John Marian Hoffman is an Adjunct Assistant Professor in the Department of Radiological Sciences at UCLA's School of Medicine. His research focuses on advancing computed tomography (CT) imaging through innovations in reconstruction algorithms, image harmonization techniques, and AI-driven diagnostic applications. He specializes in optimizing CT acquisition parameters and developing open-source tools for medical imaging research. Dr. Hoffman's work bridges biomedical physics and clinical applications, with publications exploring: AI-based stroke evaluation in non-contrast CT Computational optimization of iterative reconstruction Image quality harmonization across CT platforms Open-source frameworks for model-based CT reconstruction His research demonstrates consistent focus on improving diagnostic accuracy while addressing technical challenges in CT acquisition variability and reconstruction efficiency.
Parag Vichare is a Lecturer in Manufacturing and Computer Aided Engineering at the University of the West of Scotland (UWS), where he also serves as Programme Leader for the BSc Computer Aided Design Programme. He holds academic affiliations within the School of Computing, Engineering and Physical Sciences. His research focuses on product data management in multi-CAD environments, CNC machining, remanufacturing strategies, and STEP-NC standards. Dr. Vichare has led or co-led over 20 research projects, securing funding from bodies like the Scottish Funding Council, Innovate UK, and the EPSRC, totaling hundreds of thousands of pounds in grants. His teaching spans undergraduate and postgraduate modules including Computer Aided Design, Advanced Machining Systems, and Product Design. He has also served as an external examiner for multiple universities and contributed to international collaborations such as the Erasmus+ funded eAccess project, promoting accessible education in smart power systems. Notable awards include the Gold Medal (2005) and the Overseas Research Students (ORS) Award (2006). Research Highlights: Remanufacturing processes for circular economy, machine tool health monitoring, and extended reality (XR) in engineering education. Collaborations: Aerospace/Automotive manufacturers, Scottish Institute of Remanufacture, and international institutions like the University of Huddersfield and Changchun Institute of Technology. His work aligns with UN Sustainable Development Goals related to sustainable manufacturing and innovation. Recent research explores XR integration in teaching, structural optimization of agricultural machinery, and PEM fuel cell performance in harsh environments.
Christopher Batten is a Professor of Electrical and Computer Engineering at Cornell University, affiliated with the Computer Science department. He leads the Batten Research Group within the Computer Systems Laboratory (CSL), focusing on computer architecture, electronic design automation, and VLSI systems. His work spans programmable accelerators, interconnection networks, and agile chip design methodologies. Batten holds a PhD from MIT, an M.Phil. from the University of Cambridge, and a B.S. from the University of Virginia. He has held visiting roles at UC Berkeley and NVIDIA. Research Interests: Computer Architecture: Accelerators, interconnection networks, and emerging technologies VLSI Design: Agile methodologies, chip prototyping, and physical design Hardware-Software Co-Design: Parallel programming frameworks and productivity tools Recent Work: Focuses on optical interconnects, 3D integration, and AI-driven hardware design. Led projects like Cornell Custom Silicon Systems (C2S2), which taped out multiple chips in SkyWater 130nm. Collaborates with industry partners like NVIDIA and Meta. Awards: Recognized with the ACM/IEEE MICRO Hall of Fame, NSF CAREER Award, and multiple teaching accolades. His group emphasizes student-led chip projects and open-source frameworks like PyMTL3. Grants & Sponsors: Supported by NSF, DARPA, AFOSR, and industrial partners including Intel, NVIDIA, and Xilinx. Current efforts include NSF CSSI projects improving gem5 and NSF Panorama for pangenomics.
Benjamin Rüth (also known as Benjamin Rodenberg) is a doctoral candidate and Research Associate (Wissenschaftlicher Mitarbeiter) at the Chair of Scientific Computing in Computer Science (SCCS) at the Technical University of Munich (TUM). He is affiliated with the TUM School of CIT and the Department of Computer Science. His research focuses on fluid-structure interaction (FSI), multiphysics coupling, blackbox coupling, multiscale simulation, and time integration schemes, with a strong emphasis on research software engineering and sustainability. He is a core contributor to the preCICE coupling library, developing tools for partitioned multiphysics simulations. Education: M.Sc. (hons) in Computational Science and Engineering (2017, TUM) and B.Sc. in Engineering Science (2014, TUM). He advises numerous students on projects related to preCICE, FEniCS, and multiphysics modeling. He teaches courses such as Numerischer Programmieren and Computational Fluid Dynamics, and has coordinated the CSE Master's program. His work includes advancing higher-order time stepping schemes, waveform iteration methods, and integration of physics-informed neural networks. Research contributions span publications in conferences like SIAM CSE and journals like International Journal for Numerical Methods in Engineering. He co-developed web tools for teaching in mechanics and mathematics and contributed to open-source software ecosystems like preCICE. His activities include organizing workshops and advising on projects that bridge simulation software and real-world applications.
