جستجوی استاد راهنمای دانشگاهی

T. Thang Vo-Doanمشاهده پروفایل

مدرس

• Biorobotics
• Cyborg Insects
• Bio-inspired Robotics
• +۱۳ مورد دیگر

Dr T. Thang Vo-Doan is a Lecturer at the University of Queensland in the School of Mechanical & Mining Engineering . He directs the UQ Biorobotics Lab and serves as an affiliate of both the Future Autonomous Systems and Technologies and the Queensland Brain Institute . PhD in Mechanical Engineering, Nanyang Technological University (2016) M.Eng. in Manufacturing Engineering, Ho Chi Minh City University of Technology (2010) B.Eng. in Mechanical Engineering, Ho Chi Minh City University of Technology (2008) His research focuses on biohybrid robotics , integrating biological systems with technology through cyborg insects , bio-inspired robotics , and fast lock-on tracking systems. He explores insect biomechanics and brain imaging in untethered insects , developing platforms for search-and-rescue missions and environmental monitoring. Recent work trends include: 2025: On-demand climbing control for cyborg beetles 2024: High-resolution insect tracking and soft textile muscles 2023: Intelligent insect–computer hybrid robots 2022: Autonomous navigation and robotic leg design 2018: Ultralightweight living robots Scientific honors: Human Frontier Science Program Cross-disciplinary Fellowship (2019-2022) He supervises projects on: Insect-machine hybrid robots Insect-inspired robotics Fast lock-on tracking Insect locomotion biomechanics Current grants include: NHMRC IDEAS Grant (2025-2028): Infrared and AI for Ross River virus surveillance Queensland Corrective Services Grant (2024-2027): Wastewater drug misuse analysis

Marc Carmichaelمشاهده پروفایل

دانشیار

• Physical human-robot interaction
• Collaborative robotics
• Assistive robots
• +۷ مورد دیگر

Marc Carmichael is an Associate Professor at the University of Technology Sydney (UTS), School of Mechanical and Mechatronic Engineering. He serves as Director of Teaching and Student Engagement at the UTS Robotics Institute (UTS:RI) and leads the UTS:RI human-robot interaction (HRI) lab. With expertise in physical human-robot collaboration, he has developed several novel robotic systems addressing real-world problems across various industries. Dr. Carmichael received his B.Eng. in Mechanical and Mechatronic Engineering (2008) and PhD in Robotics (2013) from UTS. He joined the UTS Centre for Autonomous Systems as a Lecturer in 2016, was promoted to Senior Lecturer in 2019, and holds the title of Associate Professor. His educational background and career progression reflect his deep commitment to robotics research and education. His research focuses on robotic systems that provide physical assistance to users during laborious activities, enabling effective human-robot collaboration. His work spans collaborative robotics (cobots), assistive robots, robotic rehabilitation, robot kinematics and control, and robot system design. He specializes in developing systems where humans and robots work together physically to accomplish tasks, particularly in industrial settings and for assistive applications. An analysis of his recent publications reveals a strong emphasis on learning from demonstration techniques, human-robot trust modeling, cognitive conflict detection during collaboration, and practical implementation of cobots in industrial settings. His work increasingly integrates machine learning with physical human-robot interaction, focusing on making robotic systems more adaptable, intuitive, and responsive to human needs. Australia's Most Innovative Engineers by Engineers Australia (EA) in 2018 Dr. Carmichael actively supervises research students and leads multiple funded research projects. His grants portfolio includes collaborations with NOKIA, OMRON, NSW Centre for Work Health and Safety, Rookwood General Cemetery, and the Australian Cobotics Centre. He has secured funding for projects including 'Physical human-robot interaction,' '5G Connected Cobots,' 'ARC Training Centre for Collaborative Robotics in Advanced Manufacturing,' and 'Next-Generation Intelligent Robotic Mobility Aid for Vision Impaired People.' As Director of Teaching and Student Engagement at the UTS Robotics Institute, he champions educational initiatives including undergraduate engineering subjects and industry-facing micro-credentials. His leadership in the HRI lab fosters innovation in human-robot teaming, with applications spanning industrial automation, assistive technology, and rehabilitation.

