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Frank BauerView profile

Professor

• Computational Manufacturing
• Digital Design
• Architectural History
• +5 more

Frank Bauer is a Professor of Digital Fabrication at the University of Applied Sciences Erfurt (FHE) and holds a PhD fellowship at Berlin University of the Arts (UdK). He explores computational design and manufacturing, focusing on ontologies of digital modeling and fabrication processes. His research extends to industry applications and artistic production, with affiliations in the Cluster of Excellence 'Matters of Activity' and co-founding the planning agency Büro Vogel Bauer. Roles: Professor (FHE), PhD Fellow (UdK), Research Associate (Matters of Activity) Education: M.A. Architecture (UdK 2017), MA in Art History/Social Sciences (2012) Teaching: UdK B.A./M.A. Programs, Design&Computation MA (UdK/TU Berlin), Bartlett UCL's Design for Manufacture Research interests span computational manufacturing, material experimentation, and interdisciplinary art-fabrication workflows. He investigates historical and contemporary methods, bridging academic and industrial practices. Key Projects: Virtual Matters Lab, LogDesignBuild Suhl, Open Design M.A. Program Awards: Elsa-Neumann Fellowship, Margot & Paul Baumgarten Prize, Max Taut Award Bauer's work integrates art, architecture, and technology, emphasizing constraints and material agency. He advises on digital fabrication projects and collaborates with artists like Olafur Eliasson, gaining insights into contemporary art production processes.

Donald W. WhiteView profile

Professor

• Structural Engineering
• Steel Structures
• Computational Mechanics
• +4 more

Donald W. White is a Professor at the School of Civil and Environmental Engineering (CEE) at Georgia Institute of Technology since 1997. Previously, he served at Purdue University from 1987 to 1996. He earned a doctorate in Structural Engineering from Cornell University (1988) and holds an undergraduate degree from North Carolina State University. Prior to academia, he worked as a structural engineer in Raleigh, NC. Research Interests: White’s research focuses on structural design and behavior of steel and composite steel-concrete structures, computational mechanics, nonlinear analysis, and stability analysis. His work has significantly influenced steel design standards, including contributions to the AISC Direct Analysis Method (DM) and updates to AASHTO LRFD bridge specifications. He has led efforts to modernize design practices for curved/skewed steel bridges and noncomposite box-section members. Professional Contributions: He serves on AISC Technical Committees 4 and 10, the SSRC Executive Committee, and AASHTO/NSBA task groups. As Principal Investigator, he authored NCHRP Report 725 on analytical methods for curved/skewed steel bridges and is currently leading an FHWA-funded project to update AASHTO LRFD provisions for noncomposite box-section members. Awards: 2005 AISC Special Achievement Award 2009 AISC T.R. Higgins Lectureship Award 2006 ASCE Shortridge Hardesty Award 2007 Richard S. Fountain Bridge Task Force Award 2006 Richardson Medal (with collaborators) Grants & Advising: White’s research has been supported by FHWA, NCHRP, and industry collaborations. He has advised on major infrastructure projects, including experimental testing of curved steel bridges at the FHWA Turner-Fairbank Research Center (1997–2005).

• Age-related macular degeneration
• Genetic epidemiology
• Ophthalmic genetics
• +4 more

Johanna Margaret Seddon, MD, ScM, is a Professor at UMass Chan Medical School, holding multiple appointments across the T.H. Chan School of Medicine in the Department of Ophthalmology and Visual Sciences, Department of Population and Quantitative Health Sciences, and at the Morningside Graduate School of Biomedical Sciences in the Departments of Masters in Clinical Investigation, MD/PhD Program, and Population Health Sciences. University of Pittsburgh: BS in Chemistry/Physics Harvard University School of Public Health: MS in Epidemiology University of Pittsburgh School of Medicine: MD Dr. Seddon is a leading researcher in age-related macular degeneration (AMD), with a research focus spanning genetic epidemiology, complement system involvement in eye disease, nutritional factors affecting eye health, and development of risk prediction models. Her work integrates genomic data with clinical and imaging biomarkers to understand AMD progression and develop personalized prevention strategies. She has pioneered research on the role of rare genetic variants in complement pathway genes and their interaction with environmental factors in determining AMD risk. Analysis of her recent publications reveals a strong emphasis on integrating genetic, imaging, and clinical data to develop predictive models for AMD progression. Her research increasingly incorporates advanced computational methods including deep learning for retinal image analysis and sophisticated statistical approaches for risk prediction with hierarchical data structures. Her work spans basic science investigations of complement pathway genetics through to clinical applications of risk prediction models and preventive strategies. Development of AMD risk prediction algorithms incorporating genetic, demographic, and lifestyle factors Investigation of rare genetic variants in complement pathway genes (CFH, CFI, C4A) Analysis of nutritional factors and their interaction with genetic susceptibility in AMD Development of screening tools for AMD risk assessment Application of AI and deep learning to retinal imaging analysis Dr. Seddon's research has significant implications for personalized medicine approaches to AMD prevention and management, with potential to transform clinical practice through genetically-informed risk stratification and targeted interventions.

