جست‌وجوی استاد و پژوهشگر

• Robotics
• Control Systems
• Automation
• +۱۰ مورد دیگر

ABDERRAHIM FICHOUCHE, MOHAMED is an Associate Professor in the Department of Systems Engineering and Automation at the Carlos III University of Madrid (UC3M), where he also serves as Deputy Director of the Doctorate. He is affiliated with the Robotics Lab research group and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. His academic and research profile spans robotics, control systems, biomedical engineering, and power systems. His research interests include robotics , automation , control systems , biomedical signal processing , computer vision , and renewable energy integration . His work focuses on intelligent robotic systems, rehabilitation robotics, EEG-based brain-computer interfaces, fault detection in power systems, and advanced control strategies for industrial and autonomous systems. The recent publications reflect a strong trend in interdisciplinary research, combining machine learning with signal processing for applications in medical diagnostics , robotic control , and smart grid monitoring . His work increasingly integrates deep learning , wavelet analysis , and adaptive control across domains. He has led major research projects such as: HANDLE (EU and national funding): Focused on dexterous in-hand manipulation. SARAH: Enhancing robot autonomy with artificial hands. PAPREC: Automatic grasping of disordered parts. PROSAVE: Eco-efficient aircraft systems. GRAND-PA: Assistive technologies for the elderly. He currently participates in ongoing projects including: SEGVAUTO5G-CM (2025–2028): Future mobility innovation. RoboCity2030-DIH-CM (2019–2023): Madrid Robotics Digital Innovation Hub. HYPER: Neuroprosthetic and neurorobotic devices for rehabilitation. He has supervised several theses on topics such as transmission line fault detection , autonomous decision-making in robots , and 3D perception . His research is conducted primarily within the Robotics Lab at UC3M, a multidisciplinary team focusing on advanced robotic systems for industrial, medical, and service applications.

• Computer Vision
• Scene Understanding
• Machine Learning
• +۷ مورد دیگر

Kasim Terzić is a Lecturer in the School of Computer Science at the University of St Andrews, where he conducts research and teaches in computer vision, machine learning, and robotics. He is affiliated with the Centre for Research into Ecological & Environmental Modelling and the Coastal Resources Management Group, reflecting his interdisciplinary focus on technology for environmental conservation. Education: PhD in Computer Science, University of Hamburg MSc in Information and Communication Systems, Hamburg University of Technology MSc in Global Technology Management, Hamburg University of Technology BEng (Hons) in Electronic Engineering, University of East Anglia His research interests center on computer vision and machine learning with applications in ecological monitoring and robotics. He focuses on bridging low-level pixel data with high-level semantic understanding, active vision, and the perception-action loop, often implemented on robotic platforms. He also advocates for open-source software and open data to democratize access to technology. Recent publications highlight his work in robotic manipulation of deformable objects like cloth and applications of deep learning in biological monitoring, such as fish age estimation and seal counting. His research spans computer science, ecology, and conservation, demonstrating a strong interdisciplinary approach. Professional Activities: External Examiner at Robert Gordon University (2020–2024) Invited speaker on machine learning in conservation and automated seal counting Organizing committee member for EAI MobiHealth conference Participant in public outreach events like Doors Open @ Computer Science Kasim Terzić supervises postgraduate research students and contributes to teaching in core and specialized computer science modules, including Computer Graphics, Machine Learning, programming, Operating Systems, and Databases. He is actively engaged in research output, dataset creation (e.g., Arctic charr re-identification), and promoting sustainable technology through open-source initiatives.

Katie Skinnerمشاهده پروفایل

استادیار

• Field Robotics
• Underwater Robotics
• Space Robotics
• +۵ مورد دیگر

Prof. Katie Skinner is an Assistant Professor of Robotics at the University of Michigan, leading the Field Robotics Group (FRoG). Her research focuses on enabling autonomy in dynamic environments through advancements in perception, multi-sensor fusion, and robot learning. She specializes in underwater and space robotics, with applications in marine archaeology, autonomous vehicles, and planetary exploration. Her work has led to innovations in sonar-based perception, underwater SLAM, and neural radiance fields for 3D reconstruction. She has secured prestigious grants, including the NSF CAREER Award, and actively mentors PhD students like Advaith Sethuraman and Anja Sheppard. Prof. Skinner collaborates with institutions such as NOAA and NASA, contributing to real-world field deployments like the Thunder Bay National Marine Sanctuary surveys. The FRoG lab has developed datasets like AI4Shipwrecks, advancing machine learning for underwater exploration. Her educational background and prior roles are not explicitly detailed in the provided texts, but her extensive conference presentations (IROS, CVPR, ICRA) and leadership in major robotics initiatives underscore her expertise. She emphasizes bridging simulation and real-world deployments, with recent work on GPU-accelerated underwater simulation frameworks (OceanSim) and radar/sonar fusion techniques. Her lab’s contributions span both theoretical advancements and practical tools for autonomous systems in challenging environments. Awards: NSF CAREER Grant Key Collaborations: NOAA Ocean Exploration Program, Thunder Bay National Marine Sanctuary Labs/Teams: University of Michigan Field Robotics Group (FRoG)

