Information Assurance and Governance, Healthcare Data Security and Privacy, Cyber Physical System (CPS) Security, Cloud-Assisted Internet of Things (IoTs) Security, Wireless Body Area Networks (WBAN) Security.
Contactless sensing, wearable, implants, RF sensing, radar sensing, antennas, plant health, sensing, animal health, remote healthcare
Research Interests: Sensor and Ad hoc Networks, Cyber-Physical Systems, Future Internet, Internet of Medical Things, Artificial Intelligence in Medical Field, Machine Learning Algorithms for Medical Data Processing, and Medical Big Data Analysis.
Machine Learning (data and signals), Clinical outcome prediction (neurorehabilitation), Clinical decision support systems (neurorehabilitation), Wearable inertial sensors
Research Interests: biomedical Engineering, Medical Devices, Biomedical Sensors, Ablation Therapy, Computer-aided Diagnosis Systems, Medical Imaging, Medical Image Analysis, Machine Learning, AI-Assisted Healthcare, Simulation and Modeling, Optimization Algorithms, Neutrosophic Theory, Smart Antenna, Target Tracking, and Direction of Arrival Estimation.
Clinical research informatics, clinical decision support, clinical pathways and clinical workflow optimization, healthcare information management, interoperability.
Bioinformatics, Systems Biology, Predictive Modeling, Biological Networks, Machine Learning
Junyi Cao is a Full Professor of Mechanical Engineering at Xi'an Jiaotong University. He received the Ph.D. degree of mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2006. From September 2013 to September 2014, he was a visiting scholar with the Department of Aerospace Engineering, University of Michigan, Ann Arbor. Now, he is a director of the Institute of Design Science and Basic Components at Xi'an Jiaotong University and vice Chair of Society of Shaanxi Vibration Engineering. His main research interests include wearable biomedical sensors, Medical intelligent diagnosis, Neurodegenerative diseases, Robot-aided diagnosis system, Gait modeling and dynamics, Biomedical information fusion, Clinical health monitoring, Deep learning. He is a recipient of 2021 Best Paper Award of ASME Journal of Vibration and Acoustics, First Prize in Natural Science of Shaaxi Provine Education, 2017 IOP Best Poster Paper Award at 4th Workshop in Devices, Materials and Structures for Energy Harvesting and Storage.
Adaptive computational techniques with application to bio-signals (particularly, for cardiovascular applications), pattern recognition, modelling and Clinical Informatics.
Research Interests: Trustworthy AI, Medical Image Analysis, Deep Learning, Computer Vision, Bioinformatics
Medical monitoring system, patient health monitoring, neonatal monitoring, brain activity monitoring, smart sleep, smart rehabilitation system, wireless body area networks, wearable sensor systems, internet of things, ambient intelligence, personalized and smart environment, smart sensor systems, and signal processing.
Research Interests: bioinformatics, computational biology, systems biology, association prediction, computational model, microRNA, lncRNA, non-coding RNA, drug discovery, drug combination, complex disease, drug response prediction.
Healthcare Mechatronics, Medical Robotics, Computer-Assisted Surgery, Collaborative Robotics, Computer Vision, Artificial Intelligence, Machine/Deep Learning, and Biomedical Sensor Technologies.
Machine learning, mobile health, human behavior recognition and analysis, ambient assisted living.
Research Interests:
Medical Informatics, Health Records, Internet of Things, Distributed Systems, Mobile and Distributed Computing, Sensors and Wearables, Interoperability, Ubiquitous Computing, Telemedicine, Intelligent Interpretation of Health Data, and Ontologies and Web Semantics.
Dr. Cukur is Prof. of Electrical-Electronics Engineering, Prof. of Neuroscience, and Director of the National Magnetic Resonance Research Center (UMRAM) at Bilkent University.
Research Interests: sensor Informatics –wearable, wearable and assistive devices for rehabilitation, well-being and ageing population, algorithms, wearable technology, digital mobility outcomes, gait/ walking, postural control, Parkinson’s disease.
Body sensor networks; smart point-of-care and wireless physiological monitoring devices embedded system design, autonomic sensing, wearable and assistive devices for rehabilitation, well-being and ageing population; Electronic health record; intelligent interpretation of health data, decision support systems, remote guidance and virtual reality applications in diagnostic and therapeutic procedures. Pervasive healthcare, wellness management.
Research Interests: Machine learning; Clinical decision support systems; Simulation and modeling; Biomedical signal processing; Predictive modeling; Clinical engineering; Mobile health systems; Mobile applications; Wearable biomedical sensors.
