جستجوی استاد راهنمای دانشگاهی

Manas Bhargavaمشاهده پروفایل

پژوهشگر

• Soft robotics
• Deformable structures
• Computational design
• +۵ مورد دیگر

Manas Bhargava is a Postdoctoral Researcher at the Institute of Science and Technology Austria, working in the Material Molli Lab under Prof. Jérémie Palacci. Previously, he completed his PhD with Prof. Bernd Bickel, focusing on the design and control of deformable structures. His research spans several cutting-edge domains in computational design and robotics. Bhargava's work integrates principles from computer graphics, mechanical engineering, and materials science to create innovative deformable structures. His projects range from building 3D lighting displays using folded PCBs to developing musculoskeletal soft robots with laser-controlled artificial muscles. He has presented his research at prestigious venues including SIGGRAPH, Eurographics, and the Think and Drink Conference at ISTA. Bhargava has published in top-tier venues including ACM Transactions on Graphics (SIGGRAPH) and Computer Graphics Forum. His work demonstrates a strong trend toward practical computational fabrication methods that bridge digital design with physical implementation, particularly in soft robotics and interactive displays. The research shows increasing sophistication in control mechanisms and material integration across his publications. As an academic mentor, Bhargava has supervised multiple students including Chris Wernke (bachelor thesis on Dynamic Simulation and Fabrication of Origami Robots) and Yuhan Zhang (master thesis on Compatible Triangulations for Fabricating Deformable Structures). His collaborative approach is evident in co-supervision arrangements and multi-institutional projects spanning ETH Zurich and ISTA.

Di Francesco Marioمشاهده پروفایل

استاد

• Internet of Things
• Wireless Networking
• Mobile and Ubiquitous Computing
• +۵ مورد دیگر

Professor Mario Di Francesco is a faculty member in the Department of Computer Science at Aalto University, School of Science. He holds the academic rank of Professor and leads research in Internet of Things (IoT), wireless networking, and edge computing. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Education: PhD in Information Engineering from the University of Pisa (2009), followed by research fellowships at Scuola Superiore Sant'Anna and adjunct faculty roles at the University of Texas at Arlington (2012-2014). Research interests include IoT systems, mobile computing, virtual reality, and distributed network architectures. He has led projects such as ASPECT (mobile video optimization), MeXICO (mobile cross-reality), and DNN-ReCoDe (edge computing for AI). Recent publications focus on VR privacy, Kubernetes security, and cloud-based network measurement frameworks. Notable awards include the Google IoT Technology Research Award (2016) and a MobileHCI Honorable Mention (2020). Active in academic service, he serves on program committees for IEEE conferences and chairs workshops in networking and pervasive computing domains. Current projects explore virtual reality systems and resource-constrained AI computation.

Christopher McCombمشاهده پروفایل

دانشیار

• Sociotechnical Systems
• Machine Learning for Engineering Design
• Human-AI Collaboration
• +۱۷ مورد دیگر

Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.

Francis Ogokeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Physics-Informed Machine Learning
• +۷ مورد دیگر

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

• 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

• Geometry Processing
• Computer Graphics
• 3D Modeling
• +۳ مورد دیگر

Pierre Alliez is a Researcher at Inria , currently serving as Head of the Project-team TITANE. His work focuses on robust geometry processing and 3D modeling, with applications in computational biology and smart territories. He has received prestigious awards including the Eurographics Young Researcher Award and two European Research Council grants. Research Interests : Geometry compression, surface approximation, mesh generation, and deep learning integration for 3D modeling. Grants : ERC Starting Grant (2011) and ERC Proof of Concept Grant (2017). Leadership Roles : Program co-chair for multiple international conferences (EUROGRAPHICS, Pacific Graphics, etc.). Contact: pierre.alliez@inria.fr

Adam Clareمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Additive Manufacturing (Materials, Processes, Design, and Applications)
• Electrical Discharge Machining
• +۷ مورد دیگر

