Erchuan Zhang is an Adjunct Research Fellow at the School of Physics, Mathematics and Computing, The University of Western Australia. His research focuses on differential geometry, computational geometry, and numerical analysis, particularly in the areas of Riemannian manifolds, geodesics, and variational curves. Dr. Zhang's work centers on geometric modeling and optimization on non-Euclidean spaces, with significant contributions to cubic curve construction via the de Casteljau algorithm on manifolds and efficient computation of multi-objective variational curves. His research bridges theoretical differential geometry with practical computational methods for solving complex geometric problems. Recent publications (2022-2025) consistently address geometric modeling on manifolds, featuring themes like geodesic convergence analysis, boundary value problem solutions for Lagrangian systems, and cubic curve generalizations. These works appear in high-impact journals including Computer Aided Geometric Design and the SIAM Journal on Numerical Analysis , reflecting strong interdisciplinary connections between pure mathematics and computational science.
Leonidas Guibas is the Paul Pigott Professor of Engineering and Professor (by courtesy) of Electrical Engineering at Stanford University's Department of Computer Science. He leads the Geometric Computation group and is affiliated with the Computer Graphics and Artificial Intelligence Laboratories. His research focuses on algorithms for sensing, modeling, and reasoning about the physical world, with expertise in computational geometry, robotics, sensor networks, and topological data analysis. Current work includes geometric modeling with point clouds, 3D reconstruction, and mobility data analysis. He holds prestigious awards including ACM Fellow (1999), Allen Newell Award (2008), and membership in the National Academy of Sciences (2022). Education: PhD, Stanford University (1976) MS & BS, California Institute of Technology (1971) Research Interests: Geometric and topological data analysis 3D reconstruction and 3D shape analysis Sensor networks and robotics Biological structure modeling Machine learning for geometric problems Recent Articles Focus: Recent work emphasizes neural radiance fields (NeRF), dynamic Gaussian splatting, and physically plausible 3D shape generation. Publications span advancements in symmetry detection, articulated object manipulation, and multi-view video generation. Awards & Honors: Fellow, ACM (1999) Allen Newell Award (2008) Fellow, IEEE (2011) Member, National Academy of Engineering (2017) Member, National Academy of Sciences (2022) Advising & Teams: Advises doctoral and master's students on topics like geometric computing and robotics. Leads interdisciplinary teams at Stanford's ICME, HAI, and Woods Institute. Current advisees include Ian Huang, Boxiao Pan, and Colton Stearns. Labs & Collaborations: Active in the Geometric Computation group and collaborates with the Stanford AI Lab. Works on projects funded by grants in robotics, computer vision, and computational biology.
Carlo H. Séquin is a Professor of Computer Science in the EECS Department at the University of California, Berkeley. He is affiliated with the Graphics (GR) and Human-Computer Interaction (HCI) research areas, and associated with the Berkeley Center for New Media (BCNM), Berkeley Institute of Design (BID), CITRIS, and the Visual Computing Lab (VCL). His research focuses on geometric modeling, artistic geometry, mathematical visualizations, and CAD tools. Education: Ph.D. in Experimental Physics (1969), University of Basel, Switzerland. Past Roles: Head of Computer Science Division (1980–1983), co-inventor of RISC architecture with David Patterson. His work spans computer graphics, VLSI design, and the intersection of art and mathematics. He has pioneered functional optimization for surface design and contributed to CCD technology at Bell Labs. Séquin's awards include the Berkeley Citation (2016), ACM Fellow (1997), and teaching excellence awards. He advises students in Ph.D. and M.S. programs, as seen in his Ph.D. alumni list . His research is supported through CITRIS and other institutional collaborations. Séquin's artistic projects, such as mathematical sculptures and architectural designs, bridge engineering and art, exemplified by his work on the Pillar of Engineering and Twisted Tori .