Clinical Engineering, Human Computer Interaction, Telemedicine, Electronic Health Records
Biomedical engineering, cardiovascular disease, fluid-structure interaction, biomechanics, bioinformatics, biomedical image processing, medical informatics, multi-scale modeling, data mining, software engineering, parallel computing, computational chemistry and bioprocess modeling.
Research Interests: human-Machine Systems, Wearable Computing, Internet of Things computing and technology, agent-based computing, body area networks, wireless sensor networks, pervasive and cloud computing, multimedia networks, and mobile health systems.
Medical image analysis, medical image segmentation, machine learning, deep learning, multi-view/modal representation learning.
Medical image analysis, natural language processing, clinical decision support, human-computer interaction, artificial intelligence, scalable data-driven biomedical discovery, and high performance computing.
Embedded & Pervasive Systems, Algorithm Design, Context-Aware Computing, Cyber Physical Systems, Collaborative Processing, Design for Scalability & Robustness, Power-Aware Design, Computational Autonomy, Data Analytics, Transfer Learning, Mobile & Wireless Health, Fall Monitoring & Prevention, Sports Training.
Biomedical signal analysis, Machine learning, Wearable and assistive devices for rehabilitation, Remote home monitoring, Deep learning, Computer-aided clinical decision making
Pattern recognition and knowledge discovery from large scale, heterogeneous data, biosignal processing (EEG, eye-tracking data), computer vision, deep learning, multimodal adaptive interaction, social robots for assistive/rehabilitation activities, cognitive computing in medical systems.
Healthcare Information Management Platforms, Healthcare Data Security and Privacy, Large Scale Data Mining / Analytics, Privacy-Preserving Data Mining, Clinical Decision Support Systems, Pervasive / Ubiquitous Computing, Healthcare Applications.
Tracking systems, image and signal processing, state space modelling, filtering, embedded computing, and mHealth.
Research Interests: computational Intelligence, biomedical image processing, optimization techniques.
Non-invasive physiological monitoring, human health and performance, cardiomechanical signals, chronic disease management, home monitoring of health and disease.
Wearable computing, body sensor networks (BSN), mobile health, signal processing, design and analysis of cyber physical systems (CPS), physiological sensing, circuits and systems for electroencephalography, low power and light-weight embedded system design and optimization.
Biological signal (EEG, EMG, ECG etc.) processing, myoelectric control, brain computer interface, man-machine interface, neuromuscular system modeling, powered Orthotics, motor rehabilitation for stroke, neural plasticity.
Radar for assisted living and veterinarian applications, Future radar systems, 5G/6G
Research Interests: image processing, machine learning, medical image analysis, computer-aided diagnosis, and digital and analog signal processing.
Role as Dean of School of Systems and Technology. (July, 2020 to Date)
* Strategic planning for the School of Systems and Technology in terms of Academic and Infrastructure resources.
* Managing learning, teaching, research and student engagement across the school.
* Supervising the recruitment and appointments of high cadre faculty and staff.
* Assist other University wide offices like Rector, Registrar and QEC in setting and implementing best academic practices to yield higher standards.
* Chair Board of Faculty to supervise continual improvements in curriculum and academic processes.
* Participate in Dean Committee Meeting, Academic Council Meeting and Board of Advanced Studies and Research to provide feedback regarding University wide processes.
* Supervise arrangements regarding setup of state of art Labs and their requirements.
* Interact with local and international industry representatives for producing better industry-academia linkages and job placement for students.
* Interacting with funding agencies and exploring other funding opportunities for research projects and setting up research Labs.
* Coordinating with Alumni, alleviating their concerns and establishing a cordial relationship with them.
Area of Research: Artificial Intelligence, Machine Learning, Computer Security, Pattern Recognition, Image Processing, Computer Vision, Bioinformatics, Mathematical Modelling, Artificial Neural networks, Image processing, Systems Programming.
Area of Teaching Interest:Operating Systems, Computer Networks & Data Communication, Parallel & Pipelined Processing, Computer System Architecture, Neural networks, Discrete Mathematics, Computer graphics, Image processing, Assembly Language Programming, Systems Programming, Artificial Intelligence, Artificial Neural Networks, Computer Networks and Network Programming, Windows Based Desktop and Internet Programming.