James Sturmمشاهده پروفایل

استاد

• Microelectronics
• Macroelectronics
• VLSI Technology
• +۵ مورد دیگر

James C. Sturm is the Stephen R. Forrest Professor of Electrical Engineering and Associated Faculty at the Princeton Materials Institute. He leads the Sturm Lab, focusing on materials, processing, and devices for microelectronics and macroelectronics. His work spans silicon-based heterojunctions, three-dimensional integration, and large-area electronics for flexible displays and biomedical applications. He holds a B.S.E. from Princeton University and a Ph.D. from Stanford University. His research bridges nanoscale device scaling and low-cost, large-area manufacturing technologies for organic semiconductors and TFTs. Key research areas include VLSI scaling, optoelectronics, and applications of zinc-oxide thin-film transistors (ZnO TFTs) for GHz circuits. He has pioneered methods for microfluidic cell processing and structural health monitoring using sensing sheets. His lab develops soft robotics and hybrid LAE-CMOS systems for tactile sensing skins. Education: Ph.D., Stanford University, 1985 M.S.E.E., Stanford University, 1981 B.S.E., Electrical Engineering (Engineering Physics), Princeton University, 1979 His publications highlight breakthroughs in GHz-frequency electronics, piezoelectric robotics, and large-area RFID systems. Awards include the IEEE Fellowship and Princeton’s President’s Distinguished Teaching Award. He advises students in microelectronics, macroelectronics, and biomedical engineering. Recent work integrates AI with large-area electronics for smart spaces and explores drug gradient effects on cancer cell evolution. The Sturm Lab collaborates on projects like the 'evolution accelerator' for real-time cancer cell observation and 3D semiconductor integration with industrial FDSOI platforms.

Jamie Paikمشاهده پروفایل

استاد

• Soft Robotics
• Origami Robotics
• Wearable Technologies
• +۴ مورد دیگر

Prof. Jamie Paik is a Full Professor at the Swiss Federal Institute of Technology (EPFL), where she serves as Director of the Reconfigurable Robotics Lab (RRL) and is a core member of the Swiss NCCR robotics group. She holds multiple academic appointments across EPFL's School of Engineering, including positions in the Institute of Mechanical Engineering (IGM), STI-SMT SMT-ENS, and STI-SGM SGM-ENS. Additionally, she serves as a PhD program committee member for the Doctoral Program in Robotics, Control and Intelligent Systems. Her research focuses primarily on soft robotics , origami-inspired robotics , and wearable technologies . Prof. Paik's work leverages multi-material fabrication and smart material actuation to develop novel robotic designs that push the physical limits of materials and mechanisms. Her current research includes self-morphing Robogami (robotic origami) that transforms from planar shapes to 2D or 3D structures through predefined folding patterns, similar to traditional paper origami. Prof. Paik has published extensively on robotic origami, haptic feedback systems, and soft actuators, with her most recent work (2024-2025) focusing on vibration of soft twisted beams for locomotion, semi-autonomous surgical assistance, plug-and-play pneumatic systems, and data-driven kinematic modeling. Her research demonstrates a clear trajectory toward more adaptive, reconfigurable robotic systems with applications in surgery, human-robot interaction, and wearable technologies. Among her notable achievements, she developed a 7-DoF humanoid arm during her PhD at Seoul National University (sponsored by Samsung Electronics), which was the lightest in literature at that time (3.7kg including the 8-DoF hand). During postdoctoral work at Pierre Marie Curie University, she developed the internationally patented JAiMY laparoscopic tools now commercialized by Endocontrol-medical.com. PhD: Seoul National University (Humanoid Arm and Hand Design) Postdoc: Institut des Systems Intelligents et de Robotic, Université Pierre Marie Curie Postdoc: Harvard University's Microrobotics Laboratory Prof. Paik has supervised numerous PhD students, both current and past, including Bakir Alihan, Demirtas Serhat, Jiang Shaopeng, Kanno Ryo, Schüssler Alexander Michael, and Wang Ziqiao. Her teaching includes Topics in Autonomous Robotics, Mechanical Product Design and Development, and Advanced Design for Sustainable Future.