Gerald Jay Sussman is the Panasonic Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT). He received his S.B. (1968) and Ph.D. (1973) in mathematics from MIT and has been conducting artificial intelligence research there since 1964. His primary research focuses on understanding problem-solving strategies used by scientists and engineers, with dual goals of automating these processes and formalizing educational methodologies. He co-directs the Sussman Lab at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans artificial intelligence, computer languages, VLSI design, computational classical mechanics, synthetic biology, and telescope engineering. Notable contributions include co-creating the Scheme programming language, developing AI-based CAD tools for VLSI, designing the Digital Orrery for orbital mechanics simulations, and pioneering computational approaches to teaching classical mechanics. His current work includes developing explainable AI systems for autonomous vehicles. Professor Sussman's publications demonstrate broad interdisciplinary impact, with recent works spanning computer science education, software design, computational physics, and synthetic biology. His research consistently bridges theoretical computer science with practical engineering applications and educational innovation. Scientific Awards & Honors: Karl Karlstrom Outstanding Educator Award (ACM, 1990) Amar G. Bose Award for Teaching (MIT, 1992) IEEE EAB Major Education Innovation Award (2023) Taylor L. Booth Education Award (IEEE, 2024) National Academy of Engineering Member Fellow: IEEE, AAAI, ACM, AAAS, American Academy of Arts and Sciences He has supervised 46 PhD students spanning five decades, with dissertations covering AI, computer architecture, computational biology, and physical system modeling. His Sussman Lab develops computational tools for science education and engineering design, including contributions to the Magellan telescopes in Chile. Current projects involve explainable AI systems and computational mechanics frameworks.
Dr. Salar Kamari is an Assistant Professor at the School of Construction and Design of the University of Southern Mississippi (USM) . He holds a Ph.D. in Construction Science from Texas A&M University (2022) and an M.Sc. in Civil Structural Engineering from Istanbul Technical University. His research focuses on resilient infrastructure systems, point cloud processing, and smart construction technologies. Education: PhD, Construction Science, Texas A&M University (2022) M.Sc., Civil Structural Engineering, Istanbul Technical University His research interests include reality-capturing technologies, scanning-to-BIM semantic analysis, and AI-driven risk assessment models for construction safety. Dr. Kamari’s work emphasizes leveraging large-scale visual data and digital twins to enhance disaster preparedness and infrastructure resilience. His publications span top journals like Journal of Computing in Civil Engineering and Automation in Construction , focusing on topics such as drone-based documentation, probabilistic risk assessment of utility poles, and semantic point cloud segmentation for material management. He is affiliated with the Chain Technology Center (TEC) at USM and teaches courses in BIM systems and construction estimating. His expertise bridges construction engineering, disaster management, and smart infrastructure technologies.
Benachir Medjdoub is a Professor of Digital Architectural Design at the School of Architecture Design and the Built Environment at Nottingham Trent University (NTU). He leads the Creative and Virtual Technologies Research Lab, focusing on securing external funding and advancing digital design education. His career includes roles at Ecole Centrale de Paris, University of Cambridge, and University of Salford, with extensive industry collaboration in architectural technology and energy conservation. Research: Specializes in generative design models, energy-efficient building systems, and constraint-based design methodologies Funding: Secured over £500k in EPSRC grants and industry partnerships Collaborations: Works with Atkins, Foster+Partners, Autodesk, and ETH Zurich His work bridges architectural practice and computational methods, developing tools like ARCHiPLAN and augmented reality systems for building design. He has advised numerous postgraduate students and served as external examiner at multiple UK universities. Key research contributions include thermal infrared thermography for energy audits, BIM optimization, and parametric design systems. His publications span journals like Automation in Construction and Energy Research & Social Science, with over 75 peer-reviewed articles.
Dr. Jim Bywater is an Assistant Professor in the Department of Learning, Technology, and Leadership Education at James Madison University's College of Education. His work focuses on developing AI-driven tools to enhance teaching practices in K-12 settings, particularly in mathematics education. He holds a Ph.D. in Instructional Technology from the University of Virginia, an M.A. in Adult Education from Canterbury Christ Church University College (UK), and degrees in Physics from the University of Oxford. Education Background: Ph.D. Instructional Technology, University of Virginia M.A. Adult Education, Canterbury Christ Church University College B.A. and MPhys Physics, University of Oxford Research highlights include the DiSCS algorithm for segmenting categorical sequences and the Teacher Responding Tool that improves teacher feedback quality. He collaborates on AI-based dialogue simulators to train teachers in questioning techniques. His projects emphasize student-centered pedagogies, equity in education, and leveraging technology for deeper conceptual understanding. Labs/Projects: Leads the Mathematical Discourse Teaching Simulator and Teacher Responding Tool initiatives. Active in open-source development, including the DiSCS algorithm repository .
