جست‌وجوی استاد و پژوهشگر

Dahiana Arcilaمشاهده پروفایل

استادیار

• Phylogenetics
• Systematics
• Biogeography
• +۳ مورد دیگر

Dahiana Arcila is an Assistant Professor at UC San Diego's Scripps Institution of Oceanography in the Marine Biology department. Her research employs genomic and morphological approaches to study the evolutionary history of marine fishes, with particular focus on phylogenetic relationships, biogeographic patterns, and adaptations to deep-sea environments and past climate change events. Education: B.S., Universidad del Quindío, Colombia PhD, The George Washington University Dr. Arcila leads the Fish Evolution Lab, where her team investigates fundamental questions about fish diversification using cutting-edge genomic technologies and comparative methods. Her research program bridges paleontology, developmental biology, and computational genomics to understand how environmental changes shape biodiversity. Recent publications demonstrate Arcila's focus on deciphering evolutionary patterns in diverse fish groups through phylogenomic approaches. Her work consistently explores themes of adaptive radiation, genomic adaptation, morphological innovation, and the impact of historical climate events on marine diversification. These studies employ diverse methodologies from micro-CT scanning to genome-wide selection scans.

Yairon Cid Ruizمشاهده پروفایل

استادیار

• Commutative Algebra
• Algebraic Geometry
• Blow-up Algebras
• +۸ مورد دیگر

Yairon Cid Ruiz is an Assistant Professor in the Department of Mathematics at North Carolina State University, located in Raleigh, North Carolina. His research focuses on Commutative Algebra and Algebraic Geometry, with particular interests in blow-up algebras, local cohomology, multiplicities, differential operators, Hilbert schemes, symbolic powers, syzygies, and regularity. He holds a PhD in Mathematics from Universitat de Barcelona (2019). His academic career includes affiliations with the College of Sciences at NC State University, where he contributes to the Algebra and Combinatorics research groups. He actively engages in interdisciplinary research at the intersection of algebraic structures and geometric interpretations, leveraging computational tools like Macaulay2 for symbolic computation. Research Trends: Recent publications (2022-2025) emphasize foundational advances in commutative algebra, including studies on generalized Jouanolou duality, effective generic freeness, and log-concavity properties in equivariant cohomology. His work bridges abstract algebraic concepts with geometric applications, often addressing structural properties of algebras and modules. Awards: No scientific awards explicitly mentioned in the provided materials. Advising & Grants: No specific advising or grant information provided. His research is supported through standard university resources and collaborative projects within the Algebra and Combinatorics research groups. Labs/Teams: Participates in the Algebra and Combinatorics Research Group and Symbolic Computation initiatives at NC State University's Mathematics Department, contributing to both theoretical research and computational methodology development.

Irina Koganمشاهده پروفایل

استاد

• Differential Geometry
• Algebraic Geometry
• Computational Mathematics
• +۳ مورد دیگر

Irina Kogan is a Professor in the Department of Mathematics at NC State University, part of the College of Sciences. She holds a PhD in Mathematics from the University of Minnesota (2000). Her research focuses on geometric study of differential equations, equivalence and symmetry problems, computational invariant theory, and symbolic computation. She is affiliated with the Symbolic Computation Research Group and the Topology, Geometry, and Mathematical Physics Research Group. Dr. Kogan's recent work emphasizes differential invariants, object-image correspondence under projections, and computational methods in algebraic geometry. Her articles span topics like curve reconstruction, moving frames, and applications of invariant theory in computer vision and physics. Key contributions include studies on non-congruent curves with identical signatures and minimal-degree affine frames for polynomial curves. Her research has addressed hyperbolic conservation laws, integrability theorems of Darboux, and algorithmic approaches to μ-bases and Jacobians. While no awards are explicitly listed, her extensive publication record reflects sustained contributions to geometric and algebraic research. She collaborates on projects like the NSF-funded 'Fundamental Challenges in Nonlinear Hyperbolic PDEs' (2013). Dr. Kogan's affiliations include the Department of Mathematics office at SAS Hall 3146, and she maintains an active website. Her work bridges theoretical mathematics with computational applications, particularly in symbolic computation and geometric modeling.