• Machine Learning
• Medical Device Design
• Statistical Pattern Recognition
• +۶ مورد دیگر

Adjunct Professor Andrew Bradley is affiliated with Queensland University of Technology (QUT), where he serves as the Associate Dean (Research) for the Science and Engineering Faculty. He holds positions in the Faculty of Engineering and School of Electrical Engineering & Robotics at QUT. Bradley has a PhD from the University of Queensland and extensive industry experience, including roles as Chief Technology Officer at Ausonex Pty Ltd and Principal Biomedical Engineer at Maxwell MRI. His expertise spans biomedical and electrical engineering, computing science, and communications technology. His research focuses on computer-aided diagnosis (CAD) systems in medical imaging, machine learning, medical device design, and statistical pattern recognition. He has contributed to projects involving deep brain stimulation, artificial hearts, neonatal hearing screening, and cancer diagnosis imaging. His work bridges academia and industry, emphasizing commercially oriented solutions while maintaining a strong publication record. Bradley has published over 140 peer-reviewed papers and holds 20 patents across multiple regions. His research has garnered over 8,400 citations with an h-index of 35. He has secured $6M+ in competitive grants and supported startups raising $4M in venture capital. His awards include the ARC Future Fellowship and Fellow of Engineers Australia. He has advised 21 PhD and 4 Research Masters graduates, with three current advisory roles. Bradley’s contributions span interdisciplinary collaborations and innovation in labs focusing on medical imaging, AI, and biomedical engineering applications.

Yangyang Shuمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Weakly Supervised Learning
• +۷ مورد دیگر

Dr. Yangyang Shu serves as an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW) Canberra campus. Previously, he held a Research Fellow position at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he contributed to the Centre for Augmented Reasoning (CAR) project. His academic foundation includes a Ph.D. in Computer Science from the University of Technology Sydney. His research spans cutting-edge areas in artificial intelligence, with core expertise in Machine Learning and Computer Vision . Key focus areas include: Low-supervised paradigms (Weakly/Semi-/Self-Supervised Learning) Rationale-Guided Machine Learning systems Generative AI applications Machine Learning in Music and affective computing Fine-grained visual recognition in data-scarce environments Dr. Shu's publication record demonstrates consistent contributions to top-tier venues including CVPR, ECCV, IEEE Transactions on Multimedia, and Pattern Recognition. His work bridges theoretical advances in learning with practical applications in photo aesthetic assessment, semantic segmentation, and emotion recognition, showing particular strength in developing methods for low-data regimes and leveraging privileged information. He maintains active research collaborations through the School of Systems and Computing at UNSW Canberra, building on prior affiliations with AIML and the Centre for Augmented Reasoning. His current teaching responsibilities include ZEIT 2103 (Data Structures and Representation) for Semester 1, 2025.

Abbas Kouzaniمشاهده پروفایل

استاد

• Biomedical Engineering
• 3D Printing
• Neurotechnology
• +۵ مورد دیگر

Abbas Kouzani is a Professor of Engineering at the Deakin University School of Engineering , with a focus on cutting-edge biomedical engineering and AI applications. His work bridges 3D/4D printing and clinical challenges , particularly in dysphagia management and neurological disorders.

Nandini Sidnalمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Blockchain Technology
• Semantic Analytics
• +۸ مورد دیگر

Nandini Sidnal serves as Senior Learning Facilitator and National Academic Course Coordinator for Torrens University's Master of Software Engineering program through the Centre for Artificial Intelligence Research and Optimisation (AIRO). With over 20 years of international teaching experience in Computer Science, Engineering, and Networking, she has established herself as a key academic figure in AI and blockchain applications. Her educational foundation includes: PhD in Computer Science and Engineering (Cognitive Computing using Intelligent Agents) from Visvesvaraya Technological University (2012) M.Tech in Computer Science and Engineering (Parallel and Distributed Computing using Intelligent Mobile Agents) (2003) Bachelor of Engineering (1993) Nandini's research spans Artificial Intelligence, Blockchain Security, and Cognitive Computing , with strong emphasis on practical implementations in agriculture and healthcare. Her work integrates intelligent agents with distributed systems to solve real-world problems like food supply chain security and medical diagnostics, demonstrating consistent innovation from her early best paper award-winning thesis to current cutting-edge applications. Recent publications reveal a pronounced trend toward AI-driven agricultural optimization (dairy quality, aeroponics, nut farming) and healthcare diagnostics (epilepsy detection), alongside critical work in edge security. These outputs consistently bridge theoretical frameworks with tangible industry solutions, particularly in blockchain-secured IoT systems and deep learning applications. Her scientific recognition includes: Best Paper Award at an international conference for distributed computing research Nandini actively mentors high-impact projects including 'Strengthening Mobile-Based Services for Agriculture' and 'Enhancing VANET Performance with Cloud and Edge Technology.' Her industry collaborations with Intel (Parallel Programming integration) and Nokia (Mobility Research Lab establishment in Finland) demonstrate exceptional academic-industry synergy. The AIRO Centre serves as her primary research hub where she guides PhD candidates in blockchain-secured agri-supply chains and semantic recommender systems. Her Mobility Research Lab in Finland remains a cornerstone of her practical innovation legacy, focusing on next-generation mobile application development that continues to influence current VANET and edge computing research directions.

