جست‌وجوی استاد و پژوهشگر

• Computational Geometry
• Algorithms
• Graph Theory
• +۳ مورد دیگر

Marcel J.M. Roeloffzen serves as University Lecturer in the Applied Geometric Algorithms group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His prior academic appointments include Assistant Professor roles at Japan's National Institute of Informatics (2016-2018) and Tohoku University (2014-2015), plus a Postdoc position at the National Institute of Informatics (2015-2016). His research centers on computational geometry and algorithm design, with specialized expertise in geometric data structures under uncertainty, dynamic graph algorithms, and spatial data processing. Key contributions address Fréchet distance computations for uncertain curves, physically constrained map matching, and dynamic graph coloring optimization. His methodological approach combines theoretical analysis with experimental validation of algorithmic performance. Analysis of recent publications (2022-2024) reveals consistent focus on developing practical geometric algorithms that handle real-world constraints like input uncertainty and dynamic updates. His work bridges theoretical computer science with applications in geographic information systems and data analysis, emphasizing both computational efficiency and physical plausibility. Roeloffzen teaches core computer science courses including Advanced Algorithms, Data Structures, Discrete Structures, and Logic and Set Theory. He has supervised 18 graduate students and maintains active collaboration within the computational geometry research community. His research operates within TU/e's Applied Geometric Algorithms group, which specializes in developing algorithmic solutions for geometric data processing challenges across scientific and industrial domains.

Ranxiao Frances Wangمشاهده پروفایل

استاد

• Attention and Perception
• Spatial Cognition
• Visual Perception
• +۷ مورد دیگر

Ranxiao Frances Wang is a Professor of Psychology at the University of Illinois, with additional appointment at the Beckman Institute for Advanced Science and Technology. She holds her office in the Psychology Building and maintains an active research laboratory focused on spatial cognition and perception. Dr. Wang earned her Ph.D. from Massachusetts Institute of Technology before joining the University of Illinois faculty. Her research program spans multiple domains within cognitive psychology, particularly focusing on how humans process spatial information and attention. Her primary research interests include mind wandering and cognitive control, human-robot interaction, biologically-informed artificial intelligence, visual perception, and spatial memory. She has developed innovative methodologies to study spontaneous cognitive processes, particularly in meditation contexts, and has pioneered research on human navigation in non-Euclidean spaces. Her work bridges cognitive psychology with robotics, quantum physics, and virtual reality technologies. Analysis of her recent publications reveals a strong emphasis on the cognitive mechanisms underlying attentional states, spatial navigation in both Euclidean and non-Euclidean environments, and the application of cognitive principles to human-robot interaction. Her research consistently employs rigorous experimental methods combined with computational modeling to uncover fundamental principles of human cognition. Dr. Wang has received consistent research funding that has supported numerous graduate students and collaborative projects across disciplines. Her work has been published in top-tier journals across psychology, neuroscience, and human-computer interaction fields. She actively mentors graduate students in the Psychology Department and collaborates with faculty from multiple departments including Engineering, Computer Science, and Physics. Her laboratory maintains cutting-edge virtual reality equipment for spatial cognition research.

Jennifer McLoud-Mannمشاهده پروفایل

استاد

• Discrete Mathematics
• Knot Theory
• Tiling Theory
• +۳ مورد دیگر

Jennifer McLoud-Mann is a Dean and Professor in the School of Science, Technology, Engineering & Mathematics (STEM) at University of Washington Bothell . She earned her Ph.D. in Mathematics from the University of Arkansas (2002) and a B.S. in Mathematics from East Central University (1997). She joined UW Bothell in 2013 as a tenured associate professor after serving as Associate Dean at the University of Texas at Tyler. Education Ph.D., Mathematics, University of Arkansas (2002) B.S., Mathematics, East Central University (1997) Research Interests focus on discrete mathematics , including knot theory , tiling theory , and combinatorics . She actively involves undergraduate students in research, notably leading to the discovery of the 15th pentagon tiling of the plane in 2015 with David Von Derau. Her work spans geometric and algebraic domains, with publications on lattice stick numbers, fullerene extensions, and determinantal ideals. Recent publications highlight contributions to tiling theory (e.g., 2016 SIAM News article on pentagon tilings), knot theory (e.g., 2012-2005 studies on lattice stick numbers), and algebraic structures (2005 paper on Veronese rings). Earlier work (1998) explores astronomy education methodologies. Grants : Principal Investigator for the National Science Foundation (NSF) Research Experiences for Undergraduates (REU) grant, establishing UW Bothell's first NSF REU site in mathematics.

