جست‌وجوی استاد و پژوهشگر

Pete Santagoمشاهده پروفایل

استاد

• Biomedical Informatics
• Pattern Recognition
• Machine Learning
• +۵ مورد دیگر

Dr. Pete Santago is a Professor in the Department of Computer Science at Wake Forest University. He was previously a faculty member at the Wake Forest University School of Medicine for 24 years, where he established an engineering group in Radiology and co-founded the Biomedical Engineering Department. His research spans biomedical informatics, pattern recognition, machine learning, and image/signal processing, with current interests in stochastic processes, data mining, and computer organization. Education: Ph.D. in Electrical Engineering from North Carolina State University Education: M.S. in Computer Science from Virginia Tech Dr. Santago’s research bridges biomedical engineering and computer science, with applications in medical imaging, data analysis, and educational innovation. His work emphasizes practical solutions for image processing, diagnostic tools, and hands-on STEM learning environments like the WakerSpace makerspace. His 15 most recent publications highlight expertise in medical imaging (e.g., PET, CT, MRI), algorithm development (segmentation, reconstruction), and educational initiatives. Techniques such as convex sets, MAP estimation, and geodesic active contours recur in his contributions to biomedical informatics and computer science. As Faculty Director of WakerSpace, Dr. Santago focuses on service work and student mentorship, guiding independent projects that integrate data mining and computer organization principles.

David Monniauxمشاهده پروفایل

پژوهشگر

• program verification
• abstract interpretation
• static analysis
• +۸ مورد دیگر

David Monniaux is a senior researcher (directeur de recherche) at CNRS and an adjunct professor at École polytechnique. He works at VERIMAG, a computer science laboratory jointly operated by CNRS and the University of Grenoble. Dr. Monniaux obtained his PhD in 2001 from Université Paris Dauphine under Professor Patrick Cousot, with a dissertation on the static analysis of probabilistic programs by abstract interpretation. He later earned his habilitation in computer science in 2009 from Université Joseph Fourier, Grenoble, and also holds an agrégation in mathematics. Monniaux's research focuses on program verification, with particular emphasis on proving software correctness. His work spans theoretical foundations in computability theory and practical applications in safety-critical systems. He has made significant contributions to abstract interpretation, static analysis, and the verification of numerical properties in programs. His research bridges computer science theory with practical engineering challenges, particularly in the context of critical embedded systems where software failures can have severe consequences. His work connects to diverse fields including game theory, algebra, and convex optimization. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques. Key themes include polyhedral approximation, program analysis with local policy iteration, abstraction of arrays and maps, synthesis of ranking functions, and computing worst-case execution times. These works collectively advance the field of program verification by addressing challenges in handling nonlinear constraints, branching, and complex data structures while maintaining computational feasibility. Monniaux has supervised several students including Julien Henry, Alexis Fouilhé, George (Egor) Karpenkov, and Alexandre Maréchal (now at LIP6), with current students Hang Yu and Valentin Touzeau. He has led significant research projects including VERASCO (2012-2015), which aimed at integrating a static analyzer into the CompCert certified compiler, and STATOR (2012-2017), an ERC starting investigator grant exploring advanced techniques for automatic inference of program invariants. At VERIMAG, Monniaux is part of a vibrant research community focused on critical systems. His work connects with broader efforts in formal methods, with applications in aviation, automotive systems, and other safety-critical domains where software reliability is paramount.

Bert Kappenمشاهده پروفایل

استاد

• Quantum Machine Learning
• Stochastic Control Theory
• Neural Networks
• +۴ مورد دیگر

Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems. His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include: Quantum Machine Learning : Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization Path Integral Control : Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience Bayesian Inference : Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol) Atomic-scale Neural Networks : Implementing neuromorphic computing using nanoscale atomic switches Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory. He teaches courses including Introduction to Machine Learning , Advanced Computational Neuroscience , and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.

