جست‌وجوی استاد و پژوهشگر

Elynn Chenمشاهده پروفایل

استادیار

• Time Series Analysis
• Reinforcement Learning
• Transfer Learning
• +۱۲ مورد دیگر

Elynn Chen is an Assistant Professor in the Department of Technology, Operations, and Statistics (TOPS) at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since September 2021. Her research bridges statistics, machine learning, and operations research with applications in business, economics, and healthcare. Her educational background is highly interdisciplinary: Ph.D. in Statistics, Rutgers University B.A. in Economics, Peking University B.S. in Computer Science, Tsinghua University Professor Chen's research is centered on developing novel methodologies for data-driven decision-making and complex data analysis. Her primary interests include: Tensor learning for multi-dimensional data representation Reinforcement learning with applications in societal domains such as healthcare and education Transfer learning and knowledge fusion across heterogeneous tasks High-dimensional time series and matrix-variate factor models She emphasizes algorithmic innovation and statistical rigor in addressing real-world challenges. Her recent publications reveal a consistent focus on advanced statistical learning methods. She has made significant contributions to tensor decomposition, reinforcement learning in heterogeneous environments, and high-dimensional network modeling. Her work combines theoretical depth with practical applications in international trade, clinical treatments, and corporate finance. Her scientific recognition includes: NSF Postdoctoral Research Award DMS-1803241 She actively mentors students and postdoctoral researchers, fostering a collaborative research environment. Her group works on cutting-edge topics such as tensor-view graph neural networks and dynamic matrix factor models. She has received research support through prestigious postdoctoral appointments at UC Berkeley (advised by Prof. Michael I. Jordan), Princeton University (with Prof. Jianqing Fan), and OpenAI. She leads a vibrant research team focused on: Tensor learning Reinforcement learning for social applications Transfer and knowledge fusion She welcomes highly motivated individuals to join her research group.

Sam Ballasمشاهده پروفایل

دانشیار

• Geometric Structures on Manifolds
• Convex Projective Geometry
• Low-Dimensional Topology
• +۷ مورد دیگر

Sam Ballas is an Associate Professor in the Department of Mathematics at Florida State University, where he also serves as the Director of Pure Mathematics. He previously held an RTG Visiting Assistant Professor position at UC Santa Barbara and completed his Ph.D. at The University of Texas at Austin under Alan Reid. His research focuses on geometric structures on manifolds, particularly convex projective structures, CP&#185-structures, and deformations of hyperbolic and non-compact 3-manifolds. His work integrates techniques from algebra, geometry, and topology, with applications to representation varieties and discrete subgroups of Lie groups. The recent publications show a consistent trend in low-dimensional geometric topology, with a focus on convex projective geometry, gluing equations, thin subgroups, and generalized cusps. The research spans theoretical classification, constructive methods, and deformation theory, often leveraging collaboration with leading figures in the field. Scientific Awards and Funding: NSF Grant DMS-1709097 Sam Ballas advises graduate students and is involved in mentoring through his role as Director of Pure Mathematics. His research has been supported by external grants and collaborative networks such as GEAR. He has organized multiple academic meetings, including the UF/FSU Topology and Geometry Meeting and AMS special sessions, fostering regional collaboration. He is actively involved in outreach, having contributed to a Futurum article for school-aged children and applied projective geometry to soccer analytics for the FSU Women's Soccer Team. He also organizes seminars and has taught a wide range of courses from Calculus to advanced topics in topology and geometric structures.

Muhammad Ahsan Asimمشاهده پروفایل

دانشیار

• Graph Theory and Algorithms
• Computational Complexity Theory
• Combinatorial Optimization
• +۷ مورد دیگر