• Signal Processing
• Image Processing
• Machine Learning
• +7 more

Jian Qiu Zhang is a Professor at Fudan University in the School of Information Science and Technology , affiliated with the Key Laboratory for Information Science of Electromagnetic Waves and Research Center of Smart Networks and Systems in Shanghai, China. Previously, he was at the University of Greenwich (1999-2002) and earned his PhD in 1996 from Harbin Institute of Technology in the Department of Electrical Engineering . His research interests span Signal Processing , Image Analysis , and Machine Learning , with a focus on applications in Biomedical Imaging , Hyperspectral Data Analysis , and Smart Network Systems . His work often integrates Wavelet Transforms , Tensor Decomposition , and Bayesian Filtering to solve complex problems in Medical Imaging and Wireless Sensor Networks . Recent publications highlight advancements in Deep Learning for 3D Ultrasound , PARAFAC Decomposition , and Graph Neural Networks for Hyperspectral Classification . His technical contributions include Adaptive Filtering Algorithms , Nonlinear Unmixing , and Wavelet-Based Sensor Analysis . Key collaborations include researchers from institutions such as Harbin Institute of Technology, University of Greenwich, and international teams in IEEE Transactions and IGARSS conferences.

• Arctic Technology
• Sea Ice Mechanics
• Structural Analysis of Marine Structures
• +4 more

Ekaterina Kim is an Associate Professor in the Department of Marine Engineering at NTNU's Faculty of Engineering. Her research focuses on Arctic technology, sea ice mechanics, and structural analysis of marine structures under ice loads. She leads the Risk Group and co-founded the Arctic Hive Mind research initiative, emphasizing AI-driven solutions for Arctic navigation safety. Key affiliations include the Norwegian University of Science and Technology and collaborations with institutions like ASME and SNAME. Her work spans experimental and computational studies of ice-structure interactions, risk assessment models for ice-covered waters, and autonomous systems for environmental monitoring. Notable contributions include Bayesian network risk models for icebreaker operations and deep learning frameworks for sea ice classification under adverse weather conditions. Kim has advised over 20 master’s and PhD students on topics ranging from structural analysis of icebreakers to AI applications in maritime safety. Her interdisciplinary approach integrates mechanical engineering, data science, and environmental monitoring to address challenges in Arctic shipping and infrastructure resilience. Recent research highlights include developing a probabilistic approach to correct biases in shipborne sea ice mapping and creating material models for polycrystalline ice behavior during impacts. Her work is published in journals like Ocean Engineering , International Journal of Impact Engineering , and IEEE Sensors Journal .

Carolina Vittoria BeccariView profile

Associate Professor

• Geometric modeling
• Numerical methods for curves and surfaces
• Interpolation
• +4 more