• Biomedical Engineering
• Cardiac Electrophysiology
• Neural Interfaces
• +۵ مورد دیگر

Overview Amr Al Abed is a Senior Lecturer at the Graduate School of Biomedical Engineering, University of New South Wales . He specializes in computational modeling, biomedical instrumentation, and electrophysiology with a focus on cardiac and neural systems. His work bridges biocomputational simulations with experimental validation, emphasizing translational applications like gene therapy delivery, medical devices, and brain-machine interfaces. Education PhD in Biomedical Engineering (UNSW, 2012) Masters in Biomedical Engineering (UNSW, 2006) Bachelor of Medical Sciences (1st Class Honours, UNSW, 2004) Research Interests His research spans: Optrode Technology: Development of liquid crystal-based optrodes for neural and cardiac sensing. Gene Electrotransfer: Novel methods for targeted DNA/RNA delivery using conductivity-clamped electric fields. Computational Cardiology: Multi-physics models of heart mechanics and fluid dynamics for surgical planning and device design. Neural Interfaces: Bidirectional stimulation/detection systems for retinal and neural prosthetics. Publications His 2023-2025 work highlights advancements in flexible electrode arrays , motorless robotic systems , and deep tissue gene delivery . Key themes include biomedical device innovation, computational modeling of cardiovascular systems, and neurotechnology. Grants & Labs Leads projects on liquid crystal optrodes and electrotransfer systems. Collaborates with labs focused on neural interfaces and cardiac electrophysiology. Active in IEEE and SPIE conferences with over 70 peer-reviewed publications.

• Artificial Intelligence
• Data Analytics
• Machine Learning
• +۳ مورد دیگر

Professor Xiaohui Tao is a distinguished academic at the University of Southern Queensland's School of Mathematics, Physics and Computing, leading the Computing Discipline Team and chairing the ICT Programs Governance Committee. He holds a PhD in Information Technology from Queensland University of Technology (2009). His research focuses on artificial intelligence, machine learning, and health informatics, with over 200 publications in top journals like IEEE TKDE and conferences such as AAAI and IJCAI. He has mentored 10 doctoral students and leads a research group developing real-world AI applications. Education: PhD in Information Technology, Queensland University of Technology, 2009 Research Interests: Dr. Tao's work spans AI-driven health informatics, machine learning algorithms, natural language processing, and privacy-preserving technologies. His projects address critical challenges like mental health monitoring, smart healthcare systems, and privacy in 5G/IoT environments. Recent advancements include federated learning for unlearning mechanisms and multimodal fusion for medical decision support. Grants & Awards: Awarded Australia Research Council grants (DP220101360), Australian Endeavour Fellowships, and multiple best paper awards at conferences like BESC’22 and WI-IAT’20. Recognized for contributions to remote patient monitoring and AI in depression treatment. Labs & Teams: Leads a research group focused on AI applications in healthcare and data science, collaborating on projects like computational social science for mental health and privacy-preserving IoT systems.