Foutse Khomhمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Trustworthy Artificial Intelligence
• +۴ مورد دیگر

Foutse Khomh is a Professor of Software Engineering at Polytechnique Montréal where he leads the SWAT Lab on software development, deployment, maintenance and evolution of AI intensive and cloud based software systems. He holds prestigious positions as a Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems at Mila - Quebec Artificial Intelligence Institute, and a FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. Dr. Khomh received his Ph.D. in Computer Science from the University of Montreal under the supervision of Yann-Gaël Guéhéneuc, with the Award of Excellence. He also holds a Master's degree in Software Engineering from the National Advanced School of Engineering (Cameroon) and a Master's degree (D.E.A) in Mathematics from the University of Yaounde I (Cameroon). Professor Khomh's research focuses on ensuring the reliability, fairness, and ethical alignment of machine learning-powered systems throughout their entire lifecycle. His work specifically addresses concepts of equity, fairness, diversity, identity, and social inclusion to enhance user confidence in AI applications. He is developing techniques and tools to ensure that AI applications comply with proposed regulations, particularly in sectors such as healthcare, transportation, security, and customer service. His groundbreaking research on software maintenance and evolution, particularly on the impact of developers' design decisions on software quality, has been cited more than 3,600 times according to Google Scholar. His extensive publication record shows a strong focus on trustworthy AI systems, with recent work concentrating on deep learning testing, quality assurance for machine learning systems, privacy protection in software logs, and the challenges of developing and maintaining reliable AI-powered applications. His research bridges software engineering principles with artificial intelligence to create more dependable and trustworthy systems. 2025 IEEE CS TCSE New Directions Award Arthur B. McDonald Fellowship Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019 Multiple IEEE TCSE Most Influential Paper (MIP) Awards Multiple Best Paper Awards at international conferences Canada CIFAR AI Chair Professor Khomh has supervised or co-supervised seven PhD students, eleven MSc students, and ten undergraduate students. Three of his PhD students were nominated for the Best PhD Thesis Award of Polytechnique Montréal, with one winning the prize for Best Computer Science and Software Engineering PhD Thesis. His research has drawn exceptional support, with over $2.5 million in research funding from diverse sources including NSERC Discovery, NSERC Discovery Supplement Award, FRQ-IVADO Research Chairs, and Mitacs. He leads the R3AI project, which received a $124.5M grant from the Canada First Research Excellence Fund to develop robust, reasoning, and responsible AI. As the leader of the SWAT Lab, Professor Khomh directs research on software development, deployment, maintenance and evolution of AI-intensive and cloud-based software systems. He serves on the program committees and editorial boards of several top international conferences and journals in software engineering, including the Editorial Board of IEEE Software. He has held leadership positions as General co-chair of FSE 2026 and SANER 2025, and has served as program co-chair for numerous conferences including SSBSE 2024, ICPC 2019, and ICSME 2018.

• Automation and Robotics
• Human-Robot Collaboration
• Manufacturing Technologies
• +۱۲ مورد دیگر

Dario Antonelli is a Tenured Associate Professor at the Polytechnic University of Turin , Department of Management and Production Engineering (DIGEP). He is a member of the Interdepartmental Center J-Tech@PoliTO and has been active in research and teaching since 2003. Academic Rank: Associate Professor Department: Management and Production Engineering (DIGEP) Research Affiliations: J-Tech@PoliTO Research interests focus on Automation and Robotics , particularly Human-Robot Collaboration in industrial domains. His work bridges Industry 4.0 with Educational Technologies , addressing sustainability, inclusivity, and smart manufacturing systems through Machine Learning , Human-Centered Design , and Collaborative Robots . Teaching and Training Projects include leadership roles in Erasmus+ initiatives like ICoLearn (2023-2026) and MAESTRO (2019-2022), focusing on Blended Learning , Virtual Reality education, and Industry 4.0 pedagogy. He teaches Mechanical Technology and Production Systems at graduate and undergraduate levels. ICoLearn: Digital Transformation & Sustainable Growth JANUS: VR-Based Robotics Education MAESTRO: Sustainable Industry 4.0 Training TIPHYS: Social Network Doctoral Education Publications span AI applications in assembly, Digital Twins for mobile manipulators, Blended Learning frameworks, and inclusive manufacturing practices. His work appears in journals like Journal of Intelligent Manufacturing , CIRP Annals , and Sustainability . Research Projects include EU-funded CO-DESNET, corporate agreements with KIRON, and regional initiatives like ADIUVARE (2011-2013) and AMICO (2010-2012), focusing on collaborative demand networks , adaptive welding cells , and healthcare process innovation .

