academic contributions include pioneering methods for indoor line-of-sight coverage optimization and developing novel modulation recognition techniques. His research also addresses industrial IoT performance optimization and biomedical applications such as noninvasive activity recognition using mmWave radar.