Adam Clare is a Professor in the Department of Mechanical Engineering at the University of British Columbia (Faculty of Applied Science). He holds the Royal Academy of Engineering Research Chair and is a Fellow of the Higher Education Academy (FHEA) and the Institution of Engineering and Technology (FIET). His research focuses on sustainable manufacturing technologies, including additive manufacturing (materials, processes, design), electrical discharge machining, electrolyte jet machining, and stochastic design tools. Current research questions address digital twins for AM components, stochastic design in aerospace/biomedical applications, and sustainable energy manufacturing. Recent work explores zero-recast EDM, chemical recovery of spent powders, and microstructural manipulation in LPBF. He has authored/co-authored landmark reviews in Powder Bed Fusion and Surface Integrity in Metal Machining . Scientific Awards & Affiliations: Royal Academy of Engineering Research Chair Fellow of the Higher Education Academy (FHEA) Fellow of the Institution of Engineering and Technology (FIET) Clare investigates advanced manufacturing for high-integrity applications, with recent emphasis on stochastic design, multi-material AM, and residual stress prediction. He actively publishes on topics spanning metamaterials, DED repair technologies, and microstructural evolution.

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Computational Structural Dynamics
• +۱۲ مورد دیگر

Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.

Flavio SARTORETTOمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Science
• Medical Signal Processing
• +۴ مورد دیگر

Flavio SARTORETTO is an Associate Professor in Scientific Computing at Ca' Foscari University of Venice. He holds a Mathematics degree from the University of Padua and has held academic positions at University of Padua (1982-1992) and Sapienza University of Rome (1992-1993) before joining Ca' Foscari in 1993. His research focuses on numerical analysis, computational methods, and interdisciplinary applications including environmental modeling, cognitive processes, and assistive technologies. Key research areas include numerical solutions of PDEs, meshless methods, EEG signal analysis, and e-learning tools for impaired individuals. He has participated in major research projects such as EC Network (1992-1995), PRIN initiatives (1997-2010), and contributed to software development for geomechanical models and air quality systems. His work spans computational fluid dynamics, robotics applications, and cognitive studies. Recent publications highlight advancements in mesh refinement strategies, robotic assistive devices, and spatial cognition research. He has reviewed for prestigious journals and served in academic committees for state examinations and international conferences. He maintains active involvement in academic service, including roles in evaluation committees and contributions to professional societies like SIAM and CICAP.

Yucheng Liuمشاهده پروفایل

استاد

• Computer modeling and simulation
• CAD/CAE/FEA/CFD
• crashworthiness analysis
• +۱۱ مورد دیگر

Yucheng Liu is a Professor and the Jack Hatcher Chair in Engineering Entrepreneurship in the Department of Mechanical Engineering at Mississippi State University’s Bagley College of Engineering. He is a Fellow of both the American Society of Mechanical Engineers (ASME) and the Society of Automotive Engineers (SAE), reflecting his significant contributions to mechanical engineering research and education. His research spans computational modeling, crashworthiness, structural dynamics, vehicle systems, energy technology, and applied mathematics. Ph.D., Mechanical Engineering, University of Louisville, 2005 M.S., Mechanical Engineering, University of Louisville, 2003 B.S., Mechanical Engineering, Hefei University of Technology, 1997 Dr. Liu’s research interests center on computer modeling and simulation , crashworthiness analysis , structural mechanics and dynamics , vehicle system design , and ocean and marine energy technology . He has developed advanced computational models for thin-walled structures, energy absorption systems, and wave energy converters. His work integrates finite element analysis (FEA), CFD, and experimental validation to solve complex engineering problems in automotive, aerospace, and energy sectors. He has also contributed to applied mathematics by developing numerical methods for differential and integral equations. His recent publications reveal a strong focus on additive manufacturing , composite materials , tribology in precision gears , and electro-mechanical coupling in metals . These works often combine experimental and computational techniques, demonstrating a multidisciplinary approach. Trends include the use of internal state variable (ISV) models, phase-field simulations, and Taguchi-based optimization in mechanical systems. Dr. Liu has received numerous honors, including: Forest R. McFarland Award, SAE (2020) SAE Fellow (2019) ISET B. N. Gupta Award (2018) ASME Fellow (2017) Faculty Research Award, Mississippi State University (2018) Junior Faculty Researcher of the Year, UL Lafayette (2013) Marquis Who’s Who in America (since 2012) He has secured funding from NASA, NSF, Louisiana Space Consortium, and Mississippi Space Grant Consortium. His educational contributions include project-based learning frameworks , instructional courseware in thermodynamics and vibrations , and reforming senior design courses to be industry-tied and team-oriented. He has advised numerous student projects, including a Martian robot mining system and autonomous vehicle data acquisition systems. He leads the Wave Energy and Technology Lab, supporting experimental and computational research in renewable energy.