Christopher W. Brown is a Professor in the Department of Computer Science at the United States Naval Academy, specializing in computational methods for semi-algebraic sets and computer science education. His research bridges theoretical computer algebra with practical applications in epidemic modeling and pedagogical tools, maintaining active contributions to symbolic computation since the late 1990s. His primary research focuses on cylindrical algebraic decomposition (CAD) and quantifier elimination, with significant extensions to projection operators, bi-equational constraints, and truth-invariant decompositions. He also develops educational software like RegeXeX for teaching regular expressions, demonstrating his dual commitment to theoretical advancement and practical pedagogy in computer science education. Analysis of his 13 publications (1998-2007) reveals sustained innovation in CAD efficiency, including reduced projection operators and complexity analysis, while expanding applications to epidemiological modeling and educational systems. His work consistently targets real-world usability, exemplified by the QEPCAD B software suite for industrial/scientific deployment of quantifier elimination. No scientific awards are documented in the provided materials. As a professor, Brown advises students and leads research projects, though specific mentoring details are absent. His development of RegeXeX demonstrates direct classroom integration of research, with studies confirming its effectiveness in reducing instructor intervention while improving student outcomes in formal language theory. Brown directs the QEPCAD project and associated tools (Tarski, webCal), creating an ecosystem for semi-algebraic set computation. These systems form the core of his research group's work, emphasizing both theoretical refinement (e.g., projection-definable CADs) and practical deployment in scientific computing contexts.
PABLO GARCIA TAHOCES is a Professor at the Department of Electronics and Computing, Higher Technical School of Engineering, University of Santiago de Compostela, Spain. His research focuses on medical imaging technologies, particularly computer-aided diagnosis (CAD), digital mammography, computed tomography (CT), and machine learning applications in radiology. He holds a Doctorate from the University of Santiago de Compostela, with a thesis on breast cancer risk assessment through radiographic analysis. Key contributions include the development of automated systems for detecting breast masses and microcalcifications, image compression techniques for mammograms, and algorithms for aortic geometry analysis. He has collaborated on the Galician Telemammography System project, integrating CAD tools into telemedicine platforms. His work spans over 30 years of research in digital radiography and PACS systems, with a focus on enhancing diagnostic accuracy and workflow efficiency. His publications emphasize advanced imaging techniques, including ellipse-based motion estimation, CT visualization tools, and deep learning applications for cardiovascular and dental imaging. His research has been applied in clinical settings, improving the detection of pulmonary nodules, interstitial lung disease, and aortic pathologies. He maintains active collaborations with institutions like AIMEN Technological Center and Siemens Medical Systems.
Pierre-Antoine ADRAGNA serves as a Research Teacher at the University of Technology of Troyes (UTT), affiliated with the Laboratory of Mechanical & Material Engineering (LASMIS). His academic work bridges theoretical research and industrial applications in advanced manufacturing technologies. His research interests focus on Additive Manufacturing optimization , particularly FDM 3D printing processes, where he develops techniques for infill reinforcement and continuous extrusion. Additional expertise includes Reverse Engineering for medical prosthetics and building reconstruction, Mechanical Tolerancing methodologies, and Finite Element Analysis for predicting manufacturing outcomes. His work demonstrates strong industry collaboration, notably with Levels3D in the Automodel3D project for 3D building reconstruction. Dr. ADRAGNA's publication record reveals consistent contributions to mechanical engineering literature, with recent work emphasizing practical solutions for 3D printing challenges and manufacturing process optimization. His research integrates computational methods with experimental validation to address real-world manufacturing constraints. Automodel3D (2016-2019): Automated 3D building reconstruction from point clouds, co-financed by EU and Champagne-Ardenne region OptiFabAdd (2018-2021): Digital tool development for FDM additive manufacturing optimization, supported by EU and CD10 His laboratory work at LASMIS focuses on advancing mechanical engineering methodologies through computational approaches and experimental validation, contributing to UTT's research profile in advanced manufacturing systems.