• Computer Modeling
• Structural Dynamics
• Innovation Management
• +۲ مورد دیگر

Olena Volodymyrivna Korotunova is an Associate Professor at the Department of Applied Mathematics within the Mechanical Engineering Faculty of National University 'Zaporizhzhia Polytechnic'. She holds a Candidate of Technical Sciences degree (Ph.D. equivalent) from Zaporizhzhia National Technical University (2004), specializing in Mechanics of Deformable Solids. Her dissertation focused on the dynamics of structures with complex element interactions. Her research integrates applied mathematics with engineering and economics, spanning: Computer modeling of structural dynamics and non-stationary deformations Innovation assessment in industrial enterprises Cluster-based regional economic development strategies Elastic body mechanics and contact problem solutions Her publications emphasize mathematical modeling in engineering contexts (e.g., deformation analysis, structural integrity) and economic applications (e.g., innovation management, competitiveness). Recent works (2022-2024) show a trend toward sustainable technologies and computational economics. Awards: Certificate of Merit (2009) from Zhovtneva District Administration for contributions to education and research She teaches courses in Higher Mathematics, Probability Theory, Optimization Methods, and Economic-Mathematical Modeling. With 50+ publications, she collaborates internationally (e.g., Malaysia, Bulgaria, France) and maintains active researcher profiles (ORCID, Scopus, Google Scholar).

Zoran Tomljanovićمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Damping Optimization in Mechanical Systems
• Control Theory
• +۱ مورد دیگر

Zoran Tomljanović is an Associate Professor and Vice-Dean for Teaching and Students at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on numerical linear algebra, damping optimization in mechanical systems, control theory, and matrix equations, with significant contributions to model reduction and parametric system optimization. His work includes novel approaches to $H_{\infty}$ norm analysis for multi-agent systems, dimension reduction techniques for damped vibrational systems, and parametric dominant pole algorithms for semi-active damping optimization. These methods have been applied to problems in mechanical engineering, synchronization, and high-dimensional data partitioning. Key projects led by Tomljanović include Accelerated solution of optimal damping problems (DAAD 2021–2022), Vibration Reduction in Mechanical Systems (Croatian Science Foundation 2020–2023), and Robustness optimization of damped mechanical systems (DAAD 2017–2018). He has also contributed to educational initiatives through publications like Metode optimizacije (2014), a textbook on optimization methods. Professional activities include co-organizing the 8th Croatian Mathematical Congress (2024), Winter School on Model Reduction (2024), and multiple international workshops on optimal control and model reduction. Teaching responsibilities include courses on linear algebra and control theory applications.

Antonio Orvietoمشاهده پروفایل

استاد

• Deep Learning
• Optimization Theory
• Sequence Modeling
• +۷ مورد دیگر

Antonio Orvieto is a Professor and Principal Researcher at the Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen, where he leads the Deep Models and Optimization research group. He is also a lecturer at the University of Tübingen and faculty for the CLS, ELLIS, and IMPRS-IS PhD Programs. His research focuses on improving the efficiency of deep learning technologies through theoretical understanding of optimization dynamics and innovative neural network architectures. Dr. Orvieto earned his PhD from ETH Zürich under the supervision of Prof. Dr. Thomas Hofmann and Dr. Aurelien Lucchi. Prior to his PhD, he obtained his master's degree in Robotics, Systems, and Control from ETH. His educational background also includes undergraduate studies at Universita' degli studi di Padova in Italy. During his academic journey, he gained research experience at DeepMind (UK), Meta (US), MILA (CA), INRIA (FR), and HILTI (LI). Dr. Orvieto's research spans two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. His work particularly focuses on decoding patterns in sequential data, with applications in biology, neuroscience, natural language processing, and music generation. His theoretical approach to deep learning has led to significant contributions in understanding recurrent neural networks, transformers, and optimization methods. His recent publications reveal a strong focus on sequence modeling, optimization theory, and the theoretical foundations of deep learning, with particular emphasis on improving training efficiency and model performance. Schmidt Sciences AI2050 Early Career Fellow Dr. Orvieto actively mentors PhD students and leads a vibrant research group at the Max Planck Institute for Intelligent Systems. His Deep Models and Optimization group includes PhD students Destiny Okpekpe, Felix Sarnthein, Diganta Misra, Sajad Movahedi, and Wenjie Fan, among others. He is deeply involved in doctoral education as faculty for multiple PhD programs including CLS, ELLIS, and IMPRS-IS. His teaching includes the course "Nonconvex Optimization for Deep Learning" at the University of Tübingen. The Deep Models and Optimization research group investigates the interplay between optimizers and architectures in deep learning, with a focus on developing new networks for long-range reasoning. The group's mission is to design new optimizers and neural networks to accelerate technology and scientific discovery, with a strong theoretical foundation in optimization theory. They strongly believe that deep learning will revolutionize science and technology, and they aim to make powerful deep learning solutions accessible to scientists and engineers regardless of resource limitations.

