جست‌وجوی استاد و پژوهشگر

Masakazu Soshiمشاهده پروفایل

استاد

• Manufacturing systems
• CNC machine tools
• Mechatronics design and analysis
• +۵ مورد دیگر

Professor Masakazu Soshi is a faculty member in the Department of Mechanical and Aerospace Engineering at the University of California Davis, affiliated with the College of Engineering. He leads the Advanced Research for Manufacturing Systems (ARMS) Laboratory, focusing on improving machining and additive manufacturing processes for advanced CNC machine tool systems. His research integrates computational analysis, experimental validation, and hardware development to enhance manufacturing productivity and cost-effectiveness. Research interests include manufacturing systems , CNC machine tools , mechatronics design , additive manufacturing , and high-performance machining . The ARMS Lab specifically addresses challenges in Directed Energy Deposition (DED), hybrid manufacturing processes, and precision machine tool component design. Key projects involve optimizing material deposition rates, residual stress management, and real-time control systems for additive processes. Recent publications emphasize DED process control , hybrid additive-subtractive systems , and manufacturing system integration . His work bridges computational modeling (e.g., conforming mesh simulations) with practical hardware innovations (e.g., dynamic powder splitters). No notable awards or grants are explicitly listed, though his lab's website (http://arms.engr.ucdavis.edu) highlights ongoing collaborations and applied research. Professor Soshi's lab maintains a strong focus on industrial applications, such as improving machine tool guideways via CBN hard milling and developing high-torque spindle systems for aerospace materials. His research also extends to robotic-assisted surgical toolpath optimization and advanced cooling methods for hybrid CNC machines.

Rui Paulo da Silva Martinsمشاهده پروفایل

استاد

• Electrical Engineering
• Computer Engineering
• Integrated Circuits
• +۳ مورد دیگر

Prof. Rui Paulo da Silva Martins is a Full Professor and Chair Professor at the University of Macau's Faculty of Science and Technology (FCT), Department of Electrical and Computer Engineering. He has held leadership roles including Vice-Rector (Global Affairs) since 2018, and previously served as Dean of FCT (1994-1997). He leads the China Key Laboratory of Analog and Mixed-Signal VLSI Circuits and directs the Institute of Microelectronics (IME). His research focuses on integrated circuits, semiconductor technology, and microelectronics, with over 1,000 publications (h-index 59). He founded UM spin-offs like Digifluidic and co-founded Chipidea/Silergy. He is an IEEE Life Fellow and Full Member of the Lisbon Academy of Sciences. Educations: BSc, MSc, PhD, and Aggregation in Electrical Engineering from Instituto Superior Técnico, Lisbon (1980-2001) Research interests span VLSI design, analog circuits, semiconductor fabrication, and energy-efficient electronics. His work has led to 71 patents across USA, China, and Taiwan. Recent projects include AI-driven circuit design and IoT applications. Awards: Medal of Merit in Education (2021), IEEE CEDA Award (2016), Order of Engineers (2024) Advised 47 doctoral/master's theses and led over 1,000 publications. Managed UMTEC and incubated tech startups. Current responsibilities include IEEE leadership roles and global academic collaborations through AULP. Labs: China Key Lab of VLSI (Founder/Director 2011-2022), IME Director since 2019

Leonidas Guibasمشاهده پروفایل

استاد

• Computational Geometry
• Computer Graphics
• Robotics
• +۶ مورد دیگر

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.

• Urban Analytics
• Spatial Data Science
• Machine Learning
• +۳ مورد دیگر

Dr. Matthew Ng is a Research Fellow and research data scientist at the City Futures Research Centre, University of New South Wales (UNSW), within the School of Arts, Design & Architecture. His work focuses on applying spatial analytics, machine learning, and AI to address urban challenges in housing, planning, transport, and property markets. As UNSW’s first Industry Scientia Fellow (since 2022), he collaborates with PEXA to improve valuation systems through scalable modeling. He also leads the Asia-Pacific arm of the Colouring Cities Research Programme, aiming to enhance urban data accessibility and reliability. Education : PhD in Spatial Data Science, University College London (UCL) MSc in International Planning, UCL BSc (Hons) in Biology, University of Manchester Research Interests : Dr. Ng’s research bridges data science and urban policy, with a focus on spatial modeling, housing systems, and applied machine learning. His work emphasizes practical solutions for governments and communities, such as advising NSW on housing strategies and addressing rental vulnerability through data-driven methods. Grants & Partnerships : His collaborations include projects with the NSW Government, non-profits, and global initiatives like the Alan Turing Institute. He currently supervises five research students and welcomes inquiries on urban data infrastructure and machine learning applications. Labs & Teams : He is part of the City Futures Research Centre and contributes to the Colouring Cities Programme, fostering international partnerships to improve urban data systems.