Pascal LAFON is a Full Professor at the University of Technology of Troyes (UTT), affiliated with the Laboratory of Mechanical & Material Engineering (LASMIS) and the Department of Physics, Mechanics, Materials and Nanotechnology . He serves as the Director of Corporate Relations at UTT since 2023 and previously led LASMIS lab from 2012-2016. His academic duties include coordinating the 'Mechanics, Material and Advanced Manufacturing' master’s program (2018-2023) and the UTT doctoral program's 'Material, Mechanics, Optics and Nanotechnology' training (2012-2023). Director of Corporate Relations, UTT (2023-present) Head, LASMIS Lab (2012-2016) Master’s Program Coordinator (2018-2023) Doctoral Training Supervisor (2012-2023) His research focuses on Mechanical Optimization (mixed/random variables, nonlinear objectives), Additive Manufacturing (design, material behavior), and Smart Material Applications . Current projects integrate metamodeling (RBF, Kriging) with multi-objective optimization for manufacturing processes and structural systems. Publications and projects demonstrate expertise in: metal forming , composite materials , springback analysis , and breakwater design . He has supervised 13 PhD students since 1996, including international collaborations with Lebanese University and Northwestern Polytechnical University .
Laurent Gerbaud is a Professor at the ENS de Grenoble (Grenoble Institute of Technology) , affiliated with the G2Elab research laboratory and the MAGE team. His expertise lies in computer-aided design (CAD) of electrical systems, with a focus on electrical drives and power electronics. Research Interests: Analytical modeling, time/frequency simulation, gradient-based optimization, and CAD tool development. Teaching Activities: Computer-aided design, energy conversion, numerical methods, and optimization techniques. Research Trends across his publications highlight the application of advanced optimization methods (e.g., SQP, automatic differentiation) to complex electromagnetic and electrical systems, emphasizing electromagnetic compatibility (EMC) in aerospace, hybrid train energy management, and thermal modeling for multiphysics systems. His work bridges analytical modeling with practical implementation in renewable energy and transportation domains. Laboratory Affiliation : G2Elab (Grenoble Electrical Engineering Laboratory), located at Grenoble Cedex 1, France.
Fernando de Cuadra García is a Full Professor at the ICAI School of Engineering and the Technological Research Institute (IIT) of Comillas Pontifical University in Madrid. He holds a PhD in Industrial Engineering from the same institution and has served as Dean of ETSI ICAI from 2001 to 2010. His research focuses on smart grids, power systems, railway systems, and optimization techniques. He has led over 40 projects for entities like the World Bank, MIT, and the U.S. Department of Energy. His work includes developing synthetic electricity networks, electrification planning models, and energy policy frameworks. He has supervised eight PhD theses and contributed to over 26 journal articles and 23 conference papers. Recognitions include the 2016 Distinction from the ICAI Engineers' Association. Education: Industrial Engineering (1985) and PhD in Industrial Engineering (1990), both from Comillas Pontificia University. Research interests span large-scale system modeling, knowledge engineering, control theory, and sustainable energy systems. His recent articles address Texas electricity grid modeling, universal energy access strategies, and renewable mini-grid management. He actively collaborates with international organizations like Sustainable Energy for All and the Rockefeller Foundation on global electrification initiatives. Grants and projects include Rwanda’s Clean Cooking Plan, Colombia’s grid expansion models, and Canada’s synthetic distribution networks. He chairs sessions on energy transitions and advises on railway traffic control systems. Current projects include integrated clean cooking planning tools and market models for mini-grids. Labs/Teams: Leads IIT’s Smart Grids research group and collaborates with global partners on energy systems innovation.
Milan Erić is a Professor and Chair of Production Engineering at the Faculty of Engineering, University of Kragujevac, Serbia. He is affiliated with the Department for Production Engineering and has held this academic rank since September 28, 2017. His research focuses on production engineering, Industry 4.0, quality management, and the application of machine learning and computer vision in manufacturing processes. He contributes to advancing smart manufacturing systems, defect detection, and real-time nonconformity management through software solutions like the JavaScript MEAN stack. Dr. Erić's work intersects automation, IoT, and digital transformation in both industrial and healthcare contexts. He explores trends such as Quality 4.0 to Quality 5.0 transitions and ecological systems for contactless vehicle cleaning. His research often emphasizes small and medium enterprises (SMEs), addressing their challenges in adopting modern technologies for quality improvement and operational efficiency. Notable areas include: Machine vision-based quality control systems Optimization of production scheduling and material processing Integration of virtual/augmented reality for workplace safety training Development of portable welding machinery and nanocomposite analysis tools His publications span over two decades, reflecting contributions to tribology, CAD/CAM systems, and process reengineering. He advocates for digital factory models and the use of big data in public enterprises for effective digitization.