• Smart Grids
• Power System Optimization
• Renewable Energy Integration
• +۷ مورد دیگر

Souhil Mouassa is a Spanish-based power-systems researcher who earned his PhD in 2021 from the University of Jaén , Spain, under the supervision of Dr. Francisco Jurado Melguizo. He is currently affiliated with the Department of Electrical Engineering at the same university, where he pursues advanced studies in smart-grid optimisation, renewable-energy integration and meta-heuristic algorithms. Education PhD in Electrical Engineering, University of Jaén, Spain (2021) Thesis: Optimisation de l’écoulement de puissance par les méthodes non-conventionnelles dans les réseaux électriques intelligents (Smart Grid) Research Interests Mouassa’s work spans the entire spectrum of modern power-systems engineering, focusing on: Optimal power-flow computations for hybrid thermal-wind-solar networks Meta-heuristic and bio-inspired optimisation (Electric Eel, Gorilla Troops, Kepler, Bonobo, Dwarf Mongoose, Tuna Swarm, AEO, GTO, FFA, etc.) Integration of FACTS devices (TCSC, SVC, UPFC) to enhance grid stability and power quality Smart-home and smart-grid energy-management systems with storage and demand response Photovoltaic systems, MPPT techniques and active-power filtering under non-ideal grid conditions Stochastic modelling of renewable-generation uncertainty and its impact on isolated and large-scale networks Power-electronics control—Lyapunov-based adaptive sliding-mode schemes for DC-DC converters Publication Profile Between 2017 and 2025 Mouassa authored more than thirty peer-reviewed papers, concentrated in high-impact energy, power-engineering and applied-soft-computing journals. His 2024-2025 output alone includes nine articles that apply cutting-edge swarm and evolutionary algorithms to real-world optimal-power-flow and energy-management problems, demonstrating a clear trajectory toward large-scale, practical implementation of renewable-rich power systems. Notable case studies include the isolated Adrar network in Algeria and various European-scale transmission systems. Scientific Awards No specific prizes, fellowships or medals have been publicly listed to date. Advising & Grants No supervised students or funded projects are explicitly mentioned in the provided materials. Laboratories & Teams Mouassa carries out his research within the Department of Electrical Engineering research group at the University of Jaén, accessing power-system simulation laboratories and computational clusters necessary for large-scale optimisation studies.

Jan Kofroňمشاهده پروفایل

دانشیار

• Program Verification
• Static Analysis
• Software Reliability
• +۴ مورد دیگر

Jan Kofroň is an Associate Professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research focuses on program verification, static analysis, and software reliability. His educational background includes a Ph.D., as indicated by his title. He maintains an active research profile with numerous recent publications in top venues. Professor Kofroň's research interests span several key areas in software engineering and formal methods. He specializes in interpolation-based code model checking, static analysis of programs, system behavior models and verification, and programming language semantics. His work bridges theoretical foundations with practical applications in software development. His recent publications demonstrate strong focus on verification techniques for complex software systems, particularly in the areas of Horn clause solving, program comprehension using computational notebooks, and uncertainty-aware self-adaptive cyber-physical systems. His research shows consistent contributions to both theoretical foundations and practical applications of program analysis. Professor Kofroň actively leads and participates in multiple research projects including the current AIDE project (Advanced Analysis and Verification for Advanced Software) and past projects such as SNAPPY, ROBUST, Weverca, Q-ImPrESS, SOFA 2, and ASCENS. He teaches courses related to Java programming, Python programming, program semantics, and mobile devices programming. He maintains an open-door policy for student consultations, requesting students email him to arrange meetings rather than maintaining fixed office hours. His department maintains active GitHub presence, contributing to open-source projects related to software verification and analysis tools.

