RURIMITA User
@rimitauser
AI Biomedical Engineer specializing in medical imaging and deep learning.
South KoreaWhat I'm looking for
I am an accomplished AI Research and Development Engineer with a Master's in Biomedical Engineering, specializing in AI-driven medical image analysis. My experience spans deep learning, computer vision, and biomedical signal processing, where I have successfully delivered end-to-end projects such as MRI brain tumor segmentation and ECG arrhythmia classification. I am passionate about applying AI to real-world healthcare datasets, including MRI, ECG, and ultrasound, and I am driven by a vision to develop next-generation AI-powered diagnostic and wearable platforms.
Currently, I work as a Research Associate at Sungkyunkwan University, where I have engineered advanced models like a deep U-Net for MRI segmentation and an attention-based Dual-Branch U-Net for ultrasound tumor segmentation. My academic background is complemented by a strong foundation in flexible electronics and biosensors, and I have a proven track record of cross-functional collaboration, data analysis, and project execution. I am actively seeking opportunities in Cork, Ireland, to contribute to healthcare innovation and medical imaging technology.
Experience
Work history, roles, and key accomplishments
Research Associate - AI Biomedical Engineer
Sungkyunkwan University
Jan 2022 - Present (3 years 8 months)
Trained and fine-tuned ResNet-18 on the OCT2017 dataset for multi-class retinal disease classification, achieving macro-ROC AUC of 0.979 and interpretability via Grad-CAM overlays. Engineered a deep U-Net for MRI brain tumor segmentation with IoU of 0.703 and precision of 0.853, generating clinical-grade overlays and architecture diagrams.
Graduate Studies Projects
Indian Institute of Engineering Science and Technology
Jan 2020 - Present (5 years 8 months)
Developed a 1D CNN model for ECG arrhythmia classification using MIT-BIH data, achieving 98.4% accuracy, 97.8% sensitivity, and 99.1% specificity, incorporating CAM visualization. Built a two-stage pneumonia classification and lesion detection system using Faster R-CNN with ResNet-50, achieving improved bounding-box accuracy and 0.19 validation loss.
Undergraduate Studies Projects
West Bengal University of Technology (WBUT)
Jan 2016 - Present (9 years 8 months)
Analyzed large-scale healthcare datasets and performed evaluation using AUC, F1-score, PR/ROC curves for segmentation and classification models. Produced publication-ready visual outputs, confusion matrices, and CAMs to support model interpretability.
Education
Degrees, certifications, and relevant coursework
Indian Institute of Engineering Science and Technology
Master of Technology, Biomedical Engineering
2020 - 2022
Developed a 1D CNN model for ECG arrhythmia classification using MIT-BIH data, achieving 98.4% accuracy, 97.8% sensitivity, and 99.1% specificity, incorporating CAM visualization. Built a two-stage pneumonia classification and lesion detection system using Faster R-CNN with ResNet-50, achieving improved bounding-box accuracy and 0.19 validation loss. Applied advanced data augmentation, stratified
West Bengal University of Technology (WBUT)
Bachelor of Technology, Electrical Engineering
2016 - 2020
Analyzed large-scale healthcare datasets and performed evaluation using AUC, F1-score, PR/ROC curves for segmentation and classification models. Produced publication-ready visual outputs, confusion matrices, and CAMs to support model interpretability. Authored and contributed to peer-reviewed publications and research presentations in the domain of AI in healthcare.
Tech stack
Software and tools used professionally
Availability
Location
South KoreaAuthorized to work in
Job categories
Skills
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