جست‌وجوی استاد و پژوهشگر

Rémi Allègreمشاهده پروفایل

دانشیار

• Geometric Modeling
• Appearance Modeling
• Texture Synthesis
• +۶ مورد دیگر

Rémi Allègre is an Associate Professor in Computer Science at the Institute of Technology Haguenau (IUT Haguenau), affiliated with the University of Strasbourg (France). He conducts research at the ICube CNRS laboratory within the Geometric and Graphical Computing (IGG) group. Department: Multimedia and Internet Professions (Internet and multimedia technologies) Research Lab: ICube - UMR 7357, CNRS Contact: remi.allegre@unistra.fr | +33 (0)3 88 05 34 39 His research focuses on Geometric Modeling , Appearance Modeling , and Texture Synthesis , with applications in 3D data processing, procedural modeling, and biomedical imaging. Key trends include: Developing differentiable point process models for inverse procedural modeling Creating GAN-based data augmentation techniques for histopathological images Advancing semi-procedural texture synthesis methods Improving dynamic surface reconstruction algorithms Investigating hybrid representations (implicit surfaces, meshes, point sets) Multiscale label-map extraction for structured texture synthesis Scientific recognition includes: AFIG 2005 Best Article Award He supervises doctoral students such as Pascal Guehl, Florian Allender, Guillaume Baldi, Steve de Rose, and Maximilien Nowak. Collaborations span the ICube laboratory and biomedical imaging projects.

Feng Liuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Feng Liu is an Assistant Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He previously served as a Postdoctoral Researcher at Michigan State University's Department of Computer Science and Engineering. His research spans computer vision, machine learning, and biometric recognition, with a focus on 3D scene understanding, generative AI, and human-AI interaction. Research Interests: 3D Computer Vision: 3D object/scene understanding, 3D generation, VR/AR, 3D vision+language understanding 3D Human Digitization: Modeling, reconstruction, rendering, biomechanics Generative AI: Explainability, generalization, controllability in generative models, DeepFake detection Biometric Recognition: Face and gait recognition, person re-identification AI + X: Applications in Education and Healthcare Publication Trends: Dr. Liu's recent publications demonstrate a strong focus on advancing biometric recognition systems, particularly in open-set scenarios and video-based person re-identification. His work integrates 3D vision, diffusion models, and large-scale benchmarking, with increasing emphasis on real-world challenges such as aerial-ground integration, long-range recognition, and controllable synthetic data generation for training robust models. Scientific Recognition: Area Chair for BMVC 2025, ACM MM 2025, FG 2025, IJCNN 2025 Area Chair for FG 2024 Advising and Engagement: Dr. Liu is actively recruiting PhD students and interns, indicating an expanding research group. He regularly presents at major conferences including CVPR, NeurIPS, and WACV, and participates in workshops such as ELFA, demonstrating active engagement with the research community. His collaborations span multiple institutions, including Michigan State University, Queensland University of Technology, and others. Labs and Teams: While specific lab names are not mentioned in the provided texts, Dr. Liu leads a research group focused on computer vision and AI, with projects involving 3D reconstruction, biometrics, and generative models. His work on AG-VPReID and HAMoBE suggests leadership in developing large-scale benchmarks and hierarchical recognition systems.

• Computer Graphics
• 3D Computer Vision
• User Interaction
• +۵ مورد دیگر

Chi-Wing FU, Philip is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK). He holds dual roles in research and education, including Associate Editor-in-Chief of IEEE Computer Graphics and Applications. His research focuses on computer graphics, 3D vision, and human-computer interaction, with over 100 publications in top venues like SIGGRAPH, CVPR, and IEEE Visualization. Education: B.Sc. (1st Hons), Computer Science & Engineering, CUHK M.Phil., Computer Science & Engineering, CUHK PhD, Indiana University, Bloomington Research Interests: Dr. Fu's work spans 3D shape generation, computational LEGO design, AR visualization, and robotic interaction. He has pioneered projects like Make-A-Shape (large-scale 3D modeling) and DreamStone (text-driven 3D creation). His team also develops tools for medical data visualization and hand-object pose estimation. Recent Trends in Articles: Recent work emphasizes AI-driven creativity (e.g., LEGO art, text-to-3D systems) and real-time AR applications. His publications often bridge theory (e.g., generative models) with practical systems (e.g., user interfaces for design). Awards: Postgraduate Research Output Award (2023) MSRA Fellowship Nomination (2022) Best Associate Editor (IEEE CG&A) Outstanding Reviewer (ICCV 2021, CCF CAD/CG 2023) Advising & Grants: Supervised over 40 PhD/Master students and postdocs. Active in securing grants for projects like computational LEGO design (with Autodesk), medical AR visualization, and 3D generative AI. Collaborates with industry partners like Adobe and Huawei. Labs & Teams: Leads the Computational Design and Visualization Lab, focusing on 3D systems, robotics, and creative AI. Key projects include the LEGO Sketch Art toolchain and the HandShadowPoser AR system.