• Mathematical Physics
• Quantum Graphs
• Spectral Theory
• +۴ مورد دیگر

Prof. Pavel Exner is a faculty member at the Czech Technical University, affiliated with the Faculty of Nuclear Sciences and Physical Engineering. He maintains a joint appointment at the Nuclear Physics Institute of the Czech Academy of Sciences. His research focuses on mathematical physics, particularly quantum graphs, spectral theory of Schrödinger operators, and quantum transport phenomena. Exner's research explores spectral properties of quantum systems, including geometric perturbations, magnetic field effects, and optimization of eigenvalues. His interests span quantum graphs, Dirac operators, lattice transport, and inverse problems. Recent work emphasizes vertex couplings, spectral statistics, and asymptotic behavior of quantum systems under various boundary conditions. Analysis of Exner's 15 most recent publications reveals consistent focus on spectral theory in quantum mechanics, particularly quantum graphs with specialized vertex conditions. Key themes include magnetic transport phenomena, spectral optimization, lattice systems, and inverse problems. The works demonstrate advanced mathematical techniques in operator theory, perturbation analysis, and asymptotic methods applied to quantum systems. Exner has received the Václav Votruba Prize for his scientific contributions. He directs research activities at the Doppler Institute for Mathematical Physics and Applied Mathematics, organizing seminars including the Doppler Institute Seminar (DIS) and Quantum Circle (QC).

• Regional climate and environmental modelling
• Climate impact research
• Geoinformatics and remote sensing
• +۶ مورد دیگر

Prof. Dr. Jürgen Böhner is a Full Professor at the University of Hamburg's Department of Earth System Sciences, where he leads research on climate-environment interactions. His office is located in the Geomatikum building (Room 814), with regular office hours on Thursdays from 3-4 PM. He coordinates the Climate, Climate Change and Society (CliCCS) Cluster of Excellence and directs the SAGA-GIS development initiative. His research focuses on: Regional climate modelling with emphasis on high-mountain and tropical systems Climate impact assessments for Himalayan treelines and Amazonian ecosystems Advanced geospatial methods through Geoinformatics and remote sensing SAGA-GIS software development for environmental applications Process-based analysis of land degradation and soil conservation His publications demonstrate interdisciplinary approaches combining climate modelling, geoinformatics, and field validation. Recent work emphasizes: High-resolution climate projections for forest inventories Glacial lake mapping using CubeSat technology Social-ecological tipping points in the Amazon Novel validation methods using social media data Irrigation impacts on regional climate systems He leads an extensive research portfolio including: TREELINE-II (DFG, 2023-2026): Himalayan treeline sensitivity to warming PRODIGY 2/BMBF (2023-2025): Amazon tipping point management CliCCS/DFG Excellence Cluster (2019-2025): Climate-society interactions LANDMATE (2018-2023): Human land surface modification feedbacks CHELSA (ongoing): High-resolution global climatologies He advises 17 researchers and students working on climate modelling, remote sensing, and environmental process analysis. His team collaborates with institutions across Europe, South Asia, and South America.

• Computer Science
• Numerical Algorithms
• Cryptography
• +۳ مورد دیگر

Dr. Joanna Kapusta is an Assistant Professor in the Department of Applied Computer Science at the John Paul II Catholic University of Lublin, Poland. She is affiliated with the Faculty of Natural and Technical Sciences, where she conducts research in computational mathematics and cryptography. Her work primarily focuses on the design and optimization of algorithms for polynomial interpolation, numerical transformations, and cryptographic protocols. Her research interests span several key areas: Numerical Algorithms : Development of efficient methods for polynomial evaluation, interpolation, and transformation. Cryptography : Designing secure systems for secret sharing, e-voting, and encryption. Computational Mathematics : Applications in knot theory, semigroup evaluation, and matrix operations. Her publications demonstrate a consistent focus on optimizing computational efficiency in mathematical operations, with applications ranging from cryptographic security to educational tools. Articles frequently involve collaborative work, particularly with co-author Ryszard Smarzewski. No awards, students, or grant activities are mentioned in the provided materials.

