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Naglaa ElagamyView profile

Associate Professor

• Advanced Composite Materials
• Micro Computed Tomography (micro-CT)
• Stress Simulation
• +6 more

Naglaa Elagamy is an Associate Teaching Professor in the Department of Mechanical and Manufacturing Engineering at Ontario Tech University. She holds a Ph.D. in Mechanical Engineering from Carleton University (2015), an M.A.Sc. in Engineering from Alexandria University (2002), and a B.Sc. in Engineering from the same institution (1994). As a licensed Professional Engineer (P.Eng.) in Ontario (2016–present) and a Certified SolidWorks Associate (CSWA), her expertise spans advanced composite materials, micro-CT imaging, stress simulation, and fatigue assessment of composite structures. Her research focuses on predicting progressive damage in materials and applying micro-CT for quantitative and qualitative damage analysis in carbon fiber reinforced polymers (CFRP). She has also explored aerodynamic performance optimization of small unmanned aerial vehicles (UAVs) through propeller design studies. Elagamy’s publications and presentations emphasize interdisciplinary approaches, combining experimental techniques like micro-CT with computational methods such as the Cohesive Zone Model and Finite Element Analysis. Her work addresses challenges in composite material durability, structural integrity, and multi-mode fatigue behavior. Beyond research, she contributes to teaching core mechanical engineering courses, including Kinematics and Dynamics of Machines, Statics, Solid Mechanics, and Computer-aided Design. Her professional recognitions include the Carleton University Development Grant (2014) and the AUD Recognition for Services (2018). Her advising and grants activities are not explicitly detailed here, but she has secured funding for her research at Carleton University. She is affiliated with the Faculty of Engineering and Applied Science and collaborates with industry and academic conferences such as SAMPE and CAMX. While specific lab affiliations are not mentioned, her research aligns with cutting-edge structural and materials analysis within the department.

Emily WhitingView profile

Associate Professor

• Computer Graphics
• Computational Fabrication
• Architectural Geometry
• +4 more

Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.

Mark AinsworthView profile

Professor

• Numerical Approximation of Partial Differential Equations
• A Posteriori Error Estimation
• High Order Finite Element Methods
• +5 more

Mark Ainsworth is a Francis Wayland Professor of Applied Mathematics at Brown University and holds a joint faculty appointment with Oak Ridge National Laboratory. He obtained his PhD from Durham University (1989) and has held prominent roles such as Director of the Centre for Numerical Algorithms and Intelligent Software (2011-2012). His research focuses on numerical analysis, particularly finite element methods for partial differential equations, a posteriori error estimation, and high-performance computing challenges like resiliency on exascale systems. Education: PhD in Mathematics, Durham University, 1989 BSc in Mathematics, Durham University, 1986 Research Interests: Numerical approximation of PDEs A posteriori error estimation and adaptive methods High order finite element methods Resiliency of numerical algorithms on emerging architectures Fractional PDEs and scientific data compression Awards: SIAM Fellow (2014) FIMA (2010) Whitehead Prize (2004) J.L. Lions Prize (2004) Fellow of Royal Society of Edinburgh (2003) Grants & Leadership: Co-PI for ARO MURI on fractional PDEs (2015-2020) Directed NAIS center (2011-2012), a £5M multi-institutional initiative Organized major international conferences on computational mathematics Labs/Teams: Collaborations include Oak Ridge National Lab and international research networks in numerical analysis and scientific computing.