• Biomedical Engineering
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Associate Professor Rifai Chai is a faculty member in the Department of Biomedical Engineering at the School of Engineering, Swinburne University of Technology. He serves as the Academic Director (Partnerships), reflecting his leadership in academic-industry collaboration. His research focuses on the intersection of biomedical engineering and artificial intelligence, with applications in brain-computer interfaces, medical technologies, robotics, and embedded systems. University: Swinburne University of Technology School: School of Engineering Department: Biomedical Engineering Position: Associate Professor Email: rchai@swin.edu.au His educational and professional background includes over a decade of experience in product development in hardware, firmware, and software design in Indonesia and Australia from 2000 to 2011. His research interests are extensive and include: Artificial Intelligence and Machine Learning Brain-Computer Interfaces Medical Device Design Assistive Technology (e.g., smart wheelchairs, exoskeletons) Cognitive Fatigue and Workload Monitoring Back Pain Assessment and Rehabilitation Embedded Systems EEG and Physiological Signal Processing Rifai Chai's recent publications demonstrate a strong focus on AI-driven healthcare solutions. His work spans advanced computational intelligence in medical imaging (e.g., lung cancer and thyroid cancer detection), EEG-based systems for driver fatigue and emotion classification, rehabilitation technologies using exoskeletons, and cybersecurity in industrial IoT. He employs deep learning, hybrid models (e.g., MLP-BiLSTM), and advanced signal processing techniques to solve real-world biomedical problems. His research consistently targets practical, non-invasive, and real-time applications in clinical and industrial settings. His scientific contributions include numerous high-impact publications in journals such as IEEE Access, Sensors, and Medical & Biological Engineering & Computing, as well as book chapters with Elsevier and Springer. He has secured multiple research grants from industry partners and the Australian Research Council, supporting projects in breast electromagnetic scanning, solar-powered charging poles, and haptically-enabled motion simulation. Rifai Chai actively supervises PhD and Master’s students, with current HDR projects covering areas such as brain-computer interfaces for prosthetics, cognitive workload monitoring, distracted driving detection, and AI in medical imaging. He leads a multidisciplinary research team working at the forefront of biomedical innovation. His lab integrates AI, robotics, and physiological sensing to develop technologies that improve health outcomes and human performance.

Ayman Gh.مشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Computational Biology
• Preference Learning
• +۵ مورد دیگر

Ayman Gh. serves as a Postdoctoral Research Fellow at the Australian National University's Biological Data Science Institute (BDSI) and concurrently as a Visitor Scientist at CSIRO Bioprediction, Agriculture and Food Division, Crops Integrated Analytics. His academic foundation includes a 2022 PhD in Advanced Machine Learning from the University of Technology Sydney (UTS) and prior Biomedical Engineering degrees from Cairo University, Egypt. Education: PhD in Advanced Machine Learning, School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia (2022) Master of Biomedical Engineering, Faculty of Engineering, Cairo University, Egypt Bachelor of Biomedical Engineering, Faculty of Engineering, Cairo University, Egypt Research Focus: Gh.'s work centers on neural network applications for BioSignal processing, Bioinformatics, and Preference Learning. His expertise spans computational genomics, brain-computer interfaces, and novel machine learning algorithms, addressing complex challenges in biomedical image processing and neural decoding through deep learning architectures. Publication Analysis: His 2012-2024 publications reveal an evolution from ECoG-based finger movement decoding to advanced image generation techniques. Consistently leveraging neural networks, his research bridges biomedical engineering and computer vision, with increasing emphasis on generative models and robust computational solutions for biological data. Scientific Recognition: No specific awards or fellowships are documented in the provided materials. Mentorship: Registered for research student supervision though no advisees are listed, with no grant funding details disclosed in the available information. Collaborative Infrastructure: Operates within ANU's Biological Data Science Institute while maintaining active collaboration with CSIRO's Bioprediction team in agricultural analytics.

• Artificial Intelligence
• Natural Language Processing
• Data Analytics
• +۴ مورد دیگر

Professor Xiaohui Tao is a leading academic at the University of Southern Queensland (UniSQ) within the School of Mathematics, Physics and Computing. He serves as Computing Discipline Team Leader and Chair of the ICT Programs' Governance and Leadership Committee. With a PhD in Information Technology from Queensland University of Technology (2009), he has over 200 publications in top-tier journals and conferences like TKDE, AAAI, and SIGIR. His research spans Artificial Intelligence, Natural Language Processing, Machine Learning, Health Informatics, and Data Mining. He leads a prominent research group focused on real-world applications of innovative algorithms and systems. Recent articles include works on automatic summarization, brain computing, and machine unlearning His 2024 publications demonstrate leadership in Explainable AI, Recommender Systems, and Healthcare Applications Awarded the Australia Research Council Grant and Endeavour Research Fellowship, he actively contributes to academic governance as acting Head of School and editorial roles in journals like Elsevier's Natural Language Processing Journal. His mentorship has guided 10+ doctoral candidates to completion.

