Data Scientist building practical machine learning solutions that solve real-world problems
I'm a passionate data scientist with a strong foundation in machine learning, deep learning, and computational research. My journey began with a fascination for understanding complex patterns in data and has evolved into developing practical AI solutions that bridge the gap between cutting-edge research and real-world applications.
With extensive experience in bioinformatics, financial analytics, and business intelligence, I specialize in transforming raw data into actionable insights. My work spans from developing custom neural network architectures for medical research to building enterprise-grade machine learning platforms that democratize AI accessibility.
Custom CNN architecture for predicting circRNA-disease associations with Gaussian Blur preprocessing
Genetic algorithm approach to identify DNA characteristics leading to P-53 cancerous mutations
Open-source desktop application for training and running large language models
High-performance PyTorch inference framework with enterprise autoscaling capabilities
Production-ready automated ML platform with ASHT optimization and web interface
Advanced Self-Organizing Map implementation with multiple initialization methods and clustering evaluation metrics
AI-powered fitness companion with pose detection and personalized recommendations
Educational platform fostering lifelong learning and research community engagement
Psychology-focused community platform for mental health awareness in Indonesia
Multi-country GDP pattern analysis using Self-Organizing Map clustering techniques
Lead-lag relationship analysis between equity and cryptocurrency markets during business cycles