Medical Image Computing, Medical Ultrasound Image Analysis, Alzheimer’s Disease Diagnosis, Artificial Intelligence in Medicine, Tumor Detection, AI-driven Health Informatics, Bioinformatics, Brain Disease Analysis, Longitudinal Study
Body Sensor Networks, Wearable Sensors, Signal processing, Non-invasive physiological monitoring, Machine Learning, Medical Instrumentation, Bioimpedance, Gerontotechnology.
Systems biology, bionano-devices, structural bioinformatics, molecular stochastic networks, wound healing, and cellular pattern formation.
Artificial Intelligence and Deep Learning, Data Analytics and Mining of Health Informatics, NLP modeling in Mental Health, Internet of Medical Things (IoMT)
Research Interests: biomedical Image, analysis, healthcare bigdata, deep Learning.
Computer Vision, Temporal Tracking, Machine Learning, Image segmentation, Stochastic inferencing, Body Sensor Networks, Pervasive/Ubiquitous Computing.
Medical image analysis and computing, Radiomics and Radiogenomics, Imaging Informatics, AI applications in medical imaging and personalised medicine informatics.
Wearable Sensors, Internet of Medical Things (IoMT), Machine Learning, Data Science, Wearable Sensor Systems, Internet of Things, Security and Privacy in Health Care & AI-driven Health Informatics.
Dr. Priyan Malarvizhi Kumar is currently an Assistant Professor at the University of North Texas in the United States. Prior to this role, he served as an Assistant Professor at both Gannon University in the USA and Kyung Hee University in South Korea. He also gained valuable experience during his time as a Postdoctoral Research Fellow at Middlesex University in London, UK. Dr. Kumar earned his Ph.D. degree from Vellore Institute of Technology.
His ongoing research encompasses various domains, including Big Data Analytics, Internet of Things (IoT), Internet of Everything (IoE), and Internet of Vehicles (IoV), with a specific focus on healthcare applications. Dr. Kumar has authored and co-authored numerous papers that have been published in prestigious international journals and conferences, including those indexed by the Science Citation Index (SCI) such as IEEE transactions and ACM Transactions. In addition to his academic contributions, Dr. Kumar serves as an Editorial board member for the International Journal of Data Science and Analytics published by Springer and the IoT Journal published by MDPI. Furthermore, he holds the position of Associate Editor for the International Journal of Wireless Networks and Broadband Technologies (IJWNBT) published by IGI and the IEEE Journal of Biomedical Health Informatics.
Professor and Director,
Database Systems Laboratory
Multidimensional Data Analysis and Knowledge Management Laboratory
Computer Engineering and Informatics Department
University of Patras
Prof. Vasileios Megalooikonomou received his B.S. in CS&CE from Univ. of Patras, Greece in 1991, and his M.S. and Ph.D. in CS from Univ. of Maryland, USA, in 1995 and 1997, respectively. He has been on the faculty of Johns Hopkins Univ., Dartmouth College, Temple Univ. and Univ. of Patras where he is a Professor and Director of the Database Systems Laboratory. He has co-authored over 260 refereed articles in journals and conference proceedings and seven book chapters. His work has attracted over 4300 citations (h-index:34). His research interests include big data management and analytics, machine learning, biomedical informatics, medical imaging, decision support systems, and IoT. He is a member of the ACM, IEEE, SIAM, and SPIE. He received a CAREER award from the National Science Foundation (NSF) in 2003 to work on developing data mining methods for extracting patterns from medical databases. In the US he has served as a PI or a co-PI on research projects supported by National Science Foundation, National Institutes of Health, Pennsylvania Department of Health and the Lockheed Martin Corporation. He has served as the scientific coordinator/coordinator of a number of national and European projects including the FP7 ARMOR project, the BIOMEDMINE project and the H2020 FRAILSAFE project.
Biomedical radar systems and algorithms, biomedical applications of microwave/RF, remote patient monitoring, remote radar sensing, contactless sensing, remote healthcare, telemedicine, long-term health monitoring, capacitive ECG, inductive wireless power transfer, wireless sensors, biomedical circuits and systems, fall detection, indoor localization, contactless vital signs monitoring, people tracking.
After received my MSc degree in Electrical Engineering, I started in 2010 my PhD at the Department of Electrical Engineering (ESAT) of KU Leuven, Leuven, Belgium. In 2015, I have been granted of the PhD degree with the thesis entitled: “Development of contactless health monitoring sensors and integration in wireless sensor networks”. During this period, my research interests included: biomedical applications of microwave/RF, remote patient monitoring, radar systems and signal processing algorithms for biomedical applications. I was one of the first researchers in the world investigating the radars for contactless remote vital signs monitoring and indoor positioning, and a pioneer in demonstrating the use of radars for remote fall detection.