Muhammad Ahsan Asim is an Associate Teaching Professor in the Department of Computer Science and Electrical Engineering at the University of Missouri–Kansas City (UMKC), School of Science and Engineering. He is actively engaged in research and teaching, with a focus on theoretical and applied aspects of graph algorithms and discrete mathematics. Ph.D. in Mathematical Sciences, University Malaysia Terengganu (UMT), 2018 MS in Computer Science, Blekinge Institute of Technology (BTH), Sweden, 2008 His primary research interests lie in Graph Theory and Algorithms , Computational Complexity Theory , Combinatorial Optimization , and Experimental Mathematics . He applies algorithmic techniques to problems in graph labeling, topological indices, and resolvability, with applications in computer networks, digital circuits, and data science. His recent work emphasizes algorithmic solutions to complex labeling problems in structured graphs such as grid graphs, trees, and networks. The analysis of his 15 most recent publications reveals a consistent focus on graph labeling , particularly edge irregularity , total labeling , and resolving sets . These works span both theoretical developments and algorithmic implementations, often applied to chemical graphs, network topologies, and discrete structures. The keywords across these articles include Graph Theory, Discrete Mathematics, Combinatorics, and Theoretical Computer Science, with subfields ranging from metal-organic networks to circulant graphs and benzenoid structures. He has no listed scientific awards in the provided text. Muhammad Ahsan Asim advises and collaborates with several researchers and students, including Muhammad Shahzad, Wajdi Alghamdi, and Sadia Husain. His work is often supported through collaborative research grants, particularly in the domain of graph-theoretic applications in computing and chemistry. He is associated with Roslan Hasni's Lab , indicating active participation in a research team focused on algorithmic graph theory and its applications. He is involved in advanced computational research, particularly in applying algorithmic methods to solve labeling problems that are intractable via manual computation. His lab work includes modeling network structures, analyzing topological descriptors, and developing iterative algorithms for graph strength computation.

Weizhang Huangمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Numerical Partial Differential Equations
• +۶ مورد دیگر

Professor Weizhang Huang is a faculty member in the Department of Mathematics at the University of Kansas. His research focuses on numerical analysis, scientific computing, and adaptive mesh methods for solving partial differential equations. He holds a PhD from the Chinese Academy of Sciences and has held academic appointments since 1986, including serving as Chair of the Department of Mathematics (2017–2022). Huang's work emphasizes mesh adaptation, finite element methods, and computational fluid dynamics. He has authored over 100 research articles and co-authored the textbook Adaptive Moving Mesh Methods (2011). Notable awards include the Don and Pat Morrison Foundation Award for Excellence in Teaching (2011) and multiple best poster/technical awards at international conferences. Education: PhD, Institute of Applied Mathematics, Chinese Academy of Sciences (1989) M.A., Institute of Applied Mathematics, Chinese Academy of Sciences (1986) B.S., Department of Mathematics, Nanjing University (1983) Research Interests: Huang's work centers on adaptive moving mesh methods, anisotropic mesh generation, finite element/finite difference methods, and their applications in fluid dynamics, fractional calculus, and image segmentation. His MMPDElab software package for adaptive meshing is widely used in computational science. Grants & Collaborations: Huang has led or co-led numerous grants, including NSF-funded projects on mesh adaptation (DMS-1115118) and GPU computing infrastructure (AFOSR 2023). He serves as an associate editor for International Journal of Computer Mathematics and Numerical Mathematics: Theory, Methods and Applications . Labs/Teams: His MMPDElab project and collaborations with institutions like Xiamen University and TU Darmstadt highlight his contributions to computational mathematics and interdisciplinary research.

Iryna Rybakمشاهده پروفایل

مدرس ارشد

• Multiscale Methods
• Mathematical Modelling
• Numerical Algorithms
• +۳ مورد دیگر

Iryna Rybak is a Senior Lecturer and Principal Investigator at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation. Her research focuses on mathematical modeling, numerical methods, and multiscale analysis for coupled free-flow and porous medium systems. She coordinates the German side of the ANR-DFG project FLUPOR and leads Project A03 in the Collaborative Research Centre (SFB) 1313 on interface-driven multi-field processes in porous media. Academic Roles: Research Assistant, Principal Investigator Institution: University of Stuttgart Key Projects: FLUPOR (ANR-DFG), SFB 1313 (DFG) Research Interests: Multiscale methods, averaging theories, Stokes-Darcy coupling, numerical algorithms for multiphysics problems, and model validation using neural networks. Her work addresses interface conditions, porous media flow, and sediment transport in three-phase systems. Publications emphasize fluid-porous interface modeling, preconditioning techniques, and sensitivity analysis. She has advised over 15 PhD/Master students on topics like preconditioners for coupled systems and surrogate-assisted validation frameworks. Teaching includes advanced numerical methods, mathematical programming, and partial differential equations at both undergraduate and graduate levels. Grant Roles: Principal Investigator in FLUPOR and SFB 1313; coordinator of DFG-funded projects on interface modeling (2012–2025).