• Robotics
• Control Systems
• Automation
• +۱۰ مورد دیگر

ABDERRAHIM FICHOUCHE, MOHAMED is an Associate Professor in the Department of Systems Engineering and Automation at the Carlos III University of Madrid (UC3M), where he also serves as Deputy Director of the Doctorate. He is affiliated with the Robotics Lab research group and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. His academic and research profile spans robotics, control systems, biomedical engineering, and power systems. His research interests include robotics , automation , control systems , biomedical signal processing , computer vision , and renewable energy integration . His work focuses on intelligent robotic systems, rehabilitation robotics, EEG-based brain-computer interfaces, fault detection in power systems, and advanced control strategies for industrial and autonomous systems. The recent publications reflect a strong trend in interdisciplinary research, combining machine learning with signal processing for applications in medical diagnostics , robotic control , and smart grid monitoring . His work increasingly integrates deep learning , wavelet analysis , and adaptive control across domains. He has led major research projects such as: HANDLE (EU and national funding): Focused on dexterous in-hand manipulation. SARAH: Enhancing robot autonomy with artificial hands. PAPREC: Automatic grasping of disordered parts. PROSAVE: Eco-efficient aircraft systems. GRAND-PA: Assistive technologies for the elderly. He currently participates in ongoing projects including: SEGVAUTO5G-CM (2025–2028): Future mobility innovation. RoboCity2030-DIH-CM (2019–2023): Madrid Robotics Digital Innovation Hub. HYPER: Neuroprosthetic and neurorobotic devices for rehabilitation. He has supervised several theses on topics such as transmission line fault detection , autonomous decision-making in robots , and 3D perception . His research is conducted primarily within the Robotics Lab at UC3M, a multidisciplinary team focusing on advanced robotic systems for industrial, medical, and service applications.

• Computer Science
• Artificial Intelligence
• Antenna Engineering
• +۷ مورد دیگر

Josefa Gómez Pérez is a Professor in the Department of Computer Science at Universidad de Alcalá. She holds a PhD in Computer Science from the same institution (2011), focusing on CAD tools for electromagnetic analysis of complex structures under supervision of Dr. Manuel Felipe Cátedra Pérez and Dr. Iván González Diego. Her research spans multiple areas including antenna design optimization, numerical methods for engineering problems, gamification in education, medical image analysis, and computer vision applications. She is affiliated with research groups such as the Climate Physics Group (CPG), Group of Advanced Numerical Techniques (GTNA), and TIFyC (Information Technologies for Training and Knowledge). Her work frequently integrates bio-inspired algorithms and web-based tools for solving engineering challenges. Key areas of contribution include developing simulation tools for radio propagation using OpenStreetMap data, optimizing antenna positioning with genetic algorithms, and enhancing educational engagement through gamified platforms. She has also explored interdisciplinary topics like class imbalance mitigation in medical AI and gender perspectives in engineering career choices. Josefa has authored numerous publications in top venues, with recent focus on AI-human interaction, 3D reconstruction techniques, and educational technology innovations. Notable tools she has developed include the NewFasant Suite, DSEXAMS automated questionnaire system, and LearningRlab educational package.

