Synthetic data generation for tabular data
Synthetic Data Generation for mixed-type, multivariate time series.
Easy-to-use utilities to build privacy-preserving AI.
A Regression approach for the automated detection of the Parkinson's Disease based on an Ensemble of Neural Networks.
End-to-end lane mapping and refinement system for autonomous vehicles, transforming noisy recorded paths into structured graph-based networks with spline smoothing, junction logic, and bidirectional navigation. Designed for real-world deployment with integrated visualization, editing, and analysis workflows.
An Agentic AI Orchestrator for Software-Defined Vehicles (SDVs) that fuses physiological data and environmental context to deliver safe, explainable, and proactive driver interventions.
Mini Project about synthetic data generation by implementing CTGAN algorithm on tabular data
Software Defined Vehicle(SDV) service orchestration with intelligent bin-packing scheduler. Built with Python (Flask) and React.
Industrial Practicum Project
Synthetic data pipeline for tabular insurance data. Scales 1,337 rows to 50,000 using TVAE, CTGAN, TabDDPM with an 11-section QC suite.
• Developed an autonomous driving system (QCar2) on NVIDIA Jetson integrating LiDAR, CSI cameras, and Intel RealSense depth sensor for real-time perception • Trained and deployed RT-DETR model for traffic sign detection achieving 92.6% mAP and 24 FPS, enabling future integration into decision-making pipelin
A processing of a simplified video game dataset using k-nearest neighbors, and using Synthetic Data Vault and Regression/Classification models to verify synthetic model accuracy
SDV Landscape core repository
In this repository, I tried to investigate the utility of synthetic data generated by DataSynthesizer and Synthetic Data Vault in machine learning tasks. I applied the Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Naive Bayes algorithms to the synthetic data and made a comparison.
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