Paolo Bientinesiمشاهده پروفایل

استاد

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +۸ مورد دیگر

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

• Energy Systems
• Electricity Market Regulation
• Power System Stability
• +۷ مورد دیگر

Dr. José Luis Sancha Gonzalo is a Visiting Researcher at the Institute for Research in Technology (IIT), affiliated with the School of Engineering at Comillas Pontifical University. His research focuses on energy systems modeling and policy analysis, particularly in the context of the Spanish electricity market and regulatory frameworks. Electrical Engineer, ICAI School of Engineering PhD in Industrial Engineering, Polytechnic University of Madrid His research interests include electricity market regulation, power system stability, selective modal analysis, energy policy, self-consumption, and energy pricing. He is a recognized expert on topics such as the gas price cap, PVPC pricing, and the Iberian energy exception. His recent publications reveal a strong focus on contemporary energy policy challenges, particularly the economic and regulatory impacts of the gas price cap in Spain and Portugal. Earlier works emphasize power system dynamics, stability analysis using selective modal techniques, and voltage control strategies. His research bridges engineering fundamentals with economic and policy dimensions of energy systems. IEEE Senior Member CIGRE Distinguished Member Dr. Sancha has maintained continuous teaching activity at institutions including ICAI, the Spanish Energy Club, ISE, and EOI. He has led training courses on electricity billing and market dynamics. His advisory role is evident through his participation in technical reports and policy discussions, though formal PhD advisees are not listed. He has contributed to major collaborative projects, including long-term network planning models. He is actively involved in public discourse, frequently appearing in media and organizing seminars on energy transition, consumer perspectives, and electricity billing. His work with the National Association of ICAI Engineers and other entities highlights his engagement with professional engineering communities and public education.

• Complex Network Analysis
• Social Network Analysis
• Criminal Network Analysis
• +۵ مورد دیگر

Giacomo Fiumara is a distinguished Professor at the University of Messina, Department of Mathematical and Computer Sciences, where he has established himself as a leading researcher in complex network analysis and its applications. With over 90 publications spanning nearly two decades, his work demonstrates significant contributions to network science, particularly in the analysis of criminal organizations, social networks, and quantum approaches to community detection. His extensive collaborations with researchers like Pasquale De Meo (61 co-authored papers), Emilio Ferrara (36 papers), and Salvatore Catanese (27 papers) highlight his central role in an influential research network focused on network science applications. Dr. Fiumara's research interests center on the theoretical and practical aspects of complex network analysis. His work explores how network structures influence information flow, community formation, and resilience in various contexts. He has made significant contributions to understanding the structure and dynamics of criminal networks, particularly Mafia organizations, applying sophisticated network analysis techniques to identify key actors and develop disruption strategies. His recent work increasingly integrates machine learning approaches with traditional network analysis, developing novel methods for community detection, influence maximization, and node identification in complex systems. His research bridges theoretical computer science with practical applications in security, social media analysis, and infrastructure planning. The analysis of his recent publications reveals a clear evolution in his research focus toward integrating advanced machine learning techniques with network science. While maintaining his expertise in criminal network analysis, he has expanded into quantum computing applications for community detection, heterogeneous graph neural networks, and sophisticated methods for influence identification. His work shows a consistent pattern of addressing both theoretical challenges in network science and practical applications across diverse domains including social media, transportation infrastructure, and security systems. The interdisciplinary nature of his research is evident in publications spanning computer science journals, physics publications, and security-focused venues. Dr. Fiumara's work has significant implications for law enforcement strategies, social media analysis, and infrastructure planning. His research on Mafia network disruption provides concrete methodologies for identifying critical nodes in criminal organizations, while his work on information propagation has applications in marketing and public health campaigns. The integration of quantum computing concepts with traditional network analysis represents a cutting-edge direction that could revolutionize how we understand complex systems.

Julia Schleussمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Dr. Julia Schleuss is a Researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster . She is affiliated with the Mathematics Münster Graduate School and contributes to research in numerical analysis, machine learning, and scientific computing. PhD in Mathematics (2019–2023), University of Münster MSc in Mathematics with minor Economics (2016–2019), University of Münster BSc in Mathematics with minor Business Administration (2013–2016), University of Münster Her research focuses on model order reduction , multiscale methods , and domain decomposition techniques for partial differential equations (PDEs). Recent work includes time-parallel approximation spaces and residual localization strategies, bridging numerical methods with machine learning for high-dimensional problems. Selected publications address optimal local approximation spaces for parabolic PDEs (2022), randomized quasi-optimal time-domain decomposition (2023), and localized training/enrichment strategies (2024). These works emphasize computational efficiency and adaptability for complex systems. She contributes to teaching as a collaborator in courses such as Numerical Methods for PDEs , Nonlinear Modeling in Natural Sciences , and Applied Functional Analysis , working with professors like Mario Ohlberger and Christian Engwer.