Arne Sachtlerمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Differential Geometry
• +۳ مورد دیگر

Arne Sachtler is a researcher at the Technical University of Munich (TUM) and the German Aerospace Center (DLR), affiliated with the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems under Prof. Alin Albu-Schäffer. His research focuses on applying differential geometry and nonlinear dynamics to robotics, particularly in areas like elastic robots, energy-efficient locomotion principles, and intrinsic dynamics of mechanical systems. He specializes in robot control strategies, including impedance control and eigenmanifold-based model predictive control (EigenMPC). Education details are not explicitly listed, but his work indicates expertise in robotics and mathematical physics. Key research interests include robot kinematics, multi-body dynamics, and the integration of geometric principles into control frameworks. His publications span topics such as nonlinear normal modes, redundant manipulator control, and autonomous robotic assembly with visual-tactile sensing. Dr. Sachtler collaborates with institutions like DLR, contributing to both academic and industrial robotics research. His work emphasizes theoretical foundations with practical applications in energy-efficient robotics and human-robot interaction. Contact him via arne.sachtler@tum.de or arne.sachtler@dlr.de for inquiries.

Adrian Sampsonمشاهده پروفایل

دانشیار

• Computer Architecture
• Programming Languages
• Hardware-Software Co-Design
• +۴ مورد دیگر

Adrian Sampson is an Associate Professor in the Department of Computer Science at Cornell University, where he is part of the Computer Systems Laboratory and the Programming Languages Group. He leads the Capra research group, focusing on programming languages, computer architecture, and hardware-software co-design. Previously, he was an Assistant Professor at Cornell (2016–2022) and a researcher at Microsoft and Google. Education: Ph.D. in Computer Science and Engineering from the University of Washington (2015), advised by Luis Ceze and Dan Grossman. Dissertation: Hardware and Software for Approximate Computing . B.S. in Computer Science from Harvey Mudd College (2009). Research interests include approximate computing, compiler optimization, hardware accelerators, and formal methods in systems programming. His work on Geometry Types and Reticle has advanced abstractions for graphics programming and FPGA design. Sampson is known for contributions to compiler infrastructure for accelerators, such as the Timeline Types framework for predictable hardware design. Awards include the Cornell Bowers CIS Research Excellence Award (2024), IEEE TCCA Young Computer Architect Award (2021), NSF CAREER Award (2019), and the Google Ph.D. Fellowship in Computer Architecture (2013–2015). He has advised over 30 graduate and undergraduate students, contributing to projects like compiler-driven accelerator simulation and vectorization techniques. Teaching includes courses on compilers, programming languages, and computer architecture at Cornell. He has served on program committees for major conferences (ASPLOS, PLDI, ISCA) and chairs roles in ISCA and ASPLOS. Sampson’s research is supported by grants from NSF, Google, and industry partnerships.

Jaime Camelioمشاهده پروفایل

استاد

• Cyber-Physical Systems Security
• Smart Manufacturing
• Statistical Process Control
• +۷ مورد دیگر

Jaime Camelio is a Professor at the University of Georgia's School of Electrical & Computer Engineering, specializing in cyber-physical systems security, smart manufacturing, and statistical process control. His work integrates advanced technologies like machine learning, reinforcement learning, and Bayesian inference to enhance manufacturing resilience and safety. He focuses on vulnerabilities in production systems, digital twin applications, and data-driven quality control. His research spans aerospace composites, IIoT-enabled worker well-being monitoring, and synthetic data frameworks for manufacturing simulations. Research interests include cybersecurity in additive manufacturing, occupational safety monitoring via statistical control charts, and innovative approaches to fault detection in assembly systems. He leads projects funded by NSF CPS initiatives, such as collaborative research on manufacturing security and cyber-physical vulnerability assessments. His work bridges theoretical advancements with real-world applications in aerospace, automotive, and industrial IoT sectors. Scientific contributions include frameworks for digital thread integration in product lifecycle management and NURBS-based statistical monitoring of manufacturing surfaces. Publications emphasize both technical innovations and systemic risk mitigation strategies for modern manufacturing ecosystems. His lab explores cutting-edge topics like LLM applications in manufacturing workflows and random sampling strategies to counteract cyber-physical attacks.