• Computer Graphics
• Human-Computer Interaction
• Geometric Modeling
• +۵ مورد دیگر

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 .

• Photogrammetry
• 3D Reconstruction
• Image Processing
• +۷ مورد دیگر

Krzysztof Skabek is an Assistant Professor at the Department of Computer Science, Faculty of Computer Science and Telecommunications, Krakow University of Technology. His research focuses on photogrammetry, 3D reconstruction, computational geometry, and their applications in architecture, medicine, and cultural heritage preservation. Specializes in LiDAR and photogrammetric data fusion Develops algorithms for 3D surface modeling and mesh optimization Applies geometry techniques to biomedical devices (e.g., occlusal splints) Active in virtual reality systems and forensic documentation His work emphasizes accuracy verification, sensor integration (iPad LiDAR, low-cost scanners), and cross-disciplinary applications in heritage preservation and orthodontics. Bibliometric metrics include an h-index of 4 (Scopus/WoS), total impact factor 3.9, and ministerial score of 750. No teaching or leadership roles mentioned beyond his faculty position. Email: kskabek@pk.edu.pl

• Mechanical Engineering
• Systems Engineering
• Multi-disciplinary Optimization
• +۷ مورد دیگر

John Salmon is an Associate Professor in the Mechanical Engineering department at Brigham Young University's College of Engineering. He holds a B.S. and M.S. in Electrical Engineering from the University of Calgary and Utah State University respectively, followed by an M.S. and Ph.D. in Aerospace Engineering from Georgia Institute of Technology. Prior to his academic career, he worked for four years as a Research Engineer at the Aerospace Systems Design Laboratory collaborating with industry and government partners including NASA, Lockheed Martin, and General Electric. His research spans multiple interdisciplinary domains including: systems engineering and design integration, multi-disciplinary optimization, operations research, visual and data analytics, modeling/simulation, multi-agent decision making, sports analytics, virtual reality, and uncertainty analysis. Current projects include the Good Idea or Bad Idea initiative exploring innovative engineering solutions. Analysis of his recent publications reveals strong focus on three primary clusters: (1) Human-robot collaboration and soft robotics control strategies, (2) Optimization techniques for autonomous aerial systems, and (3) Data-driven sports analytics frameworks. His work consistently combines theoretical modeling with practical applications in aerospace, robotics, and sports engineering. Dr. Salmon maintains an active advising program with 4 current MS students (Jonathan Sadler, Carsten Christensen, Landon Willey, Ryan Day) and 10 graduated advisees since 2016. He teaches core mechanical engineering courses including ME 273 (Scientific Computing), ME 335 (Dynamic Systems), ME 578 (CAD/CAM), and ME 579 (Global Product Development).

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +۸ مورد دیگر

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

Bogdanffy Lorandمشاهده پروفایل

استاد

• IoT Systems
• Control Engineering
• Simulation and Modeling
• +۳ مورد دیگر

BOGDANFFY Lorand is a Professor and Head of the Department of Automation, Computers, Electrical and Power Engineering at the Faculty of Mechanical and Electrical Engineering, UPET.RO. He holds a PhD and specializes in IoT systems, control engineering, renewable energy, and e-learning technologies. His research integrates simulation, modeling, and sensor networks into practical applications like smart urban management, solar tracking systems, and underground mining safety. His work spans interdisciplinary areas such as: IoT-enabled environmental monitoring for sustainable cities Computational methods in mining safety and risk analysis Development of e-learning platforms and educational software Advanced motor control systems and sensorless vector control His recent publications highlight trends in: Smart infrastructure and energy optimization Data-driven decision-making in insurance and risk management 360° imaging systems and mobile teleoperation No scientific awards are listed in the provided texts. His advising and grants focus on engineering education and industrial automation projects. He leads teams in developing control systems and renewable energy solutions.

