جست‌وجوی استاد و پژوهشگر

Kanishka Pereraمشاهده پروفایل

استاد

• Variational methods for nonlinear PDEs
• Critical growth elliptic problems
• Fractional p-Laplacian equations
• +۲ مورد دیگر

Kanishka Perera is a Professor in the Department of Mathematics and Systems Engineering , College of Engineering and Science at Florida Institute of Technology . Active in both research and teaching, his office is located in Frederick C. Crawford Building, Room 325 , and he can be reached at kperera@fit.edu . His research focuses on advanced mathematical techniques for nonlinear partial differential equations, particularly variational and topological methods , critical growth problems , fractional p-Laplacian equations , and nonlocal operators . He has authored more than one hundred peer-reviewed articles since 2009, demonstrating sustained productivity in these areas. Recent publications (2024–2025) are dominated by investigations into • Schrödinger–Poisson–Slater type systems, • Fractional and nonlocal elliptic equations in Sobolev spaces, • Double phase and Kirchhoff-type problems, • Multiplicity and bifurcation phenomena for critical growth equations. These works collectively advance the understanding of existence, regularity, decay estimates, and qualitative properties of solutions to challenging nonlinear PDEs. Research Profiles ORCID Google Scholar ResearchGate Scopus Contact Email: kperera@fit.edu Phone: (321) 674-8869

Salvatore Angelo Maranoمشاهده پروفایل

استاد

• Mathematical Analysis
• Differential Equations
• Partial Differential Inclusions
• +۶ مورد دیگر

Salvatore Angelo Marano is a Full Professor of Mathematical Analysis at the University of Catania, affiliated with the Department of Mathematics and Computer Science since 2001. He graduated in Mathematics from the University of Catania in 1987 and has authored approximately 100 research papers in international mathematics journals. His research spans: Differential equations/inclusions and fixed point theory Critical point theory for non-smooth functions on convex sets Elliptic boundary value problems (Dirichlet, Neumann, Robin) p-Laplacian and (p,q)-Laplacian equations Fractional operators and Sobolev embeddings Variational-hemivariational inequalities Recent publications (2019-2025) focus on singular systems, fractional operators, quasilinear convection problems, and multi-solution phenomena in nonlinear PDEs. His work consistently employs variational methods, truncation techniques, and critical point theory. Awards include: Foundation 'Francesco Severi' Prize (1991) Gioenia Academy Young Researcher Prize (1989) He collaborates internationally with researchers in Poland, Portugal, France, Greece, USA, and Algeria, and serves on editorial boards of journals including Nonlinear Analysis: Real World Applications and Discussiones Mathematicae .

• Signal Processing
• Optimization
• Sensitivity Analysis
• +۳ مورد دیگر

Hugo Raguet serves as an Associate Professor at INSA Centre-Val de Loire in Blois, France, with research affiliations at the Fundamental and Applied Computer Science Laboratory (jointly associated with University of Tours). His work bridges theoretical computer science and applied mathematics, focusing on algorithmic solutions for real-world computational challenges. His research expertise spans: Signal Processing: Developing efficient algorithms for real-time applications and medical imaging Optimization: Advancing convex and non-smooth optimization techniques with graph-based regularization Sensitivity Analysis: Pioneering targeted methods using dependence measures for domain-specific phenomena Image Processing: Extending raster-to-vector conversion to multicolor systems Publication analysis reveals consistent innovation in splitting methods (Douglas-Rachford, forward-backward) and preconditioning techniques, with strong emphasis on computational efficiency for graph-structured problems. His work frequently bridges mathematical theory with practical implementations in signal processing and machine learning. No scientific awards are documented in available sources. Student advising and grant information remain unspecified. Raguet actively contributes to the Fundamental and Applied Computer Science Laboratory, where he develops open-source tools for academic collaboration including live conferencing software and LaTeX templates for scientific communication.