Masatoshi OkutomiView profile

Professor

• Medical Imaging
• Computer Vision
• Deep Learning
• +5 more

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

Duncan J. IrschickView profile

Professor

• Evolutionary Biology
• Animal Behavior
• Bioinspiration
• +3 more

Duncan J. Irschick is a Professor of Biology at the University of Massachusetts Amherst, leading the Irschick Lab and co-founding initiatives like Digital Life and the Center for Evolutionary Materials. His research integrates evolutionary biology, biomechanics, and technology to study animal movement, adhesion systems, and 3D imaging. He earned a B.S. from UC Davis (1991) and a Ph.D. from Washington University, St. Louis (1996). Research focuses on gecko adhesion (GeckskinTM), 3D imaging (BeastcamTM), and bioinspired technologies. He explores how animal form and function inform synthetic design, with applications in conservation, robotics, and material science. His work spans diverse taxa, including sharks, salamanders, and reptiles, emphasizing functional morphology and evolutionary adaptation. Key achievements include over 137 peer-reviewed publications, patents on adhesives, and global media recognition. Awards include UMass Amherst's Outstanding Research Award (2014), Chancellor's Medal (2014), and Distinguished Faculty Lecture (2016). He has delivered 76 invited talks worldwide and collaborates with engineers, computer scientists, and conservationists. His lab develops open-access 3D models via Digital Life, aiding education and conservation. Current projects include studying elasmobranch energetics, reptile locomotion, and biomimetic materials. Grants from NIH, NSF, and private foundations fund this multidisciplinary work.

David E. BreenView profile

Professor

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +12 more

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

Sermet DEMİRView profile

Assistant Professor

• Mechanical Engineering
• Biomedical Engineering
• Additive Manufacturing
• +3 more

Sermet DEMİR is an Assistant Professor at the Faculty of Engineering , Department of Mechanical Engineering , Doğuş University. His work focuses on additive manufacturing, orthotic device design, and mechanical property optimization of composite materials. He teaches courses such as Experimental Engineering, Manufacturing Technology, and Computer-Aided Design. Education : BSc and MSc in Mechanical Engineering from Marmara University; PhD in Mechanical Engineering from Marmara University (2018). Research Interests center on biomedical devices, 3D printing, and structural analysis. His publications often employ the Taguchi method, Response Surface Methodology (RSM), and Quality Function Deployment (QFD) for design optimization. Recent works explore triply periodic minimal surface (TPMS) metamaterials, war bow mechanics, and adhesive joint performance. Scientific Awards are not explicitly mentioned in the text. His projects are sponsored by Doğuş University Scientific Research Projects Coordination Unit (grants 2021–22-D1-B02).

Alla ShefferView profile

Professor

• Geometry Processing
• Computer Graphics
• Discrete Geometry
• +7 more

Alla Sheffer is a Professor and Associate Head of Faculty Affairs in the Department of Computer Science at the University of British Columbia, Faculty of Science. She is affiliated with multiple research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Institute of Applied Mathematics, and ICICS (Institute for Computing, Information and Cognitive Systems). B.Sc., Hebrew University, Jerusalem (1991) M.Sc., Hebrew University, Jerusalem (1995) Ph.D., Hebrew University, Jerusalem (1999) Postdoctoral Research Associate, University of Illinois, Urbana-Champaign (1999-2001) Assistant Professor, Technion, Israel (2001-2003) Assistant Professor, University of British Columbia (2003-2008) Associate Professor, University of British Columbia (2008-present) Professor Sheffer's research focuses on geometry processing, addressing algorithmic challenges in digital shape modeling and manipulation. Her work primarily deals with discrete geometry representations, specifically meshes (polygonal model representations), with applications in computer graphics and computer-aided engineering. She utilizes tools from computational and differential geometry, discrete mathematics, and graph theory to generate, manipulate, and edit discrete geometric models. Her research spans virtual and augmented reality, visual computing, and 3D modeling, with significant contributions to sketch-based modeling, mesh processing, and cloth simulation. The 15 most recent publications reveal a consistent research trajectory in geometry processing, with recent work focusing on vector sketch processing, VR drawing tools, and advanced mesh manipulation techniques. Her work demonstrates a strong connection between human perception and computational methods, particularly in the interpretation of freehand sketches and the generation of perceptually-accurate geometric representations. The recurring themes across her publications include flowlines, curve networks, mesh parameterization, and the application of perceptual studies to improve algorithmic outputs. Eurographics Fellow ACM Fellow IEEE Fellow Royal Society of Canada Fellow SIGGRAPH Academy Member UBC Killam Research Prize NSERC Discovery Accelerator Supplement IBM Faculty Award Professor Sheffer has supervised numerous doctoral and master's students, with recent theses focusing on geometric mesh processing, vector sketch interpretation, VR drawing tools, and garment modeling. Her research group maintains strong connections with industry through various partnerships and has received substantial grant funding to support their innovative work in geometry processing and computer graphics. She teaches courses in computer graphics, geometric modeling, and video game programming, contributing significantly to both undergraduate and graduate education in computer science.