• Symbolic Computation
• Machine Learning
• Computer Algebra
• +۲ مورد دیگر

Tereso del Rio Almajano is a Researcher at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University, Linz, Austria. His work focuses on optimizing Cylindrical Algebraic Decomposition (CAD) algorithms through machine learning and heuristic development to address doubly exponential computational complexity in symbolic computation. He earned his PhD from Coventry University's Centre for Computer Science and Mathematical Modelling under supervisors Dr. Matthew England, Dr. AmirHosein Sadeghimanesh, Prof. Vasile Palade, and Dr. Kamal Bentahar. His dissertation, "Heuristics and Machine Learning to Improve Symbolic Computation Algorithms: Speeding Up Quantifier Elimination", pioneered methodologies for accelerating CAD while ensuring output validity. His research spans Symbolic Computation, Machine Learning, and Computer Algebra, with specific expertise in Quantifier Elimination and variable ordering optimization. Key contributions include dataset augmentation techniques that reduced CAD construction time by 38%, the state-of-the-art gmods heuristic derived from complexity analysis, and the first application of Explainable AI in symbolic computation to extract human-interpretable heuristics from machine learning models. As part of RISC, he addresses computational challenges in algebraic geometry and quantifier elimination, demonstrating how machine learning can surpass human-designed heuristics in symbolic computation efficiency while maintaining mathematical certainty.

• Sequencing and Scheduling
• Combinatorial Optimization
• Computational Complexity
• +۶ مورد دیگر

Dr. Yumei Huo is a Professor in the Department of Computer Science at the College of Staten Island, City University of New York (CUNY). Her work spans theoretical and applied domains, including combinatorial optimization, sequencing and scheduling, high performance computing, and medical imaging. She has developed and taught multiple HPC-focused courses and actively supervises undergraduate and graduate research. Education : BS, University of Science and Technology (China) MS, University of Science and Technology (China) PhD, New Jersey Institute of Technology Dr. Huo's research focuses on Sequencing and Scheduling , Combinatorial Optimization , Computational Complexity , Operations Research , High Performance Computing (HPC) , and more recently, Medical Imaging and Big Data . Her recent publications apply deep learning and CNN models to lung nodule detection, polyp classification, and CT image analysis, demonstrating a shift toward biomedical data science. Her recent publications (2018–2021) show a strong trend in applying machine learning to medical imaging, particularly in CT scans for lung and colon analysis. Earlier works focus on theoretical scheduling problems with constraints like machine availability, processing sets, and completion time optimization, indicating a foundation in algorithmic design and complexity theory. Scientific Awards and Recognition : Extensive peer reviewer for over 40 journals and conference proceedings Reviewed more than 50 PSC-CUNY research grant proposals and Chancellor’s Research Fellowship applications Contributed to NSF/IEEE TCPP curriculum initiatives in parallel computing education Dr. Huo has supervised numerous students on research projects and co-authored publications with them. She has also led curriculum development, including launching four HPC courses. She has no known active grants mentioned, but her sustained publication and educational contributions reflect ongoing research support. She is affiliated with research teams in algorithm design and medical imaging AI, particularly collaborating with researchers like H. Zhao, J. Y-T. Leung, and J. Tan.

Elisabetta Manconiمشاهده پروفایل

دانشیار

• Structural Vibration Analysis
• Wave Propagation
• Composite Materials
• +۵ مورد دیگر

Elisabetta Manconi is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. She holds a European Doctorate in Sound and Vibration Studies from the University of Southampton and a PhD in Industrial Mechanical Engineering from the University of Parma. Specializing in structural vibrations and wave propagation, her work bridges theoretical and experimental dynamic modeling of mechanical systems. Research Interests Elastic and acoustic wave propagation characterization Dynamic performance of composite materials Waveguides and fluid-structure interaction High-frequency vibration modeling Band-gap formation in periodic structures Scientific Contributions : Her recent publications focus on robotics motion control, vibration attenuation in plates, and NURBS-based path planning, reflecting interdisciplinary applications in mechanical systems and industrial automation. Key collaborators include researchers from the University of Southampton, Aalborg University, and UNESP. Honors & Grants : FFABR research grant (ANVUR/MIUR) Marie Curie EST Fellowship (EU) The Royal Society International Joint Project-2009/R4 Academic Leadership : She supervises PhD theses, serves on doctoral committees, and chairs international conference sessions. Her teaching portfolio includes Mechanical Vibrations, Applied Mechanics, and Industrial Automation across Mechanical and Management Engineering programs.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, where he has held roles such as Chair (2010-2015) and Acting Chair (2008-2009). He also served as Vice President and inaugural Head of the Samsung Artificial Intelligence Research Center in Toronto (2018-2024). His academic journey includes positions at Rutgers University and affiliations with MIT, University of Maryland, and others. Education: B.A.Sc. in Systems Design Engineering, University of Waterloo (1983) M.S. and Ph.D. in Computer Science, University of Maryland (1988, 1991) Research Interests: Focuses on object recognition, shape perception, and the integration of human and computer vision. Key areas include generic object recognition, shape abstraction, symmetry detection, and multiscale part-based representations. His work bridges low-level image features and high-level shape models, emphasizing mid-level shape priors and perceptual grouping. Awards and Honors: NSF CAREER Award (1996) Ontario Premiere's Research Excellence Award (2002) Lifetime Research Achievement Award from CIPPRS (2012) Fellow of IEEE and IAPR Contributions: Co-edited influential volumes like Object Categorization: Computer and Human Vision Perspectives (2009) and Shape Perception in Human and Computer Vision (2013). Served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2017–2021), and on multiple editorial boards. Active in organizing workshops and conferences, including CVPR 2014 and WACV 2019. Labs and Collaborations: Involved with the Vector Institute for Artificial Intelligence, and has collaborated on projects in artificial intelligence, robotics, and vision-based applications for accessibility and navigation.