Rory Russellمشاهده پروفایل

استاد

• Classical Guitar Performance
• Chamber Music
• Guitar Quartet
• +۴ مورد دیگر

Rory Russell serves as Professor of Guitar at the Royal Northern College of Music (RNCM), where he provides specialized instruction to classical guitar students. His responsibilities include weekly ensemble coaching sessions, one-on-one lessons conducted once per term, and leading the weekly performance class for guitar students. Russell also directs annual department ensemble projects culminating in summer public concerts, designed to develop students' large ensemble skills and experience working under conductor direction. Professor Russell maintains an active professional performance career as a core member of the Aquarelle Guitar Quartet. The ensemble has performed extensively across Britain, Europe, South Korea, and Israel, appearing at prestigious venues including London's Albert Hall & Wigmore Hall, Manchester's Bridgewater Hall, and Athens' music conservatoire. They regularly feature on Classic FM and BBC Radio 3, with their recordings receiving critical acclaim. Chandos Records artist since 2009 Performed concertos with UK orchestras including Rodrigo's Concierto de Aranjuez Extensive chamber music collaboration experience with diverse musicians Russell's discography with the Aquarelle Guitar Quartet demonstrates a diverse artistic range, from traditional Spanish and Brazilian music to film adaptations and contemporary compositions. The quartet's recordings have achieved significant recognition, with multiple albums receiving regular airplay on major classical music stations across Britain and Australia. Rory Russell has established himself as both an educator dedicated to developing the next generation of guitarists and a performing artist of international standing, bridging academic instruction with professional performance practice.

Ayush Asthanaمشاهده پروفایل

استادیار

• Quantum Chemistry
• Quantum Computing
• Electronic Structure Theory
• +۴ مورد دیگر

Ayush Asthana serves as Assistant Professor in the Department of Chemistry & Biochemistry at the University of North Dakota, where he leads the Asthana Group focused on quantum chemistry and quantum computing. His research bridges traditional quantum chemistry with emerging quantum technologies to develop novel computational methods for molecular sciences. Education: PhD in Theoretical Chemistry, Johns Hopkins University (2021) BS-MS dual degree in Chemistry, Indian Institute of Technology Kanpur (2016) Ayush's research spans multiple cutting-edge areas at the intersection of quantum computing and chemistry. His group develops new ab initio methods for predictive computational molecular sciences, focusing on electronic structure theory using quantum computers and machine learning techniques. Key research thrusts include quantum chemistry on quantum computers, quantum control for algorithm development, material simulation, and software development for quantum chemistry applications. His work emphasizes boundary-crossing science without departmental constraints. Analysis of his recent publications reveals a strong focus on practical quantum algorithms for chemistry applications, particularly addressing challenges in NISQ-era devices. His work shows increasing emphasis on relativistic quantum chemistry, excited state calculations, and error mitigation techniques, with a clear trajectory toward practical quantum advantage in chemical simulations. Scientific Awards and Recognition: NSF ExpandQISE grant (2024) for quantum algorithms in relativistic quantum chemistry NSF Epscor Fellow grant (2024) for quantum algorithms on early fault-tolerant computers UND Early Career grant (2023) for neural network formalisms in quantum chemistry Amazon Braket hardware resources (2023) for quantum research Ayush actively mentors students and postdocs in his research group, with current members including graduate students Prince Frederick Kwao and Olusayo, postdoc Srivathsan Poyyapakkam Sundar, and undergraduate Sean Glaholt. His group has secured significant funding including multiple NSF grants and maintains active collaborations with institutions like Virginia Tech, PNNL, and University of Minnesota. The Asthana Group emphasizes interdisciplinary collaboration and student development in a supportive environment that encourages learning from mistakes. The group operates within the University of North Dakota's research ecosystem, with access to quantum computing resources through Amazon Braket and computational facilities at UND. They maintain an active presence at major conferences including APS meetings and IEEE Quantum Week, with regular presentations and invited talks.

