Syahril Siregar, Ph.D.
Assistant Professor, Division of Medical Physics and Biophysics, Faculty of Mathematics and Natural Sciences, Universitas IndonesiaSyahril Siregar, Ph.D.
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Welcome to SIRE Lab (Simulation • Intelligence • Research • Exploration), a computational biomedical physics laboratory within the Medical Physics and Biophysics Group, Department of Physics, Universitas Indonesia. Our research brings together computational physics, artificial intelligence, mathematical modeling, and biomedical science to better understand complex biological systems and develop innovative technologies for healthcare.
We develop computational methods that combine mechanistic modeling, scientific computing, and data-driven intelligence to analyze biomedical images, spectroscopic data, and other biomedical measurements. Our work spans medical imaging, biomedical spectroscopy, mathematical and physiological modeling, computational simulations, machine learning, explainable AI, and digital health. By integrating physics, mathematics, computation, and biomedical science, we strive to create interpretable, reliable, and clinically relevant solutions for disease diagnosis, prognosis, personalized medicine, and healthcare innovation.
At SIRE Lab, we believe that the future of biomedical research lies in the synergy between simulation, intelligent computation, and quantitative science—transforming complex biomedical data into meaningful scientific discoveries and real-world clinical impact.
Research Focus:
Within our broad research vision, we investigate a diverse range of topics, including:
Computational Biomedical Physics: Developing computational methods to investigate physical phenomena in biological systems, medical imaging, and healthcare technologies through quantitative modeling, simulation, and scientific computing.
Artificial Intelligence for Healthcare: Developing machine learning, deep learning, explainable AI, and multimodal learning algorithms for disease diagnosis, prognosis, precision medicine, and clinical decision support.
Mathematical & Physiological Modeling: Constructing mathematical models of biological and physiological systems, including glucose-insulin regulation, hormonal dynamics, disease progression, pharmacokinetics, and other complex biological processes.
Medical Imaging & Computer Vision: Developing computational approaches for medical image reconstruction, segmentation, feature extraction, radiomics, image classification, and quantitative image analysis across ultrasound, MRI, CT, PET, and SPECT imaging.
Biomedical Spectroscopy & Chemometrics: Applying FTIR, Raman spectroscopy, chemometric analysis, and machine learning to develop non-invasive diagnostic methods, biomarker discovery, and quantitative biochemical analysis.
Biomedical Signal Processing: Analyzing physiological signals and biomedical data using advanced signal processing, statistical learning, and computational intelligence to extract clinically relevant information.
Physics-Informed Artificial Intelligence: Integrating physical principles, mathematical models, and machine learning to develop interpretable, robust, and data-efficient AI models for biomedical applications.
Scientific Computing & Numerical Simulation: Developing numerical algorithms, optimization techniques, parameter estimation methods, and computational simulations for solving complex biomedical and medical physics problems.
Computational Modeling for Digital Health: Building predictive computational models and digital representations of physiological systems to support personalized medicine, digital twins, and intelligent healthcare technologies.
Scientific Software & Open-Source Tool Development: Developing reproducible software, computational pipelines, and open-source scientific tools for biomedical image analysis, spectroscopy, mathematical modeling, and artificial intelligence research.
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Departemen Fisika FMIPA UI, Pondok Cina, Depok City, West Java, Indonesia
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