Claudia Denkingerمشاهده پروفایل

استاد

• Infectious Diseases
• Tuberculosis
• Molecular Diagnostics
• +۳ مورد دیگر

Prof. Claudia Denkinger is a renowned academic at Heidelberg University, holding the position of Medical Director and Professor in the Department of Infectious and Tropical Medicine. She leads the AG Global DX research group and has spearheaded global health initiatives, particularly in tuberculosis (TB) diagnostics and antimicrobial resistance. Her work bridges clinical research, public health policy, and innovative diagnostic technologies. Roles: Professor, Medical Director at Heidelberg University Hospital Affiliations: Medical Faculty of Heidelberg University, Foundation for Innovative New Diagnostics (FIND) Her research focuses on advancing TB diagnostics, host biomarkers, and global health equity. Notable contributions include developing non-sputum-based TB tests (e.g., tongue swab diagnostics) and evaluating molecular tools like Xpert MTB/RIF. She has pioneered collaborations with institutions worldwide to address TB drug resistance and public health challenges. Key awards include the Gertrud Meissner Award (2008) for mycobacteriology contributions and the Memento Preis (2018) from Médecins Sans Frontières. Her career spans leadership roles at Harvard Medical School, McGill University, and the University of Tübingen, reflecting a commitment to translational research and global health impact. Publications emphasize diagnostic accuracy studies, biomarker development, and policy-relevant analyses, often involving multi-country trials to ensure practical applications in resource-limited settings.

Catherine Duclosمشاهده پروفایل

دانشیار مدعو

• Consciousness and its alterations
• Anesthesia
• Sleep and circadian rhythms
• +۵ مورد دیگر

Catherine Duclos is an Adjunct Associate Professor affiliated with multiple departments at the Université de Montréal's Faculty of Medicine, including Anesthesiology & Pain Medicine, Neurosciences, and Pharmacology & Physiology. She holds a PhD in Psychiatry (2018) with exceptional distinction and completed a postdoctoral fellowship at McGill University and Western University, focusing on consciousness, anesthesia, and computational neuroscience. Research Focus : Her work centers on characterizing brain networks in altered states of consciousness (e.g., anesthesia, coma, TBI) using high-density EEG and computational methods. Key themes include improving cognitive outcomes in surgical/critical care patients through network modulation, developing neurocomputational tools for consciousness prediction, and investigating sleep disruptions post-TBI. She leads or co-leads major grants totaling $8.1M CAD, including the UNIQUE AI-neuroscience initiative and CIFAR-Azrieli programs. Affiliations : Researcher at Hôpital du Sacré-Cœur de Montréal (trauma/critical care and neurophysiology axes), affiliated with CIUSSS Nord-de-l'Île-de-Montréal and the Centre d'études avancées en médecine du sommeil (CÉAMS). Active in translational research with collaborations across Quebec’s neuroscience and AI ecosystems. Grants & Funding : Secured funding from FRQS (Junior 1), CRSNG, Fondation Canadienne pour l'Innovation, Brain Canada, and others. Her infrastructure projects include EEG setups for consciousness modulation and neuro-AI integration. Community Engagement : LGBTQ+ advocate as volunteer and board member of GRIS-Montréal, promoting inclusivity in science. Publicly shares her journey as a lesbian mother and researcher to inspire marginalized communities.