Søren Haubergمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +۱۱ مورد دیگر

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

• Differential Geometry
• Complex Geometry
• Lie Algebras
• +۳ مورد دیگر

Stefano Marini serves as a fixed-term Researcher at the Department of Mathematical, Physical and Computer Sciences, University of Parma. His teaching portfolio includes: Mathematics (First cycle degree, Gastronomic Science, 2025/2026) Algebra and Geometry (First cycle degree, Computer Science, 2024/2025) Geometry 1 - mod 2 (First cycle degree, Mathematics, 2024/2025) Geometry and Algebra (First cycle degree, Information Systems Engineering, 2020/2021) His research expertise spans: Differential Geometry Complex Geometry Lie Algebras CR Geometry Homogeneous Spaces Algebraic Geometry Marini's scholarly work centers on the geometric analysis of CR manifolds and Kähler structures, with particular emphasis on homogeneous models, nondegeneracy conditions, and Levi form theory. His investigations frequently employ Lie group actions and algebraic classification methods to address fundamental questions in complex differential geometry. Analysis of his 13 publications (2017-2024) reveals a cohesive research trajectory focused on CR geometry and Lie algebras, with 12/13 articles in pure mathematics. His recent work (2022-2024) explores projectively induced Kähler cones, root involutions in real forms, and finitely nondegenerate CR structures. The sole interdisciplinary exception (2019) applies GPU-accelerated optimization to terrestrial laser scanning in computer science. No information is available regarding academic advising, research grants, laboratory affiliations, or professional awards.

Henrietta Tománمشاهده پروفایل

استادیار

• Medical image processing
• Fusion systems
• Quasigroups
• +۳ مورد دیگر

Dr. Henrietta Tomán serves as an Assistant Professor in the Department of Data Science and Visualization at the University of Debrecen's Faculty of Informatics, where she bridges advanced mathematical theory with practical medical imaging applications. Her work integrates abstract algebraic structures with cutting-edge AI systems to solve critical healthcare challenges. Her research portfolio spans three interconnected domains: Medical image processing (particularly ensemble-based segmentation and quality assessment for ophthalmic diagnostics) Geometric structures (quasigroups, loops, and differentiable manifolds) Stochastic optimization for resource-constrained AI systems Analysis of her publication trajectory reveals evolving expertise: early work (2010-2014) established foundations in geometric loop theory applied to image processing, while recent research (2020-2024) pioneers stochastic fusion techniques for medical image ensembles under computational constraints. Her most significant contributions involve translating mathematical abstractions into robust clinical decision-support tools, particularly in diabetic retinopathy detection and epidemic modeling. Current work demonstrates increasing focus on real-time AI systems that maintain accuracy under hardware limitations, reflecting urgent needs in telemedicine and mobile health applications. Dr. Tomán maintains active collaboration within the Doctoral School of Informatics and contributes to Hungary's national research initiatives in medical AI, with consistent publication output in top-tier venues spanning computer vision, medical imaging, and mathematical computing.

Franko Šikićمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Retail Analytics
• +۳ مورد دیگر

Franko Šikić is a Researcher at the Department of Electronic Systems and Information Processing within the Faculty of Electrical Engineering and Computing (FER) at the University of Zagreb. His work bridges computer vision and practical retail applications, focusing on real-world problem solving through deep learning methodologies. His research concentrates on computer vision systems for retail environments and human-computer interaction, particularly in out-of-stock detection, price tag recognition, and gaze estimation. Key interests include applying convolutional neural networks to shelf monitoring challenges and developing attention-based architectures for unconstrained gaze prediction. Recent publications demonstrate a clear progression toward sophisticated retail analytics solutions, with increasing emphasis on multi-task learning for shelf image analysis. His 2025 gaze estimation work introduces novel cross-attention mechanisms, while retail publications consistently address practical constraints like variable lighting and occlusion in supermarket settings.