• Geomatic Engineering
• Physical Geography
• Environmental Management
• +۶ مورد دیگر

Philip Dennison is a Professor at the School of Environment, Society & Sustainability , University of Utah, with a career spanning two decades in Geography . He holds degrees from Penn State University (BS) and University of California Santa Barbara (MA, PhD). Research focus on remote sensing, ecological modeling, and wildfire management Teaching includes courses like Intro to Remote Sensing and thesis supervision His work bridges geoinformatics and atmospheric sciences , addressing challenges in non-photosynthetic vegetation monitoring , firefighter safety zone design , and global greenhouse gas quantification . Recent studies explore airborne and spaceborne lidar for biomass estimation in piñon-juniper woodlands and crowdsourced GPS data for pedestrian mobility modeling. Collaborative efforts include NASA's EMIT mission for methane attribution and FIREX-AQ thunderstorm measurements. His machine learning frameworks (random forests, XGBoost) enhance visibility mapping and wildfire risk prediction.

Aleksandr Popovمشاهده پروفایل

پژوهشگر

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +۲ مورد دیگر

Aleksandr Popov is a researcher in computational geometry currently affiliated with the ALGO cluster at Eindhoven University of Technology. He completed his PhD in 2023 focusing on algorithms for uncertain trajectories and continues active research in this domain. Education: PhD in Algorithms for Imprecise Trajectories, Eindhoven University of Technology, 2023 Master's in Similarity of Uncertain Trajectories, Eindhoven University of Technology, 2019 Dr. Popov's research centers on computational geometry with emphasis on trajectory analysis under uncertainty . His work develops algorithms to quantify trajectory similarity using Fréchet distance, simplify complex paths while preserving essential features, and handle imprecise spatial data. His research bridges theoretical computer science with practical applications in movement pattern analysis and spatial databases, addressing fundamental challenges in how uncertainty affects geometric computations. Analysis of his publication record reveals a systematic progression from theoretical foundations of uncertain curve analysis to more applied problems like map-matching and trajectory clustering. His work consistently explores how measurement uncertainty impacts geometric algorithms, developing specialized methods that maintain computational efficiency while accounting for data imprecision. A distinctive feature of his research is the adaptation of classical geometric measures like Fréchet distance to probabilistic and uncertain settings. Dr. Popov has substantial teaching experience, having assisted with bachelor courses on data structures and algorithms (2021-2023), supported master seminars on algorithms (2019-2021), and served as a student assistant for various courses during his bachelor and master studies (2014-2019). He also contributed as an editorial assistant for Dagstuhl Seminar reports on computational geometry, demonstrating engagement with the broader academic community. As an active member of the ALGO cluster at Eindhoven University of Technology, Dr. Popov collaborates with leading researchers including Kevin Buchin, Maarten Löffler, and Marcel Roeloffzen, contributing to a vibrant research environment focused on theoretical and applied aspects of algorithm design for geometric problems.