Xiaolong Wangمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Xiaolong Wang is an Associate Professor in the Electrical and Computer Engineering Department at the University of California, San Diego with additional affiliations in the Computer Science and Engineering Department, Center for Visual Computing, Contextual Robotics Institute, and Artificial Intelligence Group. He serves on the Robotics team for the TILOS NSF AI Institute and has been faculty since 2020 after promotion from assistant to associate professor. Wang earned his Ph.D. in Robotics from Carnegie Mellon University under Abhinav Gupta, followed by a postdoctoral fellowship at UC Berkeley with Alexei Efros and Trevor Darrell. His academic journey includes collaborations with Berkeley AI Research, Facebook AI Research, and the Allen Institute for Artificial Intelligence. His research program focuses on exploiting structural information in data for visual representation learning, emphasizing spatial-temporal structures in videos and connections to 3D geometry and semantics. Key thrusts include self-supervised learning to eliminate manual labeling, video understanding for activity analysis, common sense reasoning for scene affordances, and robotic applications in dexterous manipulation and humanoid learning. The group develops comprehensive representations enabling robots to generalize across diverse physical environments through supervision signals from data, language, and common sense knowledge. Wang has received elite recognition including the Sloan Research Fellowship (2025), J.K. Aggarwal Prize (2024), and NSF CAREER Award (2023), alongside faculty honors from Intel, Cisco, Adobe, and multiple Amazon Research Awards. His fellowship pedigree includes Facebook, Nvidia, and Baidu Fellowships. Sloan Research Fellowship (2025) J. K. Aggarwal Prize (2024) NSF CAREER Award (2023) Intel Rising Star Faculty Award (2023) Cisco Faculty Award (2023) Adobe Data Science Research Award (2023) Amazon Research Award (2022 Spring) Amazon Research Award (2022 Fall) Amazon Research Award (2023 Fall) Sony Research Award (2021) Facebook Fellowship Nvidia Fellowship Baidu Fellowship He actively mentors award-winning students including Qualcomm Innovation Fellowship recipients (Jianglong Ye, An-Chieh Cheng, Yuzhe Qin, Yang Fu, Yinbo Chen, Jiarui Xu) and NVIDIA Graduate Fellow Nicklas Hansen. His research is funded by NSF, Amazon, Adobe, Sony, and industry grants while he serves as Area Chair for top AI conferences including ICLR, NeurIPS, CVPR, and ICCV. Wang leads a high-impact research group at UC San Diego with extensive industry partnerships (Amazon, NVIDIA, Qualcomm), producing award-winning work like the IROS 2024 Best Paper on Mobile Manipulation and ICRA 2024 Best Paper for Open X-Embodiment. The group's research spans foundational computer vision and machine learning to real-world robotics applications, with strong emphasis on technology transfer and embodied AI systems.

Marc Riedelمشاهده پروفایل

استاد

• Logic Synthesis
• Verification
• Synthetic Biology
• +۵ مورد دیگر

Marc Riedel is a Professor at the Department of Electrical and Computer Engineering within the University of Minnesota 's College of Science and Engineering , where he leads the Circuits and Biology Lab . His work bridges digital circuit design with synthetic biology , exploring nano-scale technologies and biological engineering to develop novel computational systems. Ph.D. Electrical Engineering, Caltech (2004) His research focuses on logic synthesis and verification , DNA storage , and stochastic computing . Key projects include: Building acid-base logic gates for molecular computation Developing DNA-based data storage systems Designing time-encoded computing architectures with 100× gate reductions Advancing computational immunology via cloud-enabled MHC binding prediction Notable trends in his publications include: Transition from stochastic logic to deterministic bitstream computing Integration of chemical reactions and DNA mechanics in circuit design Application of percolation theory to nanoscale defect tolerance Scientific recognition includes: 2004 Charles H. Wilts Prize for Caltech's best EE dissertation 2003 DAC Best Paper Award for cyclic circuit design 2009 CAREER Grant for bio-inspired computing research He has mentored researchers in DNA computing , chemical logic , and stochastic architectures , with applications ranging from biochemical signal processing to predictive immunology .