Andreas Wichmannمشاهده پروفایل

استاد

• Cartography
• Geovisualization
• 3D Metrology
• +۱۰ مورد دیگر

Prof. Dr.-Ing. Andreas Wichmann is a faculty member at the Carl von Ossietzky University of Oldenburg, holding the Chair of Cartography and Geovisualization. He contributes to the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and leads research in 3D metrology, GIS, and geospatial data systems. Academic Rank: Professor Department: Cartography and Geovisualization Labs: Laboratory for Optical 3D Metrology His research focuses on 3D building reconstruction, LiDAR data analysis, and digital mapping applications. Recent projects include deep learning for building segmentation, Scan2BIM optimization, and 3D visualization of cultural heritage sites like the Bachmann Museum in Unreal Engine. Key trends in his publications involve integrating photogrammetry with machine learning, improving GIS methodologies, and developing geospatial datasets for civil protection and heritage preservation. Scientific Awards ASPRS Esri Award for Best Scientific Paper in GIS (2020) ISPRS Best Paper Award (2018) AOLS Best Poster Award (2016) Prof. Wichmann supervises final theses across M.Sc. and B.Sc. programs in Geoinformatics and Applied Geodesy. His collaborations include the Lower Saxony State Office for Geoinformation and Surveying (LGLN) and private engineering firms.

Artyom Grigoryanمشاهده پروفایل

دانشیار

• Quantum Computing
• Image Processing
• Signal Processing
• +۳ مورد دیگر

Artyom Grigoryan is an Associate Professor at the Klesse College of Engineering and Integrated Design within The University of Texas at San Antonio, specializing in the Department of Electrical and Computer Engineering. His research bridges quantum computing and advanced image/signal processing techniques, with a focus on novel transforms and mathematical frameworks. Ph.D. in Electrical Engineering from Moscow Institute of Physics and Technology Contact: BSE 1.522, UTSA Research Interests : Quantum image processing, signal-induced heap transforms, quaternion-based algorithms, and thermal imaging enhancements. His work explores quantum representations of signals, fast transforms for multi-qubit systems, and entropy-driven segmentation methods applicable to medical and underwater imaging. Recent Publications highlight trends in quantum circuit design, color restoration for Renaissance art, lightweight neural networks for solar systems, and mathematical tools for quantum signal analysis. Notable contributions include quantum edge detection, state-to-state transformations, and golden ratio applications in vector similarity.

Christian Kokeمشاهده پروفایل

پژوهشگر

• Machine Learning
• Graph Neural Networks
• Spectral Convolutions
• +۵ مورد دیگر

Christian Koke is a PhD student and AI Researcher at the Technical University of Munich (TUM), affiliated with the Computer Vision Group under Informatics 9 (Department of Informatics). He is jointly supervised by Prof. Daniel Cremers (TUM) and Prof. Michael Bronstein (Oxford University), with additional collaborations with Bastian Rieck . His academic background includes M.Sc. degrees in Mathematics and Theoretical Physics from Ludwig Maximilian University Munich (LMU) and TU Munich, where he studied under Prof. Gitta Kutyniok on graph neural networks. Education: PhD in Computer Science (ongoing), TUM M.S. in Mathematics, Ludwig Maximilian University Munich M.S. in Theoretical Physics, LMU & TUM B.S. in Physics, Heidelberg University His research focuses on the mathematical foundations of machine learning , particularly in graph neural networks, directed graphs, and spectral methods. He explores stability guarantees, multi-scale consistency, and geometric deep learning frameworks, with applications in quantum technologies and theoretical physics. His work bridges abstract mathematical concepts with practical AI advancements. Christian has presented at major conferences, including ICLR 2024 (oral presentation), NeurIPS 2023 Workshops , and Learning on Graphs events in Paris and Madrid. His publications include theoretical and applied contributions to graph learning and physics-inspired AI. Awards: Outstanding Extended Abstract, ICML 2024 Workshop on Geometry-grounded Representation Learning He is an ELLIS PhD Student and collaborates with labs at TUM, Oxford, and Ecole Polytechnique. His work has been featured in the Computer Vision Group at TUM, with a focus on foundational research rather than applied projects.