• Discrete Mathematics
• Graph Theory
• Computational Geometry
• +۴ مورد دیگر

Mercè Mora Gine is a researcher at the Polytechnic University of Catalonia , affiliated with the Barcelona School of Informatics (FIB) and the Department of Mathematics . She works within the DCCG research group (Discrete, Combinational, and Computational Geometry), focusing on graph theory and its applications. Research Interests: Discrete mathematics, graph theory, metric dimension, computational geometry, combinatorial optimization Projects: Key contributor to EngiMath@UPC+ blended learning initiative for engineering mathematics Email: MERCE.MORA@upc.edu

Joseph Rusinkoمشاهده پروفایل

دانشیار

• Phylogenomics
• Algebraic Geometry
• Statistics
• +۷ مورد دیگر

Joseph Rusinko is an Associate Professor and Chair of the Department of Mathematics & Computer Science at Hobart and William Smith Colleges, where he has been a faculty member since 2015. He holds a Ph.D. in Mathematics from the University of Georgia (2007) and a B.S. in Mathematics and German from Davidson College (2001). Research Interests: His scholarly work lies at the intersection of mathematics, statistics, and computer science, with a focus on phylogenomics —developing computational tools to reconstruct evolutionary history—and algebraic geometry and statistics , where he studies geometric structures arising from polynomial systems and their applications in machine learning, robotics, and neuroscience. He also enjoys interdisciplinary explorations in areas such as fabric dynamics, brain mapping, bias in AI, and mathematics education. Publication Trends: His recent publications reflect a strong emphasis on phylogenetic modeling, combinatorial structures in evolutionary biology, algebraic statistics, and pedagogical innovations in undergraduate research. His work frequently involves collaborations with students and appears in journals such as SIAM Journal on Discrete Mathematics , Journal of Theoretical Biology , and PRIMUS . Teaching and Mentoring: Rusinko is deeply committed to transforming the mathematical classroom into a space for creative inquiry and inclusivity. He mentors numerous undergraduate researchers, many of whom have gone on to Ph.D. programs or careers in tech and education. His teaching includes courses like Calculus, Abstract Algebra, and the seminar Is Computing Biased? Professional Service: He is actively involved in undergraduate research initiatives and serves as a leader in the HWS STEM Scholars Program. He is a member of professional organizations including the Society for Industrial and Applied Mathematics and the Society of Systematic Biologists.

Shuai Huangمشاهده پروفایل

استادیار

• Machine Learning
• AI for Healthcare
• MRI
• +۳ مورد دیگر

Shuai Huang is an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University, affiliated with the Neuroimaging Center. His research focuses on machine learning and AI applications in healthcare, particularly leveraging MRI and computational imaging to address neurodegenerative disorders like Alzheimer's and autism spectrum disorder. The Computational Neuroscience and Artificial Intelligence (CNAI) Lab, led by Huang, develops advanced algorithms for neuroimaging data analysis. Research Interests: Machine learning, AI for healthcare, MRI, computational imaging, numerical optimization, and medical image reconstruction. The CNAI Lab actively seeks motivated students (PhD/MS) to join projects in these areas. Publications reflect expertise in Bayesian methods, parameter estimation, and medical imaging techniques. Current work emphasizes improving quantitative susceptibility mapping (QSM) reliability and algorithmic solutions for neuroimaging challenges. No scientific awards listed, though ongoing contributions to medical AI are notable. Advising: Open to recruiting students interested in computational neuroscience and AI applications. Grants and lab activities focus on interdisciplinary collaborations between engineering and neuroscience. No specific grant details provided in text.

• Multimedia Signal Processing
• Image and Video Coding
• Event-Based Imaging
• +۸ مورد دیگر