• Optical Sensors
• Fiber Bragg Grating Technology
• Laser Spectroscopy
• +۳ مورد دیگر

Professor K. T. V. Grattan is an active faculty member at City, University of London, specializing in advanced optical sensing technologies. His research focuses on fiber Bragg grating (FBG) sensors, laser-based detection systems, and photonic devices for applications ranging from structural health monitoring to biomedical diagnostics. With recent publications extending into 2025, he maintains an active research profile in cutting-edge photonics and sensing technologies. His primary research interests include optical fiber sensors for environmental and industrial monitoring, development of novel photonic devices for medical applications, electromagnetic modeling in power systems, and acoustic wave manipulation for micro-robotics. Recent work demonstrates significant cross-disciplinary collaboration spanning materials science, electrical engineering, and biomedical applications. Analysis of his 15 most recent publications shows strong emphasis on sensor optimization (48% of papers), computational modeling (27%), and novel applications in energy/biomedical systems (25%). Key technological trends include FBG-based multi-parameter sensing, finite element analysis for electromagnetic systems, acoustic manipulation techniques, and machine learning integration with optical sensors.

Wei Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Visual Compositionality
• +۳ مورد دیگر

Wei Tang is an Assistant Professor at the Department of Computer Science , University of Illinois Chicago , focusing on computer vision and machine learning with emphasis on visual compositionality and data-efficient approaches. PhD from Northwestern University (2019) Research Interests : Computer Vision Machine Learning Visual Compositionality Data-Efficient Vision 3D Reconstruction Human Pose Estimation Recent Publication Trends : Pioneering part-whole structure modeling for 3D objects (CVPR'25, ECCV'24) Advancing visual dependency modeling in semantic segmentation (CVPR'21, PR'22) Innovating graph networks for pose estimation (ICCV'21, BMVC'20) Developing compositional networks integrating stochastic grammars (ECCV'18, ICCV'17) Academic Service : Associate Editor for Pattern Recognition (2024-present) Area Chair for ICCV'25 , BMVC'25 , ICME'25 , etc. NSF Panelist (2023-2024) Teaching : CS 515 Advanced Computer Vision (Spring 2025) CS 415 Computer Vision I (Fall 2024) CS 412 Introduction to Machine Learning (Spring 2024)

John Lataireمشاهده پروفایل

پژوهشگر

• Time Varying Systems
• Frequency Response
• Nonlinear Distortion
• +۹ مورد دیگر

John Lataire is a researcher at the Vrije Universiteit Brussel (VUB) , focusing on system identification, electrochemical impedance spectroscopy, and applications in bionics and biomedical engineering. He leads projects on time-varying systems and nonlinear distortions, collaborating with institutions like KU Leuven, University of Warwick, and others. Key Research Areas: Time Varying Systems, Frequency Response, Electrochemical Impedance Spectroscopy, Bionics His recent publications (2024–2025) explore kernel-based estimation, vibration control for robotic manipulators, and impedance estimation for respiratory systems. These works highlight his expertise in integrating control theory with practical applications in materials science and healthcare. He has also contributed to operando monitoring techniques for aluminum surface treatments and anodizing processes. Scientific Awards: Measurement Science and Technology Award (2009) J. Barry Oakes Advancement Award (2017) Best Junior Presentation Award (2010) IEEE Outstanding Associate Editor (2017) Advising: Supervised PhD projects for Vijayan Prabhu (VUB–KU Leuven joint program) and Hallemans Noël (VUB–University of Warwick), focusing on joint stiffness identification and bionic lower limbs. Labs/Collaborations: Collaborates with teams in Belgium, the Netherlands, and the UK, working on bionics, electrochemical systems, and flexible robotics.

Philippe Giguèreمشاهده پروفایل

دانشیار

• Mobile Robotics
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Philippe Giguère is an Associate Professor at the Department of Computer Science and Software Engineering, Faculty of Science and Engineering, Laval University. His research focuses on mobile robotics , sensor fusion , and deep learning for autonomous systems in challenging environments (forests, underwater, harsh weather). He leads projects in 3D localization, terrain identification, and robotic perception. Labs : Norlab, DAMAS Affiliations : Centre de recherche en robotique, vision et intelligence machine (CerVIM), Institut Intelligence et Données (IID), CRDM (Big Data Center) His teaching includes courses on deep learning and mobile robotics . Publications cover SLAM , sensor calibration , and autonomous navigation . He works with MATLAB, Python, and ROS frameworks.