Carolina Vittoria Beccari is an Associate Professor in the Department of Mathematics at the University of Bologna, Italy, a position she has held since October 2021. Previously, she served as Senior Assistant Professor (2018-2021) and Junior Assistant Professor (2014-2018) at the same institution. Her academic career includes several postdoctoral research fellowships at the University of Bologna in Mathematics, Physics and Astronomy, and the CIRAM (Interdepartmental Research Center for Applied Mathematics). Her educational background includes: Ph.D. in Computational Mathematics from the University of Padova, Italy (2007) M.Sc. in Mathematics from the University of Bologna, Italy (2003) Dr. Beccari's research focuses on geometric modeling, numerical methods for curves and surfaces, interpolation, subdivision schemes, splines, and computer-aided design. She has made significant contributions to the theory and application of Chebyshevian splines, multi-degree B-splines, and rational geometric splines. Her work bridges theoretical mathematics with practical applications in engineering and design, particularly in developing numerical methods that can automatically generate smooth boundary surfaces from tessellations produced by topological optimization. Her research interests also extend to approximation theory and the integration of advanced mathematical techniques into CAD systems. Analysis of her recent publications reveals a clear progression from foundational work on subdivision schemes to more sophisticated applications in topological optimization and industrial design problems. Her work demonstrates increasing sophistication in handling multi-degree splines and Chebyshevian spaces, with growing emphasis on practical applications in manufacturing and engineering design processes. Dr. Beccari has secured multiple research grants including the Alma Idea research grant (2022/23) for mathematical methods to enhance structural optimization of environmentally friendly transport vehicles, the Eurostars Project NIIT4CAD (2010/12), and the University of Bologna Strategic Project IT4CAD (2007/09). She has also participated in numerous research projects funded by INdAM-GNCS on spline regression, data processing, and approximation methods. She teaches multiple courses at the University of Bologna including Introduction to Numerical Methods, Numerical Methods M, and Analisi Numerica e Modellazione Geometrica across various engineering and design programs. Her teaching spans both undergraduate and graduate levels with courses offered in both Italian and English, reflecting her international academic engagement.

Magnus LöfstrandView profile

Professor

• Mechanical Engineering
• Product Development
• Production Development
• +7 more

Magnus Löfstrand is a Professor of Mechanical Engineering at Örebro University, Sweden. He holds a PhD in Computer-Aided Design (2007) from Luleå University of Technology and a Docent (2015) in Product and Production Development from Chalmers University of Technology. His academic career spans multiple institutions, including Uppsala University and Umeå University prior to his current role. Research focuses on industrial system availability, digital twin technology, and data-driven approaches for optimizing production systems. Key areas include simulation-driven design, predictive maintenance, and integrating mechanical engineering with information technology. His work emphasizes sustainability through resource-efficient production processes and competitive industrial advantages. Teaching responsibilities include courses in the Industrial Engineering and Management program at Örebro University, covering topics like Process Development, Reliability Engineering, and Academic Writing. He supervises bachelor's and master's theses across engineering disciplines. Collaborations with institutions like the University of Nottingham and Umeå University's Logic and Applications group highlight his interdisciplinary approach. He is a member of the American Society of Mechanical Engineers (ASME) and actively participates in international conferences and research projects, including the AVANS MAXI program and digital twin initiatives in mining innovation.

• Industrial Engineering
• Mechanical Design
• Computer-Aided Design (CAD)
• +7 more

Alberto Vergnano is an Associate Professor at the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. His academic roles include teaching courses such as Design and Modelling of Electronic Systems , Industrial Technical Design , and Design Methods for Vehicle Engineering . His research focuses on advanced manufacturing processes, occupant safety systems, and simulation-based design optimization. Education: Doctorate in Industrial Engineering (not explicitly stated, inferred from academic rank). Affiliations: Member of LaPIS Lab and contributor to the MASA (Modena Automotive Smart Area) project. Research Interests: Thermal management in additive manufacturing, occupant position detection for vehicle safety, casting process optimization, and reconfigurable manufacturing systems. Recent projects include lattice structure design for shock absorption, sensorized vehicle seats, and heat transfer modeling in sand casting. Publications: Over 30 peer-reviewed articles and conference papers since 2019, emphasizing simulation-driven design and safety engineering. Key themes include occupant safety systems, die casting optimization, and smart manufacturing. Labs/Teams: Active in the LaPIS Lab, focusing on design tools and methods. Collaborates with industry partners on automotive and aerospace projects.