• Robotics
• Artificial Intelligence
• Tactile Sensing
• +6 more

Satoshi Funabashi is an Assistant Professor in the Department of Intermedia Art and Science at Waseda University's School of Fundamental Science and Engineering, Japan. He is affiliated with the Graduate Program for Embodiment Informatics under Waseda University's Program for Leading Graduate Schools and contributes to multiple graduate schools including the Graduate School of Creative Science and Engineering. Education: Doctor of Engineering (Waseda University, 2017-2021) Research Focus: Robotics, tactile sensing, deep learning, and embodiment informatics Academic Appointments: Assistant Professor (non-tenure-track) His research centers on symbiotic robotics and tactile-driven manipulation, with recent publications exploring graph convolutional networks, vision-touch fusion, and morphology-specific deep learning for robotic hands. He has secured multiple competitive research grants including JSPS KAKENHI and JST ACT-I programs. Scientific Awards: Grant-in-Aid for Scientific Research (B) (KAKENHI), JSPS (2024-2027) Grant-in-Aid for Early-Career Scientists, JSPS (2022-2024) JST ACT-I Research Fellow (2020-2022, 2018-2020) JSPS Research Fellowship DC1 (2017-2020) He collaborates with the Intelligent Dynamics and Representation Lab (Prof. Tetsuya Ogata) and the Intelligent Machine Lab (Prof. Shigeki Sugano) at Waseda University. He has interned at MIT's CSAIL (2018-2019) and conducted research at UC Davis (2015). His work has been cited over 500 times with an h-index of 14 according to Google Scholar.

• Additive Manufacturing
• Biomass Compaction
• Material Science
• +7 more

Prof. Ing. Juraj Beniak, PhD is a leading academic at the Institute of Production Engineering and Production Quality (Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava, STU). He serves as a Professor CSc., PhD and holds external collaborator roles at the Institute of Computer Engineering and Applied Informatics (FIIT) and the Institute of Manufacturing Technologies (MTF). His research bridges mechanical engineering and sustainable production. Office: U.V.I.P. SjF, Office 537 Contact: +421 2 57296 537 | +421 905 593 953 (mobile) Beniak's research focuses on additive manufacturing , biomass compaction , and smart production technologies . He investigates 3D printing parameter optimization, surface modification techniques, and composite material development from renewable sources. His work addresses both industrial applications and environmental sustainability through advanced manufacturing. Key projects include: OP R&I: Automation in freight railway vehicle production (2019–2023) APVV-18-0527: Additive manufacturing technology development (2019–2022) Recovery Plan: AI-driven waste management systems (2024–2026) As a KEGA grant guarantor and APVV co-investigator , he leads initiatives in CAx education, virtual laboratories, and biofuel production optimization. His contributions span from experimental equipment design to mathematical modeling of compaction processes.

Tom VerhoeffView profile

Assistant Professor

• Software Engineering
• Model-Driven Engineering
• Domain-Specific Languages
• +7 more

