International Center for Health Information Technology
Research Field
Professor Shabbir Syed-Abdul is an accomplished health informatics researcher with expertise in artificial intelligence and digital healthcare. His research focuses on the long-term care of older adults and the early prediction and management of chronic diseases such as cancer and chronic kidney disease. He is a professor at the Graduate Institute of Biomedical Informatics and a principal investigator at the International Center for Health Information Technology in Taipei, Taiwan.
In addition to his roles as a physician and researcher, Professor Syed-Abdul has served as a principal investigator in both developed and developing countries, giving him a unique perspective on the factors that influence the development of sustainable healthcare, with a particular focus on innovation and aging populations.
Professor Syed-Abdul is a member of the social media Working Group at the International Medical Informatics Association and the Scientific Program Committee of the Asia-Pacific Association for Medical Informatics. He has been actively involved in a number of projects, including a mobile teledermatology project using smartphones in resource-poor settings in developing countries, an open personal health record project, and several European Commission-funded H2020 projects.
Prof. Shabbir has an excellent track record of accomplishing projects and publishing over 130 articles in the SCI prestigious journals including The Lancet, BMJ, JAMIA, and Journal of Medical Internet Research. He is a regular invited speaker on a broad range of topics on Clinical decision support systems, Innovation, Digital Health, AI & Machine Learning in Health. He chairs, moderates, and serves on the program committee at numerous national and international conferences. He is also a consultant for the Health IT industry, and guest editor of 3 SCI journals.
His major research interests include artificial intelligence, big data analysis and visualization, long-term care with wearable technologies, mHealth, personal health records, social networks in healthcare, and hospital information systems. Through his work, he aims to empower care providers and improve patient engagement and empowerment, with a focus on the management and flow of health and medical information among healthcare providers and seekers.
Patents
• TW Patent #. M 409487. Mobile Personal Health Record System
• US Patent# 20150356272 Prescription Analysis System And Method For Applying Probabilistic Model-Based On Medical Big Data
• US Patent# US20180360346A1 Electronic incentive spirometer
The International Center for Health Information Technology(ICHIT) is a leading center who have demonstrated the excellence in cutting-edge research in the utilization of ICT in the healthcare. Our focus is 4P medicine (Prediction, Prevention, Participation, and Personalization). We work closely with our teaching hospitals and practice translational medicine to the best knowledge. We formed a multidisciplinary team consisting of experts from Biomedical informatics, Data mining, Natural Language Processing, and Social Networking experts, Consultants from Neurology, Cardiology, Nephrology, and Dermatology, Physicians, Nurses and medical technologists. We are mainly focusing on translational research in HIT (Health Information Technology).
In this context, we can offer you design as well as functional analysis of the software, business, and processes analytics, and integration of heterogeneous e-Health and m-Health systems through a common open platform.
Health IoT The Internet of Things is a concept in which more devices (sensors/things) will be communicating across the Internet than human users. Explore technologies that collect both clinical and lifestyle data to track compliance with care plans, assess the role of wearable technology in improving outcomes for chronic and acute conditions for patients.
Telemedicine & mHealth The mHealth + Telehealth technologies brings together hospitals, policy makers, and innovators to discuss the future of connected health. It bridge the gap between rural health seekers with urban health providers.
Big Data Analytis & Visualization Data visualization is the presentation of data in a pictorial or graphical format. Converting millions of data points into one graph or map for easy understanding. The potential healthcare benefits are immense, and data governance best practices can be used to help ensure a safer and quality care.
e-Learning & Medical Education Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as e-health, telematics, telemedicine, tele-education, etc. Explore the various technologies and communication systems for the need of e-health, telemedicine, e-Learning or tele-education.
- Artificial Intelligence
- Digital Health
- Social Networking in Healthcare
- Health Ageing Technologies
- Plum Blossom Card (Permanent Resident Certificate) by the Government of Taiwan 2017.
- New Investigator of Global Health by Global Health Council 2010. Washington DC.
- National Yang-Ming University Scholarship 2009—2011.
- International Scholar Ex-Change program by Norwegian Research Council 2009. • Quota scholarship at Tromso University, Norway 2006--2008.
Medical Doctor – Saint Petersburg Medical Academy Russia.
Master of Science – Telemedicine and eHealth from the University of Tromso, Norway.
Doctor of Philosophy – Health Informatics from National Yang-Ming University, Taiwan.
Job Description
This role involves conducting groundbreaking research at the forefront of healthcare, artificial intelligence, and machine learning. The successful candidate will actively contribute to projects in healthcare data analysis, predictive modeling, digital health innovation, social networking in healthcare, and health aging technologies.
Preferred Intern Educational Level
- Students who are pursuing bachelor's / Master's or Ph.D. degrees and are in their final year.
- Students pursuing Master's and Ph.D. in Healthcare Informatics, Biomedical Informatics, Computer Sciences, Health, and Life Sciences.
Skill sets or Qualities
- Students with the knowledge of Life-Science and Healthcare database.
- Students have basic experience in Python and R programming
- Students have experience in the wearable and EHR Datasets
Job Description
This role focuses on the integrative analysis of large-scale genomic and multi-omics datasets to develop predictive and diagnostic models for complex diseases. The work combines high-throughput biological data, advanced bioinformatics pipelines, and machine-learning approaches to identify robust biomarkers, uncover disease mechanisms, and enable precision-medicine solutions.
The position involves end-to-end multi-omics analytics, including data acquisition, preprocessing, integration, modeling, validation, and clinical interpretation. Data types may include genomics (WGS/WES, SNPs), transcriptomics (RNA-seq), epigenomics, proteomics, metabolomics, microbiome profiles, and associated clinical or lifestyle metadata.
Key Responsibilities
Perform quality control, preprocessing, and normalization of genomic and multi-omics datasets using established bioinformatics workflows.
Integrate heterogeneous omics layers to identify molecular signatures associated with disease onset, progression, and therapeutic response.
Develop and optimize statistical and machine-learning models (e.g., regression, random forests, gradient boosting, deep learning) for risk prediction, early diagnosis, and patient stratification.
Apply feature selection, dimensionality reduction, and pathway-level analysis to enhance model interpretability and biological relevance.
Validate predictive models using cross-validation, independent cohorts, and external datasets to ensure robustness and generalizability.
Translate analytical findings into clinically meaningful biomarkers, diagnostic scores, or decision-support tools.
Collaborate with clinicians, biologists, and data scientists to align computational models with real-world clinical needs.
Document workflows and ensure reproducibility, scalability, and regulatory readiness of analytical pipelines.
Preferred Intern Educational Level
Students currently pursuing a Bachelor’s, Master’s, or Ph.D. in Biomedical Informatics, Bioinformatics, or a related discipline, with hands-on experience in handling high-throughput bioinformatics and sequencing datasets and processing them for predictive and diagnostic analyses.
Skill sets or Qualities
Proficiency in Python, QIIME 2, and machine learning model development
Experience working with genomic and sequencing datasets