Tamy Boubekeurمشاهده پروفایل

استاد

• 3D Computer Graphics
• Computer-Aided Design
• Real-Time Rendering
• +۳ مورد دیگر

Tamy Boubekeur is a Senior Director and Senior Principal Research Scientist at Adobe Research France, leading Adobe Research France and the Computer Graphics Group at Telecom Paris. He is a part-time Professor in Computer Science at École Polytechnique, Institut Polytechnique de Paris. Previously, he held roles including Full Professor at Telecom Paris (on leave since 2019), Chief Scientist at Allegorithmic, and Founder/Director of the Computer Graphics Group at Telecom Paris. His work bridges academia and industry, focusing on 3D computer graphics, geometry processing, and real-time rendering. Education: HDR (2012), PhD (2007), and MSc (2004) in Computer Science from French universities. Professional experience spans roles at INRIA, TU Berlin, and UBC. He has been involved in significant projects like FEMONUM (medical modeling) and 3DLife (European project). Research interests include 3D shape analysis, rendering techniques, GPU programming, and global illumination. His work emphasizes efficient processing of geometric data, with applications in medical imaging, animation, and real-time visualization. Notable contributions include SQEM for shape approximation and pioneering point-based global illumination techniques. Honors include multiple Best Paper Awards at Eurographics, SIGGRAPH, and Shape Modeling International. He has supervised numerous PhD students, many of whom now hold academic and industry roles. Boubekeur has chaired major conferences (e.g., EGSR 2019) and served on program committees for SIGGRAPH, EUROGRAPHICS, and others.

• Machine Learning
• Maritime Engineering
• Robotics
• +۴ مورد دیگر

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.

• Photogrammetry
• 3D Modeling
• Cultural Heritage Documentation
• +۳ مورد دیگر

Emmanuel Alby is a Lecturer in the Civil Engineering and Topography Department at the University of Strasbourg, affiliated with INSA Strasbourg. He is a Researcher in the ICUBE Research Unit's TRIO Team and a member of the PAGE team. His work focuses on integrating geomatics technologies such as photogrammetry, laser scanning, and 3D modeling for archaeological and cultural heritage documentation. Research Interests: Alby's research emphasizes digital documentation of archaeological sites, 3D modeling of heritage structures, and the development of low-cost solutions for artifact and site recording. His work bridges traditional archaeological methods with cutting-edge technologies like UAV imagery, point cloud processing, and semantic segmentation for heritage preservation. Key Contributions: Alby has pioneered methodologies for combining photogrammetry and laser scanning data to create comprehensive 3D models of sites such as the Bronze Age cave "Les Fraux" and the Monastery of Saint Hilarion in Gaza. He has also developed open-source tools for real-time 3D visualization and semantic analysis of archaeological data. Articles Trends: His publications highlight advancements in 3D reconstruction techniques, artifact identification algorithms, and the application of machine learning to architectural heritage. Recent work includes real-time smartphone-based depth mapping and long-term monitoring of excavation sites in Jordan and France. Labs/Teams: Active in ICUBE's TRIO and PAGE teams, Alby collaborates on projects involving heritage preservation, archaeological surveying, and interdisciplinary geomatics applications.

Xuesong Liمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• 3D Reconstruction
• Object Detection
• +۵ مورد دیگر

Xuesong Li is a Research Fellow at Australian National University (ANU) and CSIRO, focusing on machine learning and 3D scene reconstruction. His research integrates autonomous systems, dynamic/static scene modeling, and object detection through innovative approaches like Neural Radiance Fields (NeRF). Previously, he worked at Huawei (Shanghai) on autonomous driving technologies involving LiDAR point cloud analysis. He holds a Ph.D. in Robotics from UNSW Sydney (2020), with a thesis centered on object detection for intelligent robots. Education: Ph.D. in Robotics, UNSW Sydney (2020) Research Interests: Machine learning applications in 3D reconstruction (e.g., dynamic/static scenes, reflective objects), spatial transcriptomics analysis, crop biomass estimation via multi-modal datasets, and autonomous system development. His work bridges computer vision with real-world challenges in agriculture, robotics, and medical imaging. Publications Trends: Recent work emphasizes cross-domain knowledge transfer, long-tailed recognition, and real-world 3D reconstruction challenges. Notable contributions include the MMCBE dataset for crop biomass prediction and AGP-Net for spatial transcriptomics analysis. Awards: None explicitly mentioned. Advising & Collaborations: Actively recruiting visiting PhD and master’s students in machine learning and 3D reconstruction. Collaborates with institutions like CSIRO and Huawei, focusing on applications in autonomous systems and precision agriculture. Labs/Teams: Involved with ANU’s Division of Ecology and Evolution and CSIRO’s research groups, leveraging interdisciplinary approaches to tackle complex problems in robotics and environmental science.