• Computer Vision
• Scene Flow Estimation
• Optical Flow
• +۴ مورد دیگر

Lukas Mehl is a Researcher at the Computer Vision Department within the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His work focuses on scene flow estimation, optical flow, and adversarial attacks in motion estimation. Education : Not explicitly mentioned in the text. Research Interests : He develops multi-frame fusion techniques for scene flow estimation, creates high-resolution benchmark datasets like Spring, and explores adversarial weather effects on motion estimation. His methods improve accuracy in automotive and computer vision applications. Scientific Awards : No awards mentioned in the text. Advising & Grants : Supervised multiple Master's and Bachelor's theses on topics including occlusion-aware optical flow and motion-compensated frame interpolation. Participated in research projects like Pre-training ProFlow. Labs & Teams : Collaborates with the VIS team at the University of Stuttgart, contributing to tools like the Spring benchmark website for evaluating motion estimation algorithms.

Pierre Dutilleulمشاهده پروفایل

استاد

• Environment
• Ecology
• Agriculture
• +۷ مورد دیگر

Pierre Dutilleul is a Professor at McGill University's Macdonald Campus, associated with the McGill School of Environment and the Department of Mathematics and Statistics. His research focuses on spatio-temporal heterogeneity, plant and soil imaging, and fractal analysis of natural structures. Current affiliations: Department of Plant Science, McGill School of Environment Specializations: CT scanning for root and wood imaging, multifractal analysis, soil aggregate dynamics Research interests span environmental sustainability , soil science , and plant production , with recent work on 3D root phenotyping, biochar characterization, and space-time modeling of agricultural and seismic events. Articles demonstrate expertise in applied statistics and computational methods for ecological and agricultural systems. Key methodological contributions include: Development of R packages for spatio-temporal analysis Innovative CT scanning applications in plant and soil research Statistical tests for separable variance-covariance structures

• Medical Imaging
• Signal Processing
• Computational Mathematics
• +۳ مورد دیگر

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center, with secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). He holds a Ph.D. from the University of Iowa and completed postdoctoral training in Cardiac MRI at the same institution. Ph.D., University of Iowa Postdoctoral Training, University of Iowa (Cardiac MRI) Dr. Zou's research bridges medical imaging and computational mathematics , focusing on advanced MRI reconstruction techniques, manifold modeling, and mathematical algorithms for biomedical applications. His work combines signal processing , deep learning , and partial differential equations to address challenges in dynamic cardiac imaging and surface reconstruction. The 15 most recent articles highlight his expertise in cardiac MRI optimization , manifold-based image reconstruction , and combinatorial mathematics . Notable contributions include MoCo-SToRM (2022) for motion-compensated imaging and Gen-SToRM (2021) for deep generative models in dynamic imaging. His mathematical work on q-Catalan numbers and Fubini sequences demonstrates a parallel focus on number theory and discrete mathematics. Dr. Zou is affiliated with the Advanced Imaging Research Center , where he collaborates on interdisciplinary projects integrating computational methods with clinical cardiology . His publications reflect a sustained effort to translate mathematical innovations into clinical imaging technologies.

• Hydrogeology
• Karstic Aquifers
• Remote Sensing
• +۵ مورد دیگر

Associate Professor Bedri Kurtulus is affiliated with Muğla Sıtkı Koçman University , where he serves in the Faculty of Engineering under the Department of Geological Engineering . His academic journey includes a Bachelor's from Hacettepe University, a Master's from Université de Poitiers, and a Ph.D. in Hydrogeological Engineering from Hacettepe University. Education: B.Sc. Geological Engineering, Hacettepe University M.Sc. Hydrogeology, Université de Poitiers Ph.D. Hydrogeological Engineering, Hacettepe University Research Interests: His work focuses on karstic aquifers , groundwater modeling , remote sensing applications in hydrology, and environmental pollution assessment . He employs computational intelligence methods like ANFIS and neural networks to analyze hydrological systems and has pioneered studies on microplastic transport in porous media. Recent Publications highlight his expertise in GIS-based interpolation techniques , thermal stratification of lakes , and karst spring characterization . He has mentored multiple students in projects ranging from groundwater resilience to climate change to UAV-based hydrological monitoring .