• Geographic Information Systems
• 3D Visualization
• Terrain Rendering
• +۵ مورد دیگر

Дејан Д. Ранчић је редовни професор на Електронском факултету у Нишу (Универзитет у Нишу), где је изабран 2015. године након дугогодишњег рада као доцент од 2005. године. Дипломирао, магистрирао и докторирао је на истом факултету у области Електротехника и рачунарство. Његово образовање укључује: Дипломирани инжењер електротехнике и рачунарства (1993) Магистар електротехнике и рачунарства (1997) Доктор електротехнике и рачунарства (2004) Ранчићев истраживачки фокус је на ГИС технологијама , 3D визуелизацији терена и обраги радарских сигнала . Кључни доприноси укључују развој алгоритама за мобилне ГИС апликације, паралелно рендеровање великих терена и радарску обраду података за сузбијање града. Његов рад комбинује теоријску рачунарску геометрију са практичним инжењерским решењима. Анализа његових радова показује еволуцију од радарских система (1997-1999) ка иновативним ГИС решењима (2000-2006), са посебним нагласком на визуелизацију терена и мобилне платформе. Најзначајнији радови објављени су у часописима као што су Computer and Geosciences и WSEAS Transactions on Computers . Тренутно учествује у 3 национална и 1 интернационални истраживачки пројекат, а као рецензент ради за WSEAS серију часописа. Иако детаљи о менторству нису наведени, његова публикацијска активност и пројекти указују на значајну улогу у развоју геопросторних технологија у Србији.

Alex Ferrer Ferreمشاهده پروفایل

دانشیار

• Materials Design
• Aeronautical Engineering
• Computational Mechanics
• +۴ مورد دیگر

Àlex Ferrer Ferre is a Serra-Hunter Associate Professor at the Department of Physics, Universitat Politècnica de Catalunya (UPC) in Barcelona, and an associate researcher at the International Center for Numerical Methods in Engineering (CIMNE). His research focuses on topology optimization, computational mechanics, and advanced material design. He holds a PhD in Computational Mechanics from UPC (2017) and has conducted postdoctoral research at the University of Oxford and École Polytechnique. Key roles include teaching courses like Flight Mechanics and leading the Swan project for topology optimization software. His work integrates academic and industrial collaborations with entities like ESA and INEGI. Research interests span multiscale design, topology optimization for additive manufacturing, and fracture mechanics. Major projects include the TopSat CubeSat chassis project (ESA-funded) and the Flexion project on flexible material design. Awards include the Marie Curie Fellowship (2019) and an Excellent Cum Laude PhD. His labs and teams collaborate globally, emphasizing open-source tools and collaborative research.

Daniel Cornelمشاهده پروفایل

پژوهشگر

• Flood Modeling
• Hydrology
• Computational Fluid Dynamics
• +۳ مورد دیگر

Daniel Cornel is a researcher affiliated with TU Wien's Department of Engineering Hydrology, part of the E222-02 research group. He holds a Dipl.-Ing. (engineering diploma) and a Dr.techn. (technical doctorate). His work focuses on integrating computational methods with environmental hydrology, emphasizing flood modeling, high-performance computing, and geospatial visualization. Cornel collaborates extensively with experts like Prof. Günter Blöschl and Prof. Jürgen Waser on projects such as the HORA 3.0 flood risk zoning initiative. His research interests span GPU-accelerated flood simulations, hydrodynamic modeling at regional scales, and decision-support systems for disaster management. Notable contributions include developing interactive visualization tools for real-time flood data and VR-based training modules for emergency response. Cornel also explores algorithm optimization for terrain modeling and subsurface network visualization. Key projects include the 'Integrated Simulation and Visualization for Flood Management' framework and the 'Master of Disaster' VR training system. His work bridges computational science with environmental engineering, addressing both technical challenges and practical applications in flood risk mitigation.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +۳ مورد دیگر

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.