• Additive Manufacturing
• Automotive Engineering
• Design Optimization
• +3 more

Francesco LEALI is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. His research focuses on Additive Manufacturing, Automotive Engineering, and Design Optimization, with applications in materials science and industrial processes. He teaches courses such as "Total Quality Management Oriented Design" and "Vehicle Conceptual Design" in the Master's programs of Mechanical Engineering and Advanced Automotive Engineering. Research Interests: Professor LEALI's work emphasizes sustainable design methodologies, advanced manufacturing processes (e.g., die casting, powder bed fusion), and the integration of sensor technologies in automotive safety systems. He has pioneered studies on multifunctional lattice structures for thermal management and shock absorption, as well as tolerance allocation and process simulation for automotive components. Publications: His recent articles address topics like additive manufacturing optimization, robust die casting processes, and sensorized seat systems for occupant safety. These contributions highlight advancements in manufacturing efficiency, material performance, and human-centric automotive design. Labs/Teams: His research is conducted within the Department of Engineering's facilities, collaborating with industry partners to bridge academic innovation with real-world applications in automotive and sustainable mobility sectors.

• Structural Engineering
• Railway Infrastructure
• Structural Health Monitoring
• +7 more

Gunnstein Thomas Frøseth is an Associate Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on predicting the remaining service life of steel railway bridges, particularly addressing challenges in fatigue analysis, structural health monitoring, and model updating techniques. He leads projects combining field measurements with numerical simulations to assess bridge conditions under evolving load conditions. Key research areas include fatigue life estimation, finite element analysis precision, and the integration of machine learning for infrastructure diagnostics. He collaborates with industry partners to develop practical solutions for railway infrastructure management, emphasizing cost-effective monitoring and predictive maintenance strategies. His team applies advanced techniques like operational modal analysis, photogrammetry, and deep learning to improve bridge safety and longevity. Recent work includes corrosion detection systems, rivet inspection algorithms, and vibration analysis methodologies. His contributions bridge theoretical modeling with real-world applications in transportation infrastructure.

• Computer Vision
• Machine Learning
• Natural Language Processing
• +3 more

Dr. Suman Kumar Ghosh is a Lecturer in Data Science and Computer Science at York St John University, affiliated with the York Business School in London. His research focuses on computer vision, machine learning, and natural language processing, with a decade of experience across diverse industries. He has contributed to advancements in handwritten character recognition, scene text understanding, and segmentation-free word spotting techniques. His work spans foundational research in document analysis, including contributions to datasets like the Versailles-FP for ancient floor plan analysis. He has published extensively in top-tier conferences such as ICDAR and CVPR, addressing challenges in word spotting, bi-gram indexing, and visual attention models for text recognition. His research bridges theoretical advancements with practical applications in multilingual systems and adaptive feature extraction. No scientific awards are explicitly mentioned in the profile. His teaching responsibilities include modules on machine learning and artificial intelligence concepts.

Steinar Hillersøy DyvikView profile

Adjunct Associate Professor

• Gridshell Structures
• Parametric Design
• Timber Engineering
• +3 more

Steinar Hillersøy Dyvik is an Adjunct Associate Professor at NTNU's Department of Architecture and Technology, Faculty of Architecture and Design. He holds a PhD and works as an Architect at Norconsult. His research focuses on parametric design, gridshell structures, timber engineering, and structural aluminum applications. His work bridges academic research and practical construction, emphasizing advanced geometry and rational building practices. Notable projects include the modular timber gridshell in Trondheim and the Printshell project exploring 3D-printed structural models. He has contributed to conferences like IASS and IABSE, and co-authored publications on topics like aluminum nodes and digital workflows. Dyvik is actively involved in architectural education through initiatives like NTNU Live Studio and Parametric Camp workshops. He has collaborated with institutions like Rambøll and the Technical University of Vienna, and his teaching spans structural design, parametric modeling, and material innovation. His creative projects include temporary benches for NTNUI Football, the Heggmoen workshop project in Bodø, and the Arctic Sauna on Fleinvær Island. He has also authored a popular children's book explaining gridshell concepts, demonstrating his commitment to accessible architectural education.