• Inverse problems
• Optimization
• Variational analysis
• +۷ مورد دیگر

D. Russell Luke is a Professor of Continuous Optimization and Variational Analysis at the University of Göttingen . He previously held a tenure-track position at the University of Delaware (2004-2009) and was a PIMS Fellow at Simon Fraser University. Current affiliations include editorial roles at the Open Journal of Mathematical Optimization , JOTA , ESAIM: COCV , and Advances in Computational Mathematics . Research Interests: Luke specializes in inverse problems, optimization, and variational analysis, with applications in atomic orbital tomography, X-FEL imaging, and fixed-point theory. His work bridges theoretical mathematics and industrial applications. Mathematics of atomic orbital tomography (with Stefan Mathias) Stochastic computed tomography for X-FEL imaging (with Helmut Grubmueller) Probabilistic algorithms in fixed-point theory (with Anja Sturm) Topological optimization for tree structures (with Stephan Huckemann) Scientific Awards: NASA/GSFC Graduate Student Research Fellow (1998-2001) PIMS Fellow (2002-2004) Advising and Grants: Luke has advised PhD students like Patrick Neumann and Thao Nguyen. His research is funded by the National Science Foundation (NSF), German Research Foundation (DFG), German Israeli Foundation (GIF), and Australian Research Council (ARC). Labs and Teams: He leads the Working Group on Continuous Optimization, Variational Analysis, and Inverse Problems and collaborates with researchers in computational mathematics, optics, and multi-scale imaging.

• Inverse Problems
• Mathematical Imaging
• Computational Mathematics
• +۳ مورد دیگر

Prof. Otmar Scherzer is a distinguished academic at the Faculty of Mathematics, Department of Mathematics, University of Vienna . His work spans Inverse Problems, Mathematical Imaging, Computational Mathematics, and Applied Mathematics , with a focus on tomography, neural networks, and medical imaging. Over nearly two decades, he has published extensively in journals like Inverse Problems , SIAM Review , and IEEE Transactions . 2025 : Advanced data-driven regularization and neural network applications in inverse problems. 2024 : Explored diffraction tomography, Hilbert space approaches to scattering, and motion detection algorithms. 2023 : Addressed uncertainty quantification, visco-acoustic imaging, and Doppler ultrasound interpolation. His recent research emphasizes uncertainty-aware blob detection, strain reconstruction in elastography , and Fourier-based tomography . Publications frequently integrate numerical experiments and computational inverse methods , reflecting a blend of theoretical and applied mathematics. Prof. Scherzer collaborates with institutions like RICAM (Johann Radon Institute) and contributes to hybrid imaging modalities such as quantitative optical coherence elastography and photoacoustic tomography . He has supervised projects in 3D shape reconstruction and non-convex regularization , though specific student names are not listed in the provided data.

Elena Resmeritaمشاهده پروفایل

دانشیار

• Inverse Problems
• Regularization Methods
• Image Restoration
• +۳ مورد دیگر

Elena Resmerita is an Associate Professor at the Institute of Mathematics , Alpen-Adria University of Klagenfurt, Austria. She holds a Habilitation in Mathematics (2013) and a PhD in Mathematics (2003) from the University of Klagenfurt and Haifa respectively. 2013: Habilitation, University of Klagenfurt 2003: PhD, University of Haifa 1997: MSc, Al. I. Cuza University of Iasi Her research focuses on inverse problems , regularization methods for ill-posed problems , image restoration , continuous optimization , convex analysis , and iterative methods for convex feasibility problems . Her recent publications highlight advancements in regularization theory, multiscale decomposition, and sparsity-driven optimization. Key contributions include: Nonconvex regularization techniques Entropic Landweber methods Bregman iterations for image processing Convergence analysis of stochastic algorithms She has received prestigious scientific awards : FWF Elise Richter Fellowship IPAM Fellow at UCLA Active in academic service, she co-organizes workshops like the Alps-Adriatic Workshop in Inverse Problems and contributes to the European Women in Mathematics (EWM) as Deputy Convenor and committee member. Her teaching spans Analysis, Calculus, and specialized inverse problem courses.

Szabolcs Simonمشاهده پروفایل

دانشیار

• Mother Tongue Pedagogy
• Hungarian-Slovak Bilingualism
• Language Policy
• +۳ مورد دیگر