In 2015, as a senior researcher at imec-Netherlands, within the Connected Health Solution (CHS) group, I set up the “Radar-based Noncontact Sensing” research program. Before that, imec used radar technologies only in automotive. I lead the radar activities towards contactless long-term health monitoring solutions, in particular focusing on vital signs monitoring, localization and tracking, and driver monitoring. In space of 4 years, my group at imec was able to close the gap with the major competitors in the field (e.g., Texas Instruments, Novelda) and proposed innovative solutions that improved the current state of the art at IC, system and algorithm level. In fact, the outcomes of my studies, conducted with demonstrators that I have personally implemented using commercial components, the developed algorithms, resulted in the imec 8 GHz UWB radar chip based on a novel radar architecture that achieves 100× improvement in power efficiency than alternative solutions and it is capable of monitoring heartbeat (up to 5 m) and respiration (beyond 15 m) of multiple subjects. This is the first ever battery powered radar chip for indoor application and can be powered by a single AA battery for more than half year. This radar won the ISSCC 2019 Demonstration Session Certificate of Recognition. With these breakthroughs and scientific outcomes, I became one of the most famous researchers in the field of biomedical radars.
In September 2021, I joined as an Experienced Researcher in Biomedical Engineering the Department of Computer Engineering, Modelling, Electronics and Systems of University of Calabria (Unical). Since December 2023, I’m an Associate Professor of Biomedical Engineering at the University Mediterranea of Reggio Calabria. My research interests include: biomedical radar systems and signal processing algorithms, remote patient monitoring and biomedical applications. Since September 2022, I have been working with imec-NL on Inductive Wireless Power Transfer.
Dr. Ahmed Abdelhadi Metwally is a Senior Research Scientist at Google. His research focuses on developing AI methods for longitudinal multimodal biomedical data fusion (wearables and omics) to enable early detection of cardiometabolic and infectious diseases and personalize their treatments.
Before joining Google, he was a Senior AI Scientist at Illumina. Dr. Metwally completed his postdoctoral studies at the Snyder Lab, Stanford University (2018-2021), and holds a PhD in Biomedical Engineering and MSc in Computer Science from the University of Illinois at Chicago (2018). He earned a BSc in Biomedical Engineering with first-class honors from Cairo University, Egypt. With over 60 publications in prestigious journals, including Nature Biomedical Engineering, Nature, and Science, his work has been featured in The New York Times, US News, CNBC, Fox News, among others. He has delivered over 50 keynote and invited talks globally and is a co-inventor of three patents related to early diabetes detection. He has received numerous awards, including the NIH Predoctoral Translational Scientist fellowship, Stanford RISE award, and ISMB’20 Best Talk award. Dr. Metwally is an IEEE senior member and was elected globally to serve on the board of IEEE EMBS from 2017 to 2019. He has served in various IEEE EMBS leadership roles since 2010, including Program Chair for BHI’24, Industry Chair for BHI’23, Student Activities Chair for BHI’19 and EMBC’18, and Conference Chair for the inaugural EMBS Student Conference in 2013.
Robot-assisted Surgery, Computer-Assisted Surgery, Medical image segmentation, Artificial Intelligence for Medical Image and Video analysis, Machine/Deep Learning, Telemedicine
Associate Professor, Department of Computer Science & Engineering, Center for Remote Health Technologies and Systems, Texas A&M University
Assistant Professor Adjunct, Center for Outcomes Research and Evaluation, Yale School of Medicine, Yale University
Research Themes
- Systems and Analytics for Personalized Digital Health: Design of machine learning methods for clinical outcomes using multimodal modeling and clinician-in-the-loop time-varying, dynamic risk prediction.
- Personalized Sensing and IoMT: Design of analytics to connect Internet of Medical Things to clinical outcomes research for personal and remote sensing and digital health.
- Translational Clinical Outcomes Research: Implement techniques to enable clinical translation and facilitate clinical interventions, observational comparative effectiveness, and improve outcomes.
HIGHLIGHTS
- Research: Establishing research that serves as an integral bridge for interdisciplinary teams in creating novel sensing, developing advanced analytics, and facilitating clinical translation. Google Scholar H-Index: 29.
- Produced first analytic models to automate diet monitoring from continuous glucose monitors.
- Designed advanced analytics for novel wearable sensors to create cuffless blood pressure monitors.