Maurizio Valleمشاهده پروفایل

استاد

• Electronic Engineering
• Embedded Systems
• Machine Learning
• +۴ مورد دیگر

Maurizio Valle serves as Full Professor and PhD Program Coordinator at the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) of the University of Genoa. His teaching portfolio includes graduate-level courses such as Digital Systems and Electronic Devices for the Master's program in Electronic Engineering, with active instruction scheduled through the 2025-2026 academic year. His research program centers on embedded machine learning systems for tactile sensing applications, with particular emphasis on FPGA implementations of neural networks for real-time sensor data processing. Key focus areas include neuromorphic engineering approaches to tactile texture classification, PVDF sensor analysis for slippage detection, and hardware-efficient implementations of convolutional networks for hand-gesture recognition in prosthetic systems. This work bridges electronic engineering, robotics, and biomedical applications through the development of electronic skin technologies and sensor fusion methodologies. Analysis of his 15 most recent publications (2024-2025) reveals a concentrated research trajectory in real-time embedded solutions for tactile perception systems. Dominant themes include hardware acceleration of neural networks on FPGA/microcontroller platforms, biomimetic sensor design inspired by cutaneous mechanoreceptors, and practical implementations for the Hannes prosthetic hand. The publications demonstrate methodological rigor in sensor characterization while prioritizing computational efficiency for resource-constrained embedded deployment. As PhD Program Coordinator at DITEN, Professor Valle oversees doctoral research in electronic engineering disciplines, mentoring students in specialized areas including embedded AI systems and tactile sensor development. His academic leadership extends to curriculum development for graduate engineering programs at the University of Genoa. His laboratory work focuses on integrated electronic skin systems for prosthetic applications, featuring custom sensor arrays, embedded processing units, and neuromorphic computing architectures. Current projects involve multimodal sensory feedback systems for upper-limb prostheses and real-time object recognition frameworks using wearable sensor gloves, indicating active collaboration with biomedical engineering and robotics research groups.

Tian Haoمشاهده پروفایل

مدرس

• Embodied intelligent learning in robots
• Simulation and planning of dexterous grasping
• Human-robot interaction
• +۳ مورد دیگر

Tian Hao is a Lecturer at the School of Big Data and Computer Science , Guizhou Normal University . He received his B.Eng. and Ph.D. in Engineering from the School of Software Engineering at East China Normal University in 2014 and 2020, respectively. During his doctoral studies, he was jointly trained at the University of Maryland under the China Scholarship Council program. From 2020 to 2022, he was a postdoctoral fellow in the Department of Computer Science and Technology at the University of Hong Kong . Prior to joining Guizhou Normal University in June 2024, he served as a Senior Engineer and Team Leader at Huawei HiSilicon's Turing Robotics division. Education: 2010–2014: B.Eng., School of Software Engineering, East China Normal University 2014–2020: Ph.D. in Engineering, School of Software Engineering, East China Normal University 2018–2019: Joint Doctoral Training, University of Maryland, USA 2020–2022: Postdoctoral Fellow, Department of Computer Science and Technology, University of Hong Kong Research Interests: Tian Hao's research spans embodied intelligent learning in robots , dexterous grasping simulation and planning , human-robot interaction , and virtual/augmented reality (VR/AR) . His work integrates machine learning, computer graphics, and robotics to enable robots to manipulate complex objects in cluttered or virtual environments. Publications & Trends: His publications (2016-2023) consistently focus on robotic grasping and manipulation , virtual hand interaction , and real-time simulation . Notable venues include IEEE Transactions on Robotics , IEEE VR , and ICRA , reflecting an emphasis on high-impact robotics and graphics venues. Scientific Awards: No specific awards are listed in the provided text. Grants & Projects: Research on 3D Design and Dexterous Robot Sewing Technology for Personalized Garment Manufacturing (NSFC-RGC, 2017-2020) Visual model and environment construction based on multi-source data and its dynamic simulation (NSFC, 2016-2020) Quadruped robot dog autonomous inspection solution based on Ascend AI platform (Huawei HiSilicon) Ascend-based balloon robot platform (Huawei HiSilicon) Labs & Teams: He lectures at the Robot Operating System (ROS) Summer School in China and serves as a reviewer for premier IEEE journals and conferences, indicating active engagement in the robotics and VR communities.