Szabolcs Simon, PhD is an Associate Professor at the Department of Hungarian Language and Literature within the Pedagogical Faculty of Selye János University in Komárno, Slovakia. His academic career spans over three decades, with significant contributions to Hungarian language and literature education in Slovakia's bilingual context. Simon has held positions at Comenius University in Bratislava and the State Pedagogical Institute before joining Selye János University in 2006. Simon's research primarily focuses on mother tongue pedagogy, Hungarian-Slovak bilingualism, and language policy. His scholarly work examines the linguistic landscape of Hungarian minority communities in southern Slovakia, with particular attention to Komárno and surrounding areas. His research methodology combines sociolinguistic surveys, textual analysis, and historical investigation to understand language use patterns, attitudes, and policy impacts on minority language maintenance. Simon's recent publications (2022-2025) demonstrate his continued scholarly productivity, with particular focus on Hungarian-Slovak bilingual communities, language politics, and educational issues. His work appears in conference proceedings, academic journals, and edited volumes, often addressing the challenges and opportunities of Hungarian language education in Slovakia. His research shows a consistent trajectory examining how historical, political, and social factors shape language use and identity in minority contexts. As founding member and leader of the Variology Research Group at Selye János University, Simon has fostered collaborative research on Hungarian language variation in minority contexts. He serves on the editorial board of the professional journal Katedra and has contributed to numerous research projects examining language policy, educational materials, and bilingualism in Slovakia. Simon's professional activities extend beyond academia through his leadership roles in civil organizations, including serving as vice-president of the Ármin Vámbéry Civil Association. His earlier work included regular radio programs on Slovak Radio's Hungarian section from 1999 to 2007, demonstrating his commitment to public engagement with linguistic and cultural issues.

Maciej Starostkaمشاهده پروفایل

استادیار

• Symplectic Geometry
• Morse Theory
• Hamiltonian Systems
• +۳ مورد دیگر

Dr. Maciej Starostka serves as an Assistant Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. His office is located in Building B, room 512, and he can be reached at phone number 604100597. His research focuses on advanced topics in symplectic geometry and topological methods in nonlinear analysis. Dr. Starostka's work primarily centers on Morse theory applications to Hamiltonian systems, the Conley index, and variational methods in infinite-dimensional spaces. His research bridges pure mathematics with applications in dynamical systems theory. Analysis of his recent publications (2015-2025) reveals a consistent focus on Morse homology in various contexts including cotangent bundles, Banach spaces, and Hilbert spaces. His work frequently addresses the Arnold conjecture and related problems in symplectic topology, employing sophisticated topological methods to establish lower bounds on critical points and fixed points of Hamiltonian diffeomorphisms. Dr. Starostka leads the MADGE research project (Morse theoretical methods in Analysis, Dynamics, and Geometry) under the WEAVE program, which commenced in October 2024 and is being conducted within the Division of Dynamical Systems. This project represents his ongoing commitment to advancing theoretical frameworks in mathematical analysis and geometry. He obtained his doctoral degree (dr) in Mathematics from the Institute of Mathematics of the Polish Academy of Sciences on June 22, 2017, establishing the foundation for his current research trajectory in topological methods and symplectic geometry.

Thijs Dekkerمشاهده پروفایل

استاد

• Welfare Economics
• Choice Modelling
• Bayesian Econometrics
• +۹ مورد دیگر

Professor Thijs Dekker is a faculty member in the Faculty of Environment at the University of Leeds , where he holds the Professor of Transport Economics position. He previously served as Associate Professor (2020-2024) and Lecturer in Transport Economics (2014-2020) at the Institute for Transport Studies. PhD in Economics, VU University Amsterdam (2012) MSc in Economics, Erasmus University Rotterdam (with highest honour, 2006) BSc in Economics, Erasmus University Rotterdam (2005) His research focuses on empirical analysis of travel behaviour and non-market valuation , with emphasis on discrete choice models , Bayesian econometrics , and Participatory Value Evaluation (PVE) . He develops statistical frameworks for preference heterogeneity and welfare measurement in transport contexts, and has pioneered PVE as an alternative to traditional cost-benefit analysis. Recent publications explore transport decarbonization , choice model robustness , and value of travel time across freight, rail-air intermodality, and shared mobility services. Methodological contributions include computational gradients for choice modelling and validity standards for experimental design. Scientific roles include: Elected Regular Board member, International Association for Travel Behaviour Research (2019-2023) Editorial advisory board member, Journal of Choice Modelling Editorial advisory board member, Transportation Research Part C: Emerging Technologies As Director of Postgraduate Research Studies, he supervises PhD researchers including Phil Churchman, Abdul Muti Sazali, and Robby Yudo Purnomo. His applied work includes UK national VTT studies, Dutch policy appraisals, and World Bank freight analysis. He contributes to the Choice Modelling research group and participates in projects like DRYvER (biodiversity in river networks) and VAAR (rail accessibility appraisal). Current responsibilities include leading the MSc Transport Economics program and developing robust transport valuation frameworks.