- Developed new machine learning techniques for dynamic, adaptable clinical outcomes research.
- Teaching: Design of machine learning classes to promote digital health. Teaching machine learning and capstone courses with a focus on translation.
- Service: Integrating objectives and connecting communities through service.
- Serving cross-cutting disciplines in engineering (Associate editor ACM Transactions on Computing for Healthcare) and health sciences (Associate statistical editor, AHA Circulation: Cardiovascular Quality and Outcomes), and the Journal of the American College of Cardiology
- Served in conference organization leadership for several flagship conferences including IEEE BSN, IEEE BHI, and AHLI CHIL.
Artificial Intelligence, Machine and Deep Learning, Computer Vision, Pattern Recognition, Intelligent Sensors, Biological signal processing, Human-Machine Systems, Digital Health, AI-Assisted Healthcare, Telemonitoring, Telehealth, Intelligent Consumer Technologies, Internet of Things
Research Interests: medical video processing and analysis, medical video communications, mHealth/ eHealth/ Telehealth, deep learning, computer vision.
Sensor signal processing, Data analytics, Wireless communication technologies, Control systems, Body sensor networks, Signal processing and control techniques for healthcare and personal well-being applications.
Industry4.0, healthcare 4.0, healthcare informatics, hospital automation, home healthcare, elderly healthcare, medical robotics, wireless sensor and actuator networks, cyber physical systems, Internet-of-Things.
Imaging Informatics, Medical Informatics, Public Health Informatics, Multi-Modal AI
e-Health, m-Health, and e-Emergency systems, medical image analysis systems (MRI, ultrasound, endoscopy, microscopy), biosignal analysis systems (electromyography), computational intelligence in medical systems, life sciences informatics.
Biomedical time-series, machine learning and deep-learning, health technology assessment (HTA), reverse engineering for early stage health economics, biomedical regulatory science, clinical engineering, healthy ageing, chronic non-communicable disease, prediction and prevention of falls.
Artificial Intelligence in Biomedicine, Computation Pathology, Imaging Informatics, Biomedical Image Computing,Tumor modeling, Computer Vision.
Dr. Qin is currently the professor of Shenzhen Institute of Advanced Technology , Chinese Academy of Sciences and Director of the Shenzhen-Hong Kong Joint Lab on Intelligence Computational Analysis for Tumor Imaging. He obtained my Ph.D. in Pattern Recognition and Intelligent System from the University of Chinese Academy of Sciences. During this period, He spent one year at Stanford University in the United States as a visiting PhD student working on artificial intelligence in medicine.
His research area focuses on the research of computing optical imaging, medical image computing and analysis, and artificial intelligence, which tackles the challenges of leveraging multi-modal medical data, to explore the new computing theories and methods of the learning-based algorithm in clinical diagnosis and treatment. He is currently the principal investigator (PI) or co-investigator (Co-I) of the National Key R& D Program of China, NSFC, Key R& D Program of Guangdong, and Key Natural Science Foundation of Shenzhen and projects from other funding agencies.
Until now, He has published more than 80 peer-reviewed publications (SCI/EI) in the well-known Journal and conference, including IEEE Trans. Med Imag., IEEE J. Biomed. Health, IEEE Trans. Comp. Imag., RADIOTHER. ONCOL., AM. J. PATHOL,. MICCAI, et al. He also have 48 patents issued and 100 patents pending to be issued as the first inventor or co-inventor. In terms of awards, he has received numerous Science and Technology Advance Awards. He is also the initiator and lead organizer of the International Workshop on Computational Mathematics Modeling in Cancer Analysis in MICCAI (CMMCA 2022/2023).
Digital health, Health Internet of Thing, process mining, patient empowerment, digital health literacy, mobile health
Associate Professor (Tenured) of Quantitative Health, Department of Medicine
Affiliate Associate Professor, Electrical & Computer Engineering, Biomedical Engineering, and Health Outcomes and Biomedical Informatics
Associate Director for Imaging, Intelligent Critical Care Center University of Florida at Gainesville
Body sensor networks; wearable, ingestible and implantable systems; biosensor design, miniaturisation and embodiment; smart point-of-care and wireless physiological monitoring devices (e.g. ECG, EMG, EEG and PPG); ASIC and embedded system design, on-node processing, smart devices and app development; autonomic sensing, distributed inference, context aware sensing and multi-sensor fusion; wearable and assistive devices for rehabilitation, well-being and ageing population.