Tom Verhoeff is an Assistant Professor at the Faculty of Mathematics and Computing Science of Eindhoven University of Technology (TU/e) , working within the Software Engineering & Technology group. His research focuses on Model-Driven Engineering (MDE) , Domain-Specific Languages (DSLs) , and the intersection of mathematics, computing, and the arts . He teaches courses in data analytics, programming, algorithms, theoretical computer science , and logic . Verhoeff earned both his MSc and PhD in Technical Science (Mathematics and Computer Science) from TU/e. He is actively involved in promoting mathematics and informatics through initiatives like the annual Bridges conference , and serves as board member and treasurer of the Dutch Mathematics Olympiad , as well as chair of the Koos Verhoeff MathArt foundation . He has also held roles as guest lecturer in Lithuania and Finals Director for the ACM International Collegiate Programming Contest . Research Interests: Verhoeff’s work spans Model-Driven Engineering , domain-specific language development , and 3D geometric modeling . His scholarship often explores symmetry, recursion, and mathematical visualization , particularly through computational art and algorithmic puzzles . Recent publications highlight 3D rotation methods , knot theory , and mathematical art using lattice paths and geometric transformations . Scientific Awards: ACM ICPC European Founders Award (2004) IOI Distinguished Service Award (2007) Second Place in the 2022 Wolfram Computational Art Contest Notable Collaborations and Affiliations: He is affiliated with the Esprit Working Group on Asynchronous Circuit Design (ACiD-WG) , WIRE (TUE Mathematics Alumni) , ACM (Senior Member) , CSTA , IEEE Computer Society , and Royal Dutch Mathematical Society (KWG) .

Gunho SohnView profile

Associate Professor

• 3D Urban Space Modelling
• Photogrammetric Computer Vision
• Geometric Remote Sensing
• +7 more

Gunho Sohn is an Associate Professor and Department Chair in the Earth and Space Science and Engineering (ESSE) Department at York University's Lassonde School of Engineering. His research focuses on advanced geomatics engineering applications, including 3D urban modeling, photogrammetric computer vision, and geospatial data integration. He specializes in developing innovative solutions for navigation systems, energy optimization, and autonomous robotics through interdisciplinary approaches. Dr. Sohn's work emphasizes practical implementations of remote sensing technologies, with notable contributions to LiDAR data processing, SLAM systems, and BIM-GIS integration. His research has addressed real-world challenges such as improving air quality models using industrial plume observations and creating inclusive pedestrian navigation tools using open geospatial datasets. Recent trends in his publications highlight advancements in deep learning for geospatial tasks, including semantic segmentation of aerial LiDAR data, noise reduction in sensor networks, and UAV positioning systems. His work also explores digital twin applications for simulating urban environments and optimizing building energy consumption through BIM data analysis. While no specific awards or grants are listed, his extensive publication record reflects significant contributions to the fields of geomatics and computer vision. His research group collaborates on large-scale datasets like YUTO MMS and Yuto Semantic, advancing mobile mapping and semantic understanding of urban infrastructure.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +7 more

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +9 more

Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.

Dinesh ManochaView profile

Professor

• Computer Graphics
• Robotics
• Virtual Reality
• +4 more

Dinesh Manocha is a Distinguished University Professor of Computer Science at the University of Maryland, with joint appointments in the Department of Electrical and Computer Engineering and the University of Maryland Institute for Advanced Computer Studies (UMIACS). He is also affiliated with the Maryland Robotics Center and the Institute for Systems Research. His educational background includes a Ph.D. in Computer Science from the University of California at Berkeley (1992) and a B. Tech in Computer Science and Engineering from the Indian Institute of Technology, Delhi, India (1987). Professor Manocha's research spans multiple domains with significant emphasis on: Computer Graphics and Visualization Robotics and Motion Planning Virtual and Augmented Reality Systems Geometric Computing Algorithms AI Applications for Autonomous Systems High Performance Computing His extensive publication record shows consistent innovation in multi-agent navigation, collision avoidance algorithms, and applications in virtual environments. Recent work focuses on trajectory prediction for autonomous vehicles and physics-based simulation for immersive experiences, with algorithms integrated into industry-standard systems like ROS (Robot Operating System). Among his numerous honors, Professor Manocha is recognized as: ACM, IEEE, AAAS, and AAAI Fellow Member of the IEEE VGTC Virtual Reality Academy Recipient of the Pierre Bézier Award from the Solid Modeling Association University of Maryland Distinguished University Professor Multiple best paper awards across premier conferences He has supervised 54 PhD students throughout his career and currently advises numerous graduate researchers. His research has attracted significant funding from NSF, Google, Amazon, Facebook, and industry partners. Notably, he co-founded Impulsonic, a company developing physics-based audio simulation technologies acquired by Valve Corporation in 2016. Professor Manocha leads the GAMMA research group, which continues to advance geometric algorithms with applications across multiple disciplines.