Robin HillyardView profile

Associate Professor

• Algorithms
• Genetic Algorithms
• Sorting Algorithms
• +5 more

Robin Hillyard is an Associate Teaching Professor at the College of Engineering , Northeastern University , Boston, MA. With a PhD in Computer Science (1978) from the University of Cambridge and a BA/MA in Engineering Science from University College, Oxford, his career spans academia, industry, and startups. He teaches courses like Big Data System Engineering with Scala and Program Structure and Algorithms . Academic Integrity : Advocates for ethical standards in education, emphasizing original work despite modern challenges. Research Focus : Interests include sorting algorithms (e.g., Huskysort for optimizing array accesses), genetic algorithms (Darwin framework), and blockchain applications for combating fake news. Open Source Contributions : Active in Scala and Java projects such as LaScala , HuskySort , and Darwin on GitHub.

• Computer Graphics
• Procedural Generation
• Real-time Rendering
• +7 more

Pascal Guehl is a Part-Time Lecturer in Computer Graphics at the University of Strasbourg and Head of the new Master of Science and Technology in Extended Cinematography at École Polytechnique (LIX lab). He holds a PhD in Computer Graphics from the University of Strasbourg (2022) and has extensive experience in R&D roles across industry and academia. His core expertise spans procedural generation, real-time rendering, GPU computing, and virtual production technologies, with notable contributions to texture synthesis and semi-procedural modeling. Education highlights include a MRes from University Lyon 1 (2016), an Advanced Master from Arts & Métiers ParisTech (2005), and a master's in Physics from University Cergy-Pontoise (2000). His teaching spans over 15 years, covering C++ programming, real-time 3D graphics, and generative AI. Research focuses on interdisciplinary applications of computer graphics in film production, biology visualization, and defense systems. Key achievements include the semi-procedural texture synthesis framework (SIGGRAPH 2020) and leadership in ANR-funded projects like Animation Conductor (2023-2027). Awards include a Graphics Replicability Stamp and Honorable Mention for procedural texture work. Professional affiliations include ICube research lab (University of Strasbourg), where he co-supervises PhD students like Tara Butler, and collaborations with institutions like Telecom Paris, ENS Louis-Lumière, and Purdue University. Current projects emphasize extended cinematography tools and generative AI integration in visual effects pipelines.

• 3D Printing
• Additive Manufacturing
• Welding
• +6 more

Ulas Yaman is an Assistant Professor in the Department of Mechanical Engineering at Middle East Technical University (ODTÜ), Ankara, Turkey. He holds a PhD (2014), MSc (2010), and BSc (2007) in Mechanical Engineering from ODTÜ, with a minor in Mechatronics. His academic career includes a Visiting Assistant Professor role at Purdue University (2014-2015) and Research Assistant positions at ODTÜ (2007-2014). Education: PhD, Mechanical Engineering, ODTÜ (2007-2014) MSc, Mechanical Engineering, ODTÜ (2007-2010) BSc, Mechanical Engineering, ODTÜ (2003-2007) Minor in Mechatronics, ODTÜ (2004-2007) Professor Yaman's research focuses on 3D Printing, Additive Manufacturing, CAD/CAM architectures, FPGA-based embedded systems, and computational geometry for manufacturing . His work addresses challenges in dimensional accuracy, interior structure optimization, and command generation paradigms for CNC and 3D printing systems. He developed the LIPRO pipeline, leveraging Python-based scripting and List Processors to reduce G-code file sizes by up to 99.7% in FDM applications. Scientific Contributions: Fundamental advancements in 2.5D curve offset generation using mathematical morphology Innovative shrinkage compensation techniques for FDM 3D printing Hybrid manufacturing systems integrating additive and subtractive processes Query-based CAD/CAM architectures for heterogeneous printing Real-time interpolator implementations on FPGA hardware Awards & Grants: Best Paper Award, IEEE Workshop on Intelligent Solutions in Embedded Systems (2016) TÜBİTAK 1001 Project Grant (2017-2020) for Hybrid Manufacturing Systems TÜBİTAK 3001 Grant (2017-2018) for LIPRO Pipeline Development ODTÜ-BAP Grant (2016) for Dimensional Accuracy Improvement TÜBİTAK PhD Scholarship (2010-2014) As an educator, he has taught advanced courses including ME 533 - Computer-Aided Design and ME 414 - System Dynamics at ODTÜ since 2015. His laboratory leads projects on smart manufacturing systems and develops open-architecture platforms for experimental validation of novel fabrication paradigms.