Biomedical signal processing, wearable monitoring systems, mobile health, man-machine interface, mental disorders, autonomic nervous system, affective computing
Medical image analysis, computer vision, and pattern recognition.
Research Interests: wearable sensors, machine learning, hand gesture recognition, wearable haptics, movement training and rehabilitation, gait biomechanics, human computer interaction.
Research Interests: multimedia data hiding, image processing, biometrics & Cryptography.
Research interests: immersive techniques, augmented/mixed/virtual realities, medical navigation systems, computer-aided surgery, medical image analysis, image registration
Bioinformatics, Medical Image Analysis, Computational Biology, Machine Learning, Pattern Recognition, Predictive Modeling, Systems Biology
Brain Computer Interfaces, Multimodal functional neuroimaging integration (EEG/high-density EEG, fMRI, ASL, EEG-TMS data), Brain functional connectivity inference, Network analysis in health and clinical applications (epilepsy, stroke, neurodegenerativediseases, etc.)
Artificial Intelligence, Machine Learning and Biomedical Image Data Processing
Prof. Abdulhamit Subasi is specialized in Artificial Intelligence, Machine Learning, Biomedical Signal and Image Processing. He is also author of the books, “Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques” and “Practical Machine Learning for Data Analysis Using Python”. Since 2020, he has been working as a Professor of medical physics at Faculty of Medicine, University of Turku, Turku, Finland .
Research Interests:
Computer Vision, Ambient Health Monitoring, AI-Assisted Healthcare
Medical instrumentation and transducers, rehabilitation engineering, assistive technology, home health care devices.
Molecular imaging, Medical image analysis, and pattern recognition.
Biomedical data mining, health informatics analytics, biomarker discovery, health risk assessment/modelling, telecare systems, mobile health, knowledge discovery in electronic medical records.
Semantic health data integration, smart eHealth and mHealth service platforms, ubiquitous sensor-based human behavior modeling, activity and context recognition, affective computing, socio-economic aspects of eHealth and mHealth technologies and services.
Research Interests: monitoring health, diagnosis and prognosis of neurological, psychiatric and neurodegenerative conditions
Keywords: Medical image analysis, machine learning, deep learning, vision and language, large-scale medical data, federated learning
Medical Imaging, Computer Aided Diagnosis, Imaging Biomarkers, Data science and data engineering for biomedicine and health, Machine learning and artificial intelligence methodologies for biomedical data analysis and interpretation
Keywords: Medical Image Analysis, Medical Imaging, Machine Learning, Biomechanics
Medical image analysis, medical image segmentation, machine learning, deep learning, biomechanics, computational physiology (cardiac physiology and tumor growth), model personalization.
Precision Mental Health, Biomarker Development and Validation, Biomedical Signal Processing, Neural Engineering, Electroencephalography (EEG)
Wireless body-worn devices, embedded networked systems integrating sensor information processing, embedded inference, and internetworked devices.
Medical image analysis, computer vision, shape modelling, graph methods, and machine learning.
Research Interests: robot sensing and its applications in healthcare, Human-Robot Interface & Safe Interaction
Human Cyber Physical Systems(H-CPS), Flexible circuits & sensors and heterogeneous integration, Biomedical micro-circuits and systems, Health Internet-of-Things (IoT).
Health Internet of Things, Physiological signal processing and informatics, Computer Simulation and modeling of cardiovascular systems, Design and Analysis of Biomedical Experiments, Nonlinear Dynamics and Chaos, Biosensing and data mining.
Medical image analysis, digital pathology, endoscopic image, clinical decision support, precision medicine
Medical Image Analysis, Machine Learning, Manifold Learning, Multimodal Diagnosis, Trustworthy AI
Body Sensor Networks, Pervasive Sensing, Digital Medicine, Biomotion/Gait Analysis, Human Biomechanics, Rehabilitation robotics, Neuro-Rehabilitation, Physiological Signal Processing.
Medical image computing, machine learning, deep learning, image-guided surgery, computer-assisted interventions, medical robotics, musculoskeletal image analysis, clinical decision support, endoscopic image, statistical shape and deformation analysis, medical image segmentation, and medical image registration
Research Interests: machine learning, artificial intelligence, data mining and image analysis. He is keen to develop novel statistical models and deep learning based approaches to solve research problems in manufacturing, healthcare, materials, earth observation, robotics and social sciences.
Computer vision, image analysis, mammography, breast cancer, prostate cancer, biometrics, machine learning, manifold learning.