2025
Exechon Algorithm
Python
SQL
ETL
API Integration
Optimization
Sentiment Analysis
small-team personal project
2024
Learning-assisted Drifting for F1Tenth Autonomous Race Cars
Python
Matlab
ROS
Optimization
Machine Learning
Control Systems
Associated with TU/e (MSc Thesis)
Developed an advanced control framework for autonomous drifting using small-scale F1Tenth vehicles, integrating automated gain tuning and Gaussian Process-based model augmentation to enhance control accuracy and path tracking in complex trajectories.
2023
Depth-Aware Video Panoptic Segmentation & Monocular Depth Estimation
Python
Deep Learning
Neural Networks
High Performance Computing
Associated with TU/e
2023
Perception-enabled Pure Pursuit for Small Scale Racing Cars
Python
Deep Learning
Object Detection
ROS
Controller
Reinforcement Learning
Associated with TU/e
Developed an autonomous agent for small-scale racing cars to navigate unknown racetracks using object detection for cones and a 2D environmental map. Implemented and modified a geometric controller for improved adaptability and speed while conducting a theoretical analysis of reinforcement learning for decision-making.
2023
High-Performance Control Design for a Fourth-Order Rotational Motion Setup
Matlab
Simulink
Control Theory
Frequency Analysis
Associated with TU/e
Designed and implemented a high-performance control system for a fourth-order rotational motion setup, significantly improving trajectory tracking accuracy using advanced feedback and feedforward techniques.
2023
Policy Learning for an Unbalanced Disc
Python
Model Identification
Probabilistic Machine Learning
Reinforcement Learning
Associated with TU/e
Developed a control policy for an unbalanced disc pendulum system using Gaussian Processes, Neural Networks, and Reinforcement Learning, enabling successful swing-up and stabilization while extending control to multiple target positions.
2023
Semantic Segmentation using a UNet-based Architecture
Python
Deep Learning
Generative AI
Encoders
Associated with TU/e
Developed a robust semantic segmentation model for urban street scenes using a U-Net-based architecture, addressing challenges in class imbalance, image quality degradation, and generalization. Enhanced performance through data augmentation, external datasets, transfer learning, and adaptive class weighting, demonstrating improved segmentation on the Cityscapes dataset.
More projects
For additional projects, check out my LinkedIn profile and coding repositories below.