• Harmonic Analysis
• Sampling Theory
• Gabor Frames
• +۴ مورد دیگر

Jose Luis Romero is an Associate Professor at the Department of Mathematics, Faculty of Mathematics, University of Vienna. His research spans harmonic analysis, sampling theory, Gabor frames, and signal processing, with a focus on mathematical physics and approximation theory. He has collaborated extensively on projects related to time-frequency analysis, Gaussian processes, and lattice structures. His work includes 15 most recent publications (2024–2019) on topics such as: Gabor frames and their stability under deformations Sampling theory in shift-invariant spaces and phase-space covers Time-frequency concentration problems and spectral estimates Determinantal point processes and Coulomb gas equidistribution His articles often integrate applied mathematics , signal processing , and mathematical physics , with keywords like Gaussian distribution, asymptotics, and Fourier transforms. While specific grants and advised students are not listed here, his collaborative projects highlight interdisciplinary applications in imaging sciences and partial differential equations.

• Numerical Analysis
• Partial Differential Equations
• Computational Fluid Dynamics
• +۴ مورد دیگر

Lyudmyla F Barannyk serves as an Associate Professor in the Department of Mathematics and Statistical Science within the College of Science at the University of Idaho. Her academic career spans over two decades with a focus on computational mathematics and interdisciplinary engineering applications. Her educational background includes: PhD in Mathematical Sciences (2003) from New Jersey Institute of Technology and Rutgers, State University of New Jersey MS in Applied Mathematics (2000) from New Jersey Institute of Technology BS and MS in Mathematics, Mathematical Physics, Teaching in Mathematics and Computer Science (1994) from Kharkiv National University, Ukraine Dr. Barannyk's research centers on Numerical Analysis and Partial Differential Equations, with significant contributions to phase change problems (particularly the Stefan problem with internal heat generation), interfacial fluid dynamics, and computational modeling of thermoelectric materials. Her work bridges mathematical theory with practical engineering challenges in thermal systems, materials science, and high-speed electronics. She has developed specialized numerical methods including Fourier-Bessel series expansions and causality enforcement techniques for electromagnetic applications. Analysis of her 2015-2024 publications reveals a strong interdisciplinary trajectory connecting mathematical physics with engineering disciplines. Her thermal modeling research consistently addresses complex geometries (cylindrical, spherical) and boundary conditions, while her signal processing work focuses on time-domain analysis for high-speed interconnects. This dual focus demonstrates exceptional versatility in applying advanced mathematical techniques to diverse physical phenomena. No specific scientific awards were documented in the source material. While her publication record indicates collaborative research, details regarding graduate student advising, research grants, or laboratory facilities were not provided in the available information. Her work appears to involve significant cross-disciplinary collaboration between mathematics, materials science, and electrical engineering departments.

• Fluid Dynamics
• Combustion Engineering
• Turbomachinery
• +۳ مورد دیگر

Pietro De Palma serves as a Full Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His research focuses on fluid machinery systems with applications spanning hydrogen propulsion, renewable energy conversion, and fundamental fluid dynamics. Contact details include email pietro.depalma@poliba.it and office phone +39 080 596 3226 at Via Orabona 4, Bari. His primary research domains encompass Fluid Dynamics , Combustion Engineering , and Turbomachinery , with specialized expertise in hydrogen engine combustion phenomena, wind turbine aerodynamics, and thermal management systems. Recent work investigates lubricant oil interactions in hydrogen engines, Coriolis effects on wind turbine wakes, and stability mechanisms in transitional channel flows. He employs advanced computational methods including Large Eddy Simulation (LES), Reynolds-Averaged Navier-Stokes (RANS), and Dynamic Mode Decomposition (DMD) for complex fluid-structure interactions. Analysis of his 15 most recent publications (2024-2026) reveals three dominant research thrusts: (1) Hydrogen propulsion systems addressing pre-ignition mechanisms and combustion characterization, (2) Wind energy optimization focusing on wake dynamics and floating platform simulations, and (3) Fundamental fluid mechanics exploring coherent structures in turbulent flows. His work consistently bridges theoretical fluid dynamics with practical energy system challenges, particularly in decarbonization technologies. No scientific awards were documented in the provided sources. While student advising details are unavailable in the scraped materials, his extensive publication record indicates active mentorship in mechanical engineering research. No specific grant information appears in the sources, though his involvement in projects like PONa3_00372 'Innovative Processes for Energy Conversion–PrInCE' suggests competitive funding acquisition. Research activities appear centered within the university's Department of Mechanics, Mathematics & Management, with collaborations evident in wind energy and combustion studies through co-authored publications. His work connects with international initiatives like the IEA Wind Task 32 on wind turbine wake behavior.