• Materials Engineering
• Nanotechnology
• Semiconductor Technology
• +۵ مورد دیگر

Prof Francesca Iacopi is an Adjunct Professor at the Faculty of Engineering & Information Technology , University of Technology Sydney (UTS). She also holds an adjunct position at Purdue University, IN, USA, and serves as Imec Fellow & Director of the Indiana R&D Center for Imec USA. With a PhD in Electrical Engineering/Materials Science from Katholieke Universiteit Leuven (2004) and MSc in Physics (1996) from Sapienza University of Rome, she has 20+ years of leadership in semiconductor R&D and academic innovation. IEEE Fellow & Elected Board of Governors Member (2021-2026) Inaugural Editor-in-Chief, IEEE Transactions on Materials for Electron Devices Chief Investigator & Industry Liaison Chair, ARC Centre of Excellence for Transformative Meta-Optical Systems Research Focus spans graphene electronics , additive manufacturing for microwave components , and energy-efficient nanodevice design . Her work bridges semiconductor industry experience (GlobalFoundries, IMEC) with academic breakthroughs in epitaxial graphene integration , nanoscale thermal emitters , and brain-machine interface sensors . Key Trends in her recent publications: 3D-printed metasurfaces for wireless communication, Fermi level tuning in graphene devices, and high-temperature operando characterization of graphene growth mechanisms. These align with her broader vision of multi-functionality on silicon platforms. Scientific Recognition : Australian Research Council Future Fellowship (2012-2016) Gold Graduate Student Award (2003, Materials Research Society) Global Innovation Award (2014) IEEE Fellowship (2024) Leadership & Education : Founded UTS's Bachelor of Engineering (Hons) Major in Electronics. Serves on UTS Academic Board (2021) and leads the Integrated Nano Systems Lab . Her teaching spans semiconductor physics, nanofabrication, and IoT component design, emphasizing cross-domain integration of CMOS, photonics, and energy storage systems.

• Artificial Intelligence
• Human-Computer Interaction
• Brain-Computer Interfaces
• +۵ مورد دیگر

Dr. Thomas Do is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), where he also serves as Co-Director of the Human-centric AI (HAI) Centre since March 2023. His academic appointments at UTS include Senior Lecturer (November 2023-present), Research Fellow (November 2021-October 2023), and Research Associate (October 2020-October 2021). He is a member of the Faculty of Engineering and Information Technology, the Australian Artificial Intelligence Institute (AAII), and the School of Computer Science at UTS. Dr. Do's educational background includes: PhD in Computer Science from the University of Technology Sydney (2020) Master's degree in Human-Computer Interaction and Robotics from the Korea Institute of Science and Technology (2016) Dr. Do's research spans the intersection of artificial intelligence, neuroscience, and human-computer interaction. His primary focus is on Brain-Computer Interfaces (BCIs), where he investigates how to integrate BCI technology into everyday applications. He explores the convergence of AI, data science, neural engineering, and human-computer interaction to unlock the full potential of BCIs. His current projects include Human-centric AI technologies (funded by GrapheneX), Brain-Robot Interfaces (funded by Australian Defence), and Human-AI teaming (funded by ARC DP). His work often involves EEG analysis, spatial navigation studies, and object recognition systems. Dr. Do's recent publications demonstrate a strong focus on advancing Brain-Computer Interface technology through innovative approaches to signal processing, multimodal integration, and practical applications. His work spans from fundamental neuroscience research on brain connectivity patterns to applied AI systems for real-world problems in healthcare, transportation, and security. A notable trend in his research is the integration of explainable AI methods with neuroscience to create more transparent and trustworthy BCI systems. Dr. Do is actively involved in teaching and supervision at UTS, where he has taught courses including Data System, AI/Analytics Capstone Project, Technology Research Methods, and Interactive Media. His current research is supported by an ARC Discovery Project grant titled "Mind-reading AI to translate silent speech into words" (2025-2027). Dr. Do maintains active collaborations with institutions worldwide, having previously worked with the Technical University of Berlin, Germany, the University of California San Diego, USA, and the US Army Research Lab. His research has practical applications in medical, defense, and consumer technology sectors.