• Neural Computation
• Artificial Intelligence
• Machine Learning
• +۱۰ مورد دیگر

Christopher L. Buckley is Professor of Neural Computation (Informatics) at the School of Engineering and Informatics, University of Sussex, where he has been a faculty member since 2014. He was promoted from Lecturer to Senior Lecturer in 2018 and to Professor in 2022. His research bridges theoretical neuroscience and artificial intelligence, with a focus on active inference, predictive coding, and embodied cognition. Master of Physics (First Class), University of Edinburgh, 2000 MSc in Evolutionary and Adaptive Systems, University of Sussex, 2001 PhD in Cognitive Robotics, University of Southampton, 2008 His research interests center on understanding how neural systems give rise to robust behavior and how such principles can inform AI. He investigates machine learning models grounded in neuroscience, particularly through the lens of the free energy principle and active inference. His work spans artificial intelligence, machine learning, theoretical neuroscience, cognitive robotics, and artificial life , with applications in planning, perception, and adaptive systems. His recent publications reveal a strong trend in scaling predictive coding, hybrid active inference models, and rate-distortion theory for action representation. He frequently collaborates with leading researchers like Karl Friston and explores topics such as tool use, synthetic awareness, and ecosystem-level intelligence. His work appears in high-impact journals and preprint servers including Neural Computation , Frontiers in Network Physiology , and arXiv. He has received multiple research grants from prestigious funders including Innovate UK, the John Templeton Foundation, BBSRC, and the European Union, supporting projects on synthetic awareness, neural abstraction, and brain-wide dynamics in vertebrates. A metapredictive model of synthetic awareness for enabling tool invention (Innovate UK) Modelling Abstractions in Deep Reinforcement Learning and Rate-Distortion Theory (VERSES INC) The Scaling-up of Purpose in Evolution (John Templeton Foundation) DIMENSIVE: Data-driven Inference of Models from Embodied Neural Systems (EU) Distributed neural processing of self-generated visual input (BBSRC) Christopher Buckley leads an active research group at Sussex, contributing to the development of next-generation AI systems grounded in biological principles. He has no listed students in the provided data, but his collaborative network is extensive. He is not part-time, not retired, and not a former staff member, indicating ongoing active engagement in research and teaching.

• Computer Vision
• Computer Graphics
• Computational Photography
• +۶ مورد دیگر

Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA

Gianmarco Cherchiمشاهده پروفایل

استادیار

• Computer Graphics
• Geometry Processing
• Surface Mesh Generation
• +۱۲ مورد دیگر

Gianmarco Cherchi is a Tenure-Track Assistant Professor and Computer Science Researcher in the Department of Mathematics and Computer Science at the University of Cagliari, Italy, where he also completed his PhD. He teaches courses in Data Visualization and Web Programming at the undergraduate level. His research lies at the intersection of Computer Graphics and Geometry Processing, with a strong focus on surface and volumetric mesh generation, optimization, digital fabrication, and polycube-based modeling. His work combines algorithmic innovation with practical applications in fabrication, visualization, and interactive systems. The recent publications highlight a consistent trend in advanced hexahedral meshing techniques (e.g., HexBox, VOLMAP), robust geometric computation (e.g., mesh booleans), and interactive tools (e.g., ProtoSketchAR, Py3DViewer). His research spans theoretical algorithm development, benchmark creation, and applied systems for VR/AR and simulation. His scientific accolades include the Young Investigator Award 2024 from the Shape Modeling International Organization, and prior Best Thesis Awards from the Eurographics Italy Association for both his M.Sc. and Ph.D. work. Cherchi actively collaborates with researchers such as Marco Livesu, Riccardo Scateni, and others, contributing to major surveys and state-of-the-art methods in hexahedral meshing. His work is supported by publications in top venues like ACM Transactions on Graphics (SIGGRAPH), Computer Graphics Forum (Eurographics), and IEEE VR. He has also developed practical software tools like Py3DViewer for geometry processing prototyping. He leads research in digital fabrication pipelines, as evidenced by publications on polycube decomposition for manufacturing and automated flat pattern generation. His lab work involves developing interactive and robust systems for 3D modeling and analysis.