• Complex Analysis
• Several Complex Variables
• Geometric Function Theory
• +۲ مورد دیگر

Professor Piotr Liczberski is affiliated with the Department of Applications of Modern Mathematical Analysis at Łódź University of Technology, where he has conducted extensive research in complex analysis and geometric function theory in multiple variables. His work focuses on holomorphic mappings, Bavrin families, and geometric properties in Cⁿ and Banach spaces. Department: Applications of Modern Mathematical Analysis University: Łódź University of Technology Research Focus: Several Complex Variables, Holomorphic Mappings, Bavrin Families His research explores starlikeness, convexity, K-Fold symmetry, and distortion theorems for biholomorphic mappings. He has developed methods for analyzing holomorphic functions using cyclic group decompositions and Jack's Lemma generalizations. Major publications include collaborations with Victor V. Starkov and Renata Długosz on domains with conically accessible boundaries and extremal problems. He co-authored the monograph Univalent mappings of several complex variables (Cluj University Press, 1998). PhD students advised: Renata Długosz (2015) and Edyta Leś-Bomba (2010). Active in international conferences including Complex Analysis and Dynamical Systems VI (2013) and International Congress of Mathematicians (2002).

Chandrakana Nandiمشاهده پروفایل

استادیار

• Formal Verification
• Smart Contracts
• DeFi Applications
• +۷ مورد دیگر

Chandrakana Nandi is the Director of US R&D at Certora and an affiliate assistant professor at the University of Washington, Seattle . Her work bridges formal verification, computational fabrication, and programming language design. Education : PhD in Computer Science from University of Washington (2021), MS in Computer Science (2014), BS in Computer Science (2012). Research Interests focus on: Automated formal verification of DeFi applications and smart contracts. Equality saturation for program synthesis, verification, and compiler optimizations. Programming languages and compilers for computational fabrication and 3D modeling. Publication Trends highlight her contributions to: Formal verification tools for real-world programs (WASM, G-code). Equality saturation applications in synthesis, optimization, and rule inference. Domain-specific languages like LambdaCAD, Taxon, and Carpentry Compiler. Scientific Awards : Distinguished Paper Award (OOPSLA 2021). Distinguished Paper Award and Sigplan Research Highlight (POPL 2021). Contact : chandra@certora.com . Her CV is available here .

Ben Steinbergمشاهده پروفایل

استاد

• Wave Propagation
• Electromagnetic Fields and Waves
• Optics
• +۴ مورد دیگر

Ben Steinberg is a Professor at the School of Electrical Engineering within the Faculty of Engineering at Tel Aviv University. His work focuses on Electromagnetics , Wave propagation and scattering , and Photonic Crystals , with applications in Artificial materials and Plasmonic structures , as well as Propagation in complex (multi-scale) medium and effective properties. He has held major roles in TAU's academic governance, including heading the Engineering Faculty's PhD Students Committee and serving on the TAU Promotion & Nomination Committee. Education: 1985-1989: PhD in Electrical Engineering, Tel Aviv University 1989-1991: PostDoc at The Catholic University of America, Washington DC Research interests span Electromagnetics Wave propagation and scattering Photonic Crystals, artificial materials, plasmonic structures Propagation in complex (multi-scale) medium and effective properties Article trends emphasize Electromagnetics , Photonics , and Condensed Matter Physics , with subfields like Non-Reciprocal Waveguides , Photonic Crystal Devices , Plasmonic Structures , Wave propagation in multi-scale medium , Green's Function Theory , and Effective Properties of Materials . Advising includes PhD and MSc students such as Yarden Mazor , Yakir Hadad , Roman Novitski , and Vitaliy Lomakin , with research topics in Plasmonics , Clustering , and Waveguide Sensitivity . He has also co-supervised projects on Optical Gyroscopes and Disordered Photonic Crystals . Academic roles include: Head of Faculty of Engineering Admission Committee (2001-2005) Head of Faculty of Engineering PhD Students Committee (2006-2009) Member of TAU Promotion & Nomination Committee (2010-2013) Industry contributions include co-founding XellAnt Inc. as CTO (2001-2004) and consulting for high-tech companies.