• Biomechanical Engineering
• Neuromusculoskeletal Biomechanics
• Digital Twin Simulations
• +۵ مورد دیگر

Professor David Lloyd is a distinguished academic at Griffith University's School of Health Sciences, specializing in Exercise Science. With over 35 years of research experience in Biomechanical Engineering, he is recognized as an international leader in neuromusculoskeletal biomechanics and rehabilitation engineering. His academic journey began with a BSc-Merit in Engineering from UNSW in 1984, followed by a PhD in Biomechanical Engineering from UNSW in 1993, and an NIH Fogarty International Postdoctoral Fellowship from 1993-1995. Professor Lloyd has held significant leadership positions including serving as founding director of the Griffith Centre for Biomedical and Rehabilitation Engineering (GCORE) from November 2015 until February 2024, which evolved into the Australian Centre of Precision Health and Technology (PRECISE). He continues as a Member of PRECISE (2025-present) and leads the Medical Devices Advanced Design and Prototyping Technologies Institute (ADaPT) since 2018. He also holds adjunct professorships at the University of Western Australia and University of Delaware. His research interests span neuromusculoskeletal biomechanical modeling, assistive devices for the neuromusculoskeletal system, surgical planning and implant design, and the causes, prevention, and management of neuromusculoskeletal conditions. Professor Lloyd and his team are pioneers in neuromusculoskeletal research, combining experimental studies with biophysical and AI-driven digital twin simulations that integrate laboratory instrumentation, medical imaging, computer vision, and wireless wearables. His extensive publication record includes over 300 peer-reviewed articles with more than 28,000 citations (h-index 88), demonstrating significant impact in biomechanics, rehabilitation engineering, and biomedical engineering. His recent work focuses on EMG-informed neuromusculoskeletal modeling, digital twins for personalized medical applications, brain-computer interfaces, and advanced computational techniques for orthopaedic engineering. Fellow of International Society of Biomechanics Recipient of 2020 Geoffrey Dyson Award by International Society of Biomechanics in Sport The Australian's 2019 Field Leader in Biophysics Ranked as world's 29th biomechanist (top 0.017%, Expertscape, Nov 2024) Ranked in top 0.3% of published biomedical engineers (Stanford University's World's Top 2% Scientists, Aug 2024) Professor Lloyd has secured over AUD $42 million in research funding as Chief or Co-Chief Investigator, demonstrating his ability to lead large-scale research initiatives. He has supervised 53 PhD completions and continues to mentor numerous doctoral students in areas including 3D printing applications in medical implant technologies, neural control of exoskeletons, and spinal cord injury rehabilitation. His research group collaborates extensively with hospitals (Gold Coast University Hospital and Queensland Children's Hospital) and industry partners including Orthocell, VALD, Materialise, OrthoPediatrics, Stryker, Adidas, and Philips.

Lim Lam Ghaiمشاهده پروفایل

مدرس

• Neuroimaging
• Functional Near-Infrared Spectroscopy (fNIRS)
• Brain-Computer Interface
• +۳ مورد دیگر

Dr. Lim Lam Ghai is a Lecturer at the School of Engineering, Monash University Malaysia. He holds a PhD in Electrical and Electronics Engineering from Universiti Teknologi PETRONAS (2022), along with a Master of Science (2017) and Bachelor of Engineering (2014) from the same institution. His research focuses on non-invasive neuroimaging (fNIRS) for mental health applications and AI-driven precision agriculture. He teaches Engineering Numerical Analysis and Applied Artificial Intelligence. Research Interests: Dr. Lim’s work bridges neuroimaging and AI, with projects on fNIRS for neurological diagnostics and AI in crop management. His contributions include enhancing fNIRS clinical applicability and developing autonomous agricultural systems. Collaborations span neuroscience, engineering, and sustainable development. Awards: He has received multiple accolades, including the IEEE SPS Malaysia Best PhD Thesis Award (2022), IChemE Training & Development Award (2022), and the International Illnovasi Competition prize (2022). Grants & Projects: He leads a project (2023–2026) on cognitive load estimation using deep learning. His work aligns with UN SDGs, particularly in health and sustainable agriculture. Active in peer review for journals like Biological Psychology and Frontiers in Human Neuroscience . Labs/Teams: Engaged in interdisciplinary research teams focused on neuroimaging technology and smart agriculture solutions.