Ana Dorotea Tarrío Tobar is a Professor in the Department of Mathematics at Universidade da Coruña (UDC) in Spain, specializing in Applied Mathematics. She is an active member of the Differential Geometry and Its Applications research group, with a strong publication record spanning over three decades. Her research focuses on Integral Geometry, Riemann Geometry, Contact Detection, and Applications of Geometry in Engineering. Key research areas include Extended Bianchi-Cartan-Vranceanu spaces, Curvature analysis, Geodesics, Contact detection, Quadrics, and graph bases. Her work bridges theoretical geometry with practical engineering applications. Dr. Tarrío Tobar teaches Mathematics I and II for the Degree in Technical Architecture, and Mathematics 1 for the Concurrent program of the Degree in Biology and Chemistry. Her recent publications (2022-2016) show a consistent output in prestigious mathematics journals, with increasing focus on computational geometry and educational applications. Her collaborative work spans multiple institutions across Spain and internationally, with frequent co-authorship with researchers like María J. Souto-Salorio, M. Brozos-Vázquez, and María J. Pereira-Sáez. While she has not directed bachelor's or master's theses since 2013, her research projects have been funded by various Spanish institutions including the Ministry of Culture, Education and University Planning, Xunta de Galicia, and the Ministry of Economy and Competitiveness.
Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.
Michael A. Perlmutter is an Assistant Professor in the Department of Mathematics at Boise State University and an affiliate faculty member in the Computing Ph.D. program. His research bridges theoretical and applied aspects of machine learning, data science, and inverse problems, with interdisciplinary applications in biomedicine and environmental systems. Research Interests: His work primarily focuses on Geometric Deep Learning , particularly neural network models for non-Euclidean data such as graphs and manifolds. This includes theoretical development of the geometric scattering transform and designing deep learning architectures for signed and directed graphs. He also investigates Phase Retrieval in ptychographic imaging, emphasizing robust, efficient algorithms with provable guarantees. His applied interests span AI-aided drug discovery, analysis of metabolic networks, single-cell data prediction, tensor compression, audio denoising, and data science for ecological and hydrological systems. Scientific Awards: Advising and Grants: While specific students and grants are not listed, his active research program and Ph.D. affiliation suggest involvement in mentoring graduate students and pursuit of external funding, particularly in interdisciplinary data science and machine learning. His career trajectory includes multiple competitive postdoctoral appointments, indicating a strong research profile. Labs and Teams: Though not explicitly named, his work implies collaboration with interdisciplinary teams in computational mathematics, biomedical data science, and environmental modeling, possibly within or affiliated with Boise State's data science and computing initiatives.
Dmitriy Dubovitskiy is a Part-Time Lecturer and Honorary Research Fellow at De Montfort University, affiliated with the School of Engineering and Sustainable Development within the Faculty of Computing, Engineering and Media. His work bridges computer science and healthcare, focusing on innovative solutions for cancer diagnosis and biomedical imaging. PhD in Computer Science, De Montfort University (UK), in collaboration with Bauman Moscow State Technical University Specialization: Digital Image Processing, Object Recognition using Fractal Geometry and Fuzzy Logic Dr Dubovitskiy's research centers on the mathematical modeling of natural structures using advanced pattern analysis techniques. He applies fractal geometry , fuzzy logic , and machine learning to develop automated systems for skin and cervical cancer screening , cytopathology , and industrial quality control . His interdisciplinary approach spans biology, pharmacology, physics, and engineering. The trend in his publications shows a consistent focus on biomedical image analysis , particularly in oncology and diagnostics. His work emphasizes automated decision-making , real-time recognition , and portable or web-based screening platforms . Key themes include texture classification, convex hull algorithms, and optical machine vision, with increasing integration of AI and mobile technologies. His scientific awards reflect excellence in both research and innovation: Commercialising Award, DIT Hothouse (2011) Microsharp Limited Prize for Best Research (2003) INTAS Research Grant (06-1000013-9357) Multiple Best Presentation/Poster Awards (2001–2004) Overseas Students Research Award (2000–2003) Dr Dubovitskiy has advised on several externally funded and industrial research projects, including collaborations with Oxford University , Trinity College Dublin , and companies like Microsharp Limited and MoleTest (UK) Ltd . He secured the INTAS grant and contributed to technology commercialization efforts. He teaches Dynamics and Control and mentors through consultancy. He is an active member of the British Machine Vision Association (BMVA) , MIET , and the Cambridge Knowledge Transfer Network . He is associated with the Centre for Engineering Science and Advanced Systems (CESAS) at DMU, where he contributes to research in intelligent systems and advanced computing. His work often involves interdisciplinary teams focused on translating academic research into practical healthcare and industrial applications.
Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)
Jesús Tordesillas Torres is an Assistant Professor in the Department of Electronics, Automation, and Communications at the School of Engineering, Comillas Pontifical University. He joined the institution in June 2024, bringing extensive experience from postdoctoral research at MIT and ETH Zurich, and prior academic training from MIT and the Polytechnic University of Madrid. Education: PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 2022 MS in Aeronautics and Astronautics, MIT, 2019 MS in Industrial Engineering, Polytechnic University of Madrid, 2019 BS in Industrial Engineering, Polytechnic University of Madrid, 2016 His research focuses on robotics, particularly autonomous navigation, trajectory planning, and optimization under uncertainty. He integrates deep learning and control theory to develop systems capable of safe, fast, and perception-aware navigation in dynamic and unknown environments. His work spans aerial and ground robots, multiagent systems, and challenging terrains, with strong emphasis on real-world deployment and robustness. The recent publications highlight a consistent trend in trajectory optimization, perception-aware planning, and multiagent coordination. His work bridges theoretical advances in optimization and learning with practical robotic applications, especially in safety-critical and communication-constrained scenarios. Scientific Awards: Best Paper Award, IEEE ICRA 2023 1st Place, Urban Circuit, DARPA Subterranean Challenge (2020) 2nd Place, Tunnel Circuit, DARPA Subterranean Challenge (2021) Finalist, Best Paper, IEEE IROS 2019 Jesús Tordesillas Torres has been actively involved in research grants and projects, including the ADS FERRARI WP-2 Project funded by Airbus Defence and Space (2025–2025). He mentors students and collaborates with leading institutions such as MIT, ETH Zurich, and the University of Pennsylvania. He also serves as a reviewer for top-tier journals including IEEE Transactions on Robotics , International Journal of Robotics Research , and IEEE Robotics and Automation Letters , as well as major conferences like ICRA and IROS. He leads and participates in research teams focused on autonomous systems, with affiliations to the Institute for Research in Technology (IIT) at Comillas. His invited talks at ETH Robotics Summer School and University of Pennsylvania reflect his growing influence in the robotics community.
Dr. Yingcai Xiao is an Associate Professor in the Department of Computer Science at the University of Akron's College of Engineering and Polymer Science. He joined the university in 1995 after working as a Software Scientist at Intergraph. Education: Ph.D. in Computer Science, Ph.D. in Physics, M.S. in Mathematics Dr. Xiao's research focuses on Visualization , Computer Graphics , Human-Computer Interaction , and their applications in Video Game Design and Numerical Simulation . His work bridges theoretical computing with practical solutions in education, healthcare, and engineering design. His publications from 2006–2008 highlight expertise in open-source learning platforms (e.g., Sakai), virtual surgical systems using fuzzy logic, and distributed 3D CAD systems. These contributions reflect interdisciplinary interests in educational technology, medical simulation, and internet-based systems. Scientific Awards: Co-PI on NSF Grant: Interactive Learning to Stimulate the Brain's Visual Center and to Enhance Memory Retention Dr. Xiao has served as Program Chair for the International Conference on Computer Graphics, Visualization, Computer Vision, and Image Processing (2007–2021), demonstrating sustained leadership in his field. He has also co-authored a chapter in Biologically-Inspired Computing for the Arts: Scientific Data through Graphics , expanding his impact into arts-oriented computational methods.
Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .
Dr. Antoni Jaume-i-Capó is a Full Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB) , affiliated with the Computer Graphics and Vision and Artificial Intelligence Group (UGiVIA) and serving as Director of the Artificial Intelligence Applications Laboratory (LAIA@UIB) . His work bridges Artificial Intelligence , Computer Vision , and Medical Imaging with applications in Motor Rehabilitation and Explainable AI (XAI) . Research Interests : Artificial Intelligence, Explainable AI, Computer Vision, Human-based Computation, Intelligent Systems for Motor Rehabilitation, Medical Imaging Processing. Teaching : Courses include Algorithmics and Data Structures , Final Degree Projects , and Practical Placements for Informatics Engineering degrees across multiple campuses. Scientific Contributions span 15 recent publications focusing on XAI fidelity metrics , medical image analysis , and crowdsourced diagnostics . These works integrate machine learning for Sickle Cell Disease classification and kinect-based rehabilitation systems . Scientific Awards : Premio al mejor trabajo JENUI 2010 Students : Supervised over 11 PhD/Master’s students, including Gabriel Moyà-Alcover and Pedro Marrero-Fernández. Projects : Leads EU-funded initiatives like EXPLAINME and EUGAIN , alongside regional efforts such as People4Sicklemia .
Dorogavtsev Igor Viktorovich serves as a Senior Lecturer in the Department of Engineering Graphics at the Institute of Mining, Geology and Geotechnology, Siberian Federal University (SFU). His teaching responsibilities include Descriptive Geometry, Engineering and Computer Graphics, Mountain Graphics, and Engineering and Geological Graphics. Education Graduated from the Krasnoyarsk State Academy of Non-Ferrous Metals and Gold in 2002 with a qualification as mining engineer, specializing in 'Mining Machinery and Equipment'. Professional Development 2005: Completed 'New information technologies for training specialists' at FPPKP GUCMIZ. 2010: Completed 'Information technologies in the educational process' at FPCP SFU. 2013: Completed 'Integration of project activities of teachers into the educational process of the university' at FPCP FGAOU VPO 'SFU'. Research Interests Dorogavtsev's research spans Engineering Graphics, Descriptive Geometry, and Computer-Aided Design, with applications in Mining Engineering and Environmental Engineering. His work emphasizes the development of educational materials for engineering graphics and the application of graphical methods to solve engineering problems, particularly in mining and environmental contexts such as gas cleaning processes. Publications He has authored 4 scientific and educational works, primarily focused on engineering graphics education. His publications demonstrate a commitment to enhancing engineering education through innovative teaching methodologies and the integration of information technologies. Notably, his 2017 work on modeling charged particles in gas cleaning processes extends his expertise into environmental engineering applications. Advising and Grants No information is available regarding student advising or research grants.
Alexander Alexandrovich Trofimov is an Associate Professor at the Department of Engineering Graphics within the Institute of Mining, Geology and Geotechnology at Siberian Federal University. Born on September 25, 1971, in Zaozerny, Krasnoyarsk Krai, he graduated from the Krasnoyarsk Institute of Non-Ferrous Metals in 1996 with a specialization in "Mining Machinery and Equipment," qualifying as a mining electrical engineer. He received his associate professorship in 2009 and has authored 30 scientific and educational works. Education Krasnoyarsk Institute of Non-Ferrous Metals (1996), Mining Machinery and Equipment, Mining Electrical Engineer Research Interests Professor Trofimov's research focuses on Engineering Graphics , Descriptive Geometry , and specialized applications in Mining and Geological Graphics . His work integrates pedagogical methods for technical education, computer-aided design, and geometric problem-solving in mining contexts. Recent investigations emphasize educational adaptations for engineering students and technological innovations in graphic instruction. Professional Development Integration of project activities of teachers into the educational process (Siberian Federal University, 2013) Management of an educational project (Siberian Federal University, 2017)
Zihan Zhou is an Assistant Professor at the College of Information Sciences and Technology, Penn State University, specializing in computer vision, machine learning, and 3D reconstruction. His research bridges geometric modeling, image processing, and human-computer interaction, with applications in assistive technology and creative design. Email: zuz22@psu.edu His work focuses on robust face recognition, sparse representation, and vision-language approaches for converting 2D CAD drawings into 3D parametric models. Recent projects include neural rendering for wireframe-to-image translation and data-driven 3D scene modeling. The 15 most recent publications highlight his contributions to end-to-end floorplan generation, depth estimation, trajectory prediction, and structured 3D modeling. These works integrate convolutional neural networks, graph construction, and optimization algorithms. Projects like Building Energy Savings by Tuning Indoor Lighting underscore his interdisciplinary approach, combining computer vision with environmental sustainability.