Matteo Santacesariaمشاهده پروفایل

دانشیار

• Inverse Problems
• Mathematical Analysis
• Medical Imaging
• +۳ مورد دیگر

Matteo Santacesaria is an Associate Professor at the Department of Mathematics (DIMA) of the University of Genoa. His research focuses on inverse problems, mathematical analysis, and their applications in medical imaging and machine learning. He teaches courses such as Mathematics for Architectural Sciences, Mathematical Analysis I, and Company Internship for Mathematics students. His work bridges theoretical mathematics with practical applications, including compressed sensing, MRI and CT reconstruction, and deep learning techniques. Recent projects involve developing algorithms for artifact correction in medical imaging and sparsity-promoting regularizers for inverse problems. He actively contributes to international conferences like SSVM and collaborates on manifold learning and variational methods. His research emphasizes interdisciplinary solutions for imaging challenges and optimization in inverse problem frameworks.

Meimei Liuمشاهده پروفایل

استادیار

• Deep learning
• Bayesian data analysis
• Learning theory
• +۳ مورد دیگر

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

Hamid Eghbal-zadehمشاهده پروفایل

پژوهشگر

• Adversarial Machine Learning
• Representation Learning
• Generative Models
• +۴ مورد دیگر

Hamid Eghbal-zadeh is a Postdoctoral Researcher at the Institute of Computational Perception at Johannes Kepler University Linz (JKU). Previously, he worked as a Research Scientist at Meta and held roles at the Institute for Machine Learning at JKU. His research focuses on adversarial machine learning, representation learning, and robust deep learning models. He has made significant contributions to acoustic scene analysis, generative models, and reinforcement learning. Education : PhD in Machine Learning from JKU (2019), defended with distinction. BSc/MSc details not explicitly stated in text. Research Highlights : Developed Mixture Density GANs to address mode collapse in GANs Pioneered receptive-field regularization techniques for audio classification Contributed to top-performing systems in DCASE challenges (2016-2019) Active in reinforcement learning and domain adaptation research Awards/Competitions : Multiple top placements in international challenges (DCASE, MediaEval), outstanding reviewer recognitions (ICML 2022, ICLR 2022). Advising : Supervised 6+ students in topics like acoustic scene classification, generative retrieval, and sound event detection.

• Addiction Psychology
• Cognitive Neuroscience
• Alcohol Use Disorders
• +۴ مورد دیگر

Kyriaki Nikolaou serves as Assistant Professor in Psychology at the University of Sussex's School of Psychology, where she conducts research on the cognitive and neurobiological mechanisms underlying alcohol addiction. Her academic journey includes a PhD in Psychology from Goldsmiths University of London, an MSc in Clinical Neuroscience from UCL, and a BSc in Psychology from Goldsmiths. Her research focuses on cognitive biases toward alcohol-related stimuli, the effects of stress and pharmacological interventions on addiction processes, and the neural mechanisms of cognitive control in substance use disorders. Utilizing cognitive/behavioral approaches, neuroimaging, and pharmacological manipulations, her work has significantly contributed to understanding how genetic variations, stress responses, and noradrenergic systems influence addiction pathways. Nikolaou's publication record demonstrates consistent expertise in alcohol addiction neuroscience, with recent work examining pain perception in binge drinkers, cognitive bias modification techniques, and genetic influences on adolescent reward processing. Her research trajectory shows evolution from fundamental neurocognitive mechanisms to translational applications including pharmacological interventions like Atomoxetine for Alcohol Use Disorders. As an educator, she convenes Biological Psychology and Cognitive Psychology modules while co-convening Psychobiology. Her teaching responsibilities include Cognition in Clinical Contexts and regular student consultation hours. Nikolaou maintains an active research profile through collaborations with the IMAGEN consortium and European Commission Horizon 2020 projects, particularly the Systems biology of Alcohol Addiction initiative.