Hey, I'm
AI & Medical Image Analysis
I build deep learning systems that turn medical and real-world images into actionable insight with a growing focus on ophthalmic disease detection, computer vision, and applied machine learning.
Who I am
I'm a Computer Engineering undergraduate with a strong interest in artificial intelligence, medical image analysis, and computer vision. I've built full working systems from a web-based attendance platform to deep learning models for crop disease and retinal disease detection — and I'm continuing to specialize in AI for healthcare, where early, accurate detection can genuinely change outcomes.
Data science is a tool I use to get there. What I care about most is building models that see and reason the way a specialist would, and shipping them as systems people can actually use.
Where I've worked
Applied research and internship experience alongside my degree.
AI Research Intern
May 2026 – August 2026- Trained and evaluated EfficientNet (B1–B4) models for RFMiD multi-disease retinal classification, ARMD screening, and Papilledema/Pseudopapilledema detection under NAAMII's TOGAI (Transforming Global Health with AI) research group
- Performed manual retinal-image annotation (eye laterality, macular and optic-disc landmarks) and organized datasets at the patient level for clinical-grade training
- Contributed trained models to the AI Screening Suite, a web-based platform for ophthalmology, syndrome, and otoscopy screening, and presented weekly progress to supervisor Mahesh Shakya
Research & Cybersecurity Awareness Program
April 2026 – September 2026- []
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AI-Powered Crop Disease Detection
A deep learning system built to identify crop disease from leaf images, aimed at supporting early intervention for farmers.
Crop Disease Detection System
Deep Learning · CNN · Computer Vision
A collaborative deep learning project that classifies crop leaf images to detect disease, built and trained with a team on Kaggle and versioned on GitHub. Designed as a step toward accessible, image-based diagnostic tools for agriculture.
Ophthalmology & Retinal Disease AI
Deep learning models for detecting and classifying retinal disease from fundus images the specific direction I'm building my medical imaging focus around.
RFMiD Retinal Disease Classification
EfficientNet · Fundus Imaging
Trained an EfficientNet-based classifier on the RFMiD dataset to detect retinal disease from fundus images.
Retinal Multiclass Disease Detection
Multiclass Classification · CNN
Extended retinal disease detection to a multiclass setting, distinguishing between multiple retinal conditions from a single model.
Papilledema Detection
Deep Learning · Medical Imaging
A classification model focused on detecting papilledema from retinal images, an early indicator that can signal raised intracranial pressure.
Mini Projects
Smaller projects that helped build my foundation in computer vision, data analysis, and full-stack development.
Pothole Detection System
YOLO · Computer Vision
Real-time pothole detection from road images using a custom-tuned YOLO model, deployed with Flask and optimized for edge devices.
Intel Image Classification
TensorFlow · CNN
Classified landscape images into 6 categories with 85% accuracy, using data augmentation and transfer learning.
Fruits 360 Classification
CNN · TensorFlow
92% accuracy across 131 fruit categories using an efficient CNN architecture with aggressive data augmentation.
House Price Prediction (Nepal)
Scikit-learn · Pandas
Linear Regression and Random Forest models with full EDA; feature engineering improved R² by 15%.
Student Performance Analysis
ML · Feature Selection
Predicted academic outcomes with multiple ML algorithms, using SHAP values for interpretability.
Web Attendance System
Flask · MySQL
Full-stack attendance tracking system with an admin dashboard, live clock-in/out, and report generation.
Academic Background
Bachelor of Computer Engineering Graduated
2022 – 2026- Four-year program covering computing systems, programming, data analysis, and image processing
- Built web-based applications and data-driven projects, and implemented algorithms for real-world problems
- Collaborated on team projects, strengthening research and problem-solving skills
Higher Secondary Education (+2 Science)
2020 – 2022- Coursework in mathematics, physics, and chemistry, building a strong analytical foundation
- Developed an early interest in technology, coding, and data analysis