Catarina Brites is an Assistant Professor in the Department of Information Science and Technology at ISCTE - Instituto Universitário de Lisboa and an Integrated Researcher at the Institute of Telecommunications - IUL, where she contributes to the Multimedia Signal Processing Group (MSP-Lx). She holds a PhD in Electrical and Computer Engineering from Instituto Superior Técnico (IST), University of Lisbon. PhD, Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2011 M.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2005 B.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2003 Her research focuses on multimedia signal processing , particularly 2D/3D visual data coding , event-based imaging , plenoptic imaging (light fields and point clouds) , machine learning for image coding , and visual quality assessment . She has authored over 70 scientific publications with more than 3,890 citations (Google Scholar) and an h-index of 30. Her recent publications highlight a consistent focus on point cloud quality assessment , event-based vision coding , and deep learning applications in image and video compression . Trends include the development of novel metrics for 3D visual data and the application of neural networks to lossless and efficient coding paradigms. Best Paper Award Finalist at 2021 IEEE International Workshop on Multimedia Signal Processing Best Paper Honorable Mention Award at 2019 IEEE International Conference on Multimedia and Expo Top 8% Reviewer at 2018 IEEE International Conference on Multimedia and Expo Top 10% Paper Award at 2017 IEEE International Workshop on Multimedia Signal Processing Best Reviewer Award at 2012 IEEE Visual Communications and Image Processing Catarina Brites has co-supervised several M.Sc. theses at IST in areas including image/video coding and deep learning-based coding. She has been actively involved in multiple national and European R&D projects, serving as Co-Principal Investigator in the DARING project and Technical Coordinator in the EVASION project. She has also contributed as an Associate Editor for IEEE journals and served on technical and organizing committees for major conferences including IEEE ICIP, ICME, VCIP, and ACIVS. She is a key member of the Multimedia Signal Processing Group (MSP-Lx) at the Institute of Telecommunications, where she leads research in next-generation visual data representation and compression. She is also involved in the Technical Commission CT-120 - Image, Audio and Multimedia Information Coding, contributing to national standardization efforts.

FERNANDO ADRIAN FERNANDEZ TOJOمشاهده پروفایل

استاد

• Mathematical Analysis
• Differential Equations
• Functional Analysis
• +۳ مورد دیگر

FERNANDO ADRIAN FERNANDEZ TOJO is a Professor at the University of Santiago de Compostela (USC), affiliated with the Department of Statistics, Mathematical Analysis and Optimization within the Faculty of Mathematics. His research focuses on differential equations, mathematical analysis, and nonlinear systems, particularly involving Stieltjes derivatives and functional differential equations. He is part of the EDNL research group (Nonlinear differential equations) and the Galician Mathematical Research and Technology Center (CITMAga). He earned his Doctorate from USC in 2015 with a thesis titled Existence and multiplicity of solutions of functional differential equations , supervised by Dr. Alberto Cabada Fernandez. His publications span topics like Stieltjes differential equations, Green’s functions, and applications to digital twin modeling. His work bridges pure mathematical analysis with applied problems in engineering and technology. Education: PhD in Mathematics, USC (2015) Affiliations: Faculty of Mathematics, CITMAga Interuniversity Center Research Themes: Differential equations with involutions, Stieltjes calculus, nonlinear boundary value problems, functional analysis. His research trends emphasize generalizing classical differential equation theories to broader Stieltjes frameworks, with applications in numerical methods and real-world modeling. Despite no awards listed here, his extensive publication record highlights sustained academic contribution.

Steven Gortlerمشاهده پروفایل

استاد

• Computer Science
• Mathematics
• Graph Theory
• +۳ مورد دیگر

Steven Gortler is a professor at Harvard University who presented a seminar on Invariant Embeddings (September 12, 2023). His research focuses on systems where configurations in d-dimensional space are measured through graph-based distance metrics. His work investigates how generically globally rigid graphs enable injective measurement maps modulo Euclidean transformations, with implications for structural rigidity and geometric data analysis.

Henry Adamsمشاهده پروفایل

استاد

• Topological Data Analysis
• Projective Geometry
• Algebraic Topology
• +۳ مورد دیگر

Henry Adams is a professor at Colorado State University who presented a seminar on The topology of projective codes on February 1, 2022. His research focuses on the intersection of topological data analysis, projective geometry, and metric space theory. Specializes in Vietoris-Rips complexes for shape analysis Works on Borsuk-Ulam theorem applications Investigates Gromov-Hausdorff distances Studies optimal packings in projective spaces This work connects classical topology with modern data analysis techniques, demonstrating applications in shape matching and resource coverage problems.