Shmuel Avidanمشاهده پروفایل

استاد

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +۳ مورد دیگر

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Ignacio IZEDDINمشاهده پروفایل

دانشیار

• Single-Molecule Localization Microscopy
• Super-Resolution Imaging
• Single Particle Tracking
• +۷ مورد دیگر

Ignacio IZEDDIN is an Associate Professor ( Maître de conférences ) at the Institut Langevin, ESPCI Paris, which is part of CNRS and Université PSL. His research sits at the critical intersection of advanced optical imaging, biophysics, and molecular cell biology, with a particular focus on pushing the boundaries of what can be visualized and understood about molecular distribution and cellular dynamics. His primary research interests include Single-Molecule Localization Microscopy (SMLM), super-resolution imaging techniques, single particle tracking (SPT), biophotonics, diffusion processes in cell biology, molecular cell biology, transcription regulation, DNA repair mechanisms, and light-matter interactions at the nanoscale. Dr. IZEDDIN's work aims to develop innovative microscopy tools that overcome current limitations in spatial and temporal resolution, enabling the capture of rapid, dynamic cellular processes with unprecedented clarity. The trends in his recent publications reveal a strong focus on developing event-based sensor technology for high-speed single-molecule localization, creating novel 3D microstructured substrates for cellular imaging and calibration, studying light-matter interactions at the nanoscale through fluorescence lifetime imaging, and applying these advanced techniques to understand fundamental biological processes like DNA repair, chromatin dynamics, and cellular differentiation. His work consistently bridges physics, engineering, and biology to solve complex imaging challenges. Dr. IZEDDIN is actively involved in research funding and recruitment, with current projects including a European LIGHTinParis COFUND PhD position analyzing alpha-synuclein diffusion in neurons using super-resolution microscopy based on event sensors, and hiring for a software engineer position to develop data processing tools for event-based SMLM technology. His laboratory employs a highly interdisciplinary approach, combining physics, biology, and engineering expertise to tackle challenging problems in cellular imaging. Current projects involve collaborations with neuroscientists, physicists, and engineers to study everything from molecular diffusion in neurons to macrophage differentiation on 3D topographical substrates.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

Sang Il Parkمشاهده پروفایل

استاد

• Computer Graphics
• Character Animation
• 3D Geometry Processing
• +۴ مورد دیگر

Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.

• Robotics
• Human-Robot Interaction
• Virtual Reality
• +۷ مورد دیگر

Sheila Sutjipto is a Postdoctoral Research Fellow at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). Her research focuses on robotics, virtual reality, and human-robot interaction, with applications in mining safety, assistive technology, and industrial automation. She has published extensively in top robotics conferences and journals, demonstrating expertise in teleoperation systems, digital twin technology, and sensor fusion for robotic applications. Dr. Sutjipto's research spans multiple domains of robotics and human-computer interaction. Her work particularly emphasizes virtual reality interfaces for robot teleoperation , digital twin technology for industrial applications , and multi-modal perception systems . She has made significant contributions to mining safety through rock scaling robotics, assistive technology for visually impaired individuals, and haptic feedback systems for industrial robots. Her interdisciplinary approach integrates mechanical engineering, computer vision, and human factors to develop intuitive and safe robotic systems. Dr. Sutjipto's recent publications demonstrate a strong trajectory toward practical applications of robotics in challenging environments. Her work on digital twin-based teleoperated rock scaling robots addresses critical safety issues in mining operations. She has also pioneered multi-modal perception systems combining RGB, event cameras, and LiDAR for assistive robotics. A consistent theme across her publications is enhancing human-robot interaction through intuitive interfaces, whether via virtual reality, haptic feedback, or annotation-assisted control systems. Dr. Sutjipto has been involved in several funded research projects, including the HALO rock scaling robot project (stages 2 and 3), IntelliForce Tooling for hydraulic safety, and integration of live surround video for VR digital twins. She has collaborated with industry partners like Ausdrill and BTP Parts Pty Ltd. Her grant history shows a strong industry-academia collaboration focused on practical robotics applications. Dr. Sutjipto appears to be part of the robotics research group at UTS's School of Mechanical and Mechatronic Engineering. Her collaborations suggest involvement with teams focused on mining robotics, assistive technology, and industrial automation. She frequently collaborates with researchers like G. Paul, D.T. Le, and K. Nguyen, demonstrating strong interdisciplinary teamwork across engineering disciplines.