Mika Murtojärviمشاهده پروفایل

عضو هیئت علمی

• Algorithms
• Programming
• Machine Learning
• +۸ مورد دیگر

Mika Murtojärvi is a Senior Researcher and University Teacher in Software Engineering at the University of Turku. His work bridges research and education in computer science, with a focus on algorithms, machine learning, and software systems. He holds a PhD in Computer Science (2016) and has over a decade of teaching experience. Research Interests: His research spans algorithm development for applications in computational geometry, biomedical informatics, e-learning, and parallel computing. Key areas include survival prediction in cancer, EEG-based neurological classification, GPU-accelerated geographic algorithms, and platform-independent educational tools. His work often integrates real-world data with machine learning and optimization techniques. The recent publications highlight a strong trend in applying algorithmic and machine learning methods to healthcare and environmental challenges. Topics such as survival prediction in prostate cancer, EEG-based Parkinson’s classification, and GPU-optimized coastal algorithms reflect interdisciplinary research at the intersection of computer science, medicine, and geosciences. The use of R packages and e-learning systems also shows a commitment to practical, software-based solutions. Scientific Awards: No awards mentioned in the text. Advising and Grants: Mika supervises BSc theses and has taught various core computer science courses. While specific grants are not listed, his research in biomedical and environmental computing suggests involvement in funded projects. He has contributed to tools like ePCR, indicating software development for research dissemination. Labs and Teams: He collaborates with researchers such as Antti Airola, Tapio Pahikkala, and Jari Björne, suggesting active participation in research teams focused on machine learning and biomedical informatics. His work on GPU algorithms also indicates ties to high-performance computing groups.

• Algorithms
• Graph Theory
• Dynamic Graph Algorithms
• +۳ مورد دیگر

Gramoz Goranci is an Assistant Professor in the Faculty of Computer Science, specializing in dynamic graph algorithms, optimization, and theoretical computer science. His research focuses on developing efficient algorithms for dynamic graphs, including problems such as shortest paths, facility location, and spectral sparsification. Research Interests: His work spans dynamic algorithms for planar graphs, approximation algorithms, graph sparsification, and optimization techniques. Key areas include incremental and decremental algorithms for network flow, facility location, and clustering problems. He explores the intersection of electrical flows, oblivious routing, and spectral methods to design efficient solutions for modern computational challenges. Publications: Goranci's recent work emphasizes dynamic graph algorithms, with contributions to Euclidean facility location, Steiner tree problems, and spectral hypergraph sparsification. His 2025 papers advance sublinear-time dynamic algorithms for bi-chromatic matching and transitive reduction, while 2024 research includes near-optimal shortest paths in planar graphs and high-dimensional facility location. Professional Contributions: He co-organized the 2024 Austrian Computer Science Day and contributed to the Vienna Graduate School on Computational Optimization (2016-2020). His research is supported by grants focusing on dynamic algorithms and graph sparsification. Labs/Groups: He leads the Theory and Applications of Algorithms research group, focusing on algorithmic foundations with applications in network analysis and optimization.

• Geometric Modeling
• Haptics
• Robot Motion Planning
• +۴ مورد دیگر

David Johnson is an Associate Professor (Lecturer) at the Kahlert School of Computing, University of Utah. His research focuses on geometric modeling, haptics, robot motion planning, and computer science education. He has collaborated extensively with institutions like the School of Computing and Department of Mechanical Engineering at the University of Utah, as well as organizations such as Siemens and NVIDIA. His work spans computational geometry, haptic interface development, and interdisciplinary applications in robotics and biomedical engineering. Notable contributions include algorithms for real-time haptic rendering, collision detection methods, and optimization techniques for robot motion planning. He has also contributed to educational initiatives, including summer computer science programs for youth. Research highlights include advancements in tactile feedback systems, virtual prototyping, and QSAR modeling for environmental toxicity. His publications span journals such as IEEE Transactions on Haptics and conferences like the IEEE International Conference on Robotics and Automation. No scientific awards are explicitly mentioned in the provided texts. Dr. Johnson’s work integrates computational methods with practical applications in fields ranging from medical robotics to sustainable urban design. His academic contributions emphasize both theoretical advancements and their real-world implementation.