I'm passionate about pioneering advancements in AI, with extensive experience in machine learning, deep learning, and generative AI systems. My journey spans from implementing traditional ML models to designing sophisticated AI workflows for automation and generative tasks.
| Project | Description | Tech Stack |
|---|---|---|
| PickSmart | AI Assistant with Agentic Workflow | LangChain, LangGraph |
| DressMe.AI | Multimodal Agent with Image Generation | VLMs, Diffusion Models, Feedback Mechanism |
| SEAD-agent | AI Research Assistant with Tool Calling | VLMs, Function Calling, RAG |
| vlm-ollama | VLMs Inference on Local Deployment | VLMs, Ollama |
| SageMaker LLM Deployment | LLM Deployment on AWS | AWS SageMaker, LLMOps |
| Project | Description | Tech Stack |
|---|---|---|
| VLM-LoRA | Fine-tuning Vision-Language Models | LoRA, Parameter-Efficient Training |
| Transformer Machine Translation | Neural Machine Translation from Scratch | Transformer, Attention Mechanisms |
| Post-Training Quantization | Post-Training 8-Bit Quantization for LLMs | Symmetric and Asymmetric Quantization, BitsAndBytes |
| Project | Description | Tech Stack |
|---|---|---|
| SeGAN | Financial Forecasting using GANs | RNN, GANs, Time Series |
| DenseNet201 Classifier | Satellite Image Classification | CNN, Transfer Learning |
| GradCAM | X-ray Image Localization | Computer Vision, Explainable AI |
| Multi-Regressor MLP | 2D Landing Control System | Neural Networks, Control Systems |
| MIMO Regressor | Biochemical Production Optimization | Random Forest, XGBoost, SVM |
Master of Science in Artificial Intelligence (Distinction)
University of Essex
Bachelor of Science in Computer Science
Mahidol University
- Vision-Language Models: Developing multimodal models to integrate visual perception with natural language understanding for complex reasoning tasks
- Joint-Embedding Predictive Architecture (JEPA): Exploring self-supervised learning frameworks that learn world models through predictive coding in abstract representation spaces
- Energy-based Models: Designing probabilistic models that capture data distributions through energy functions
- Few-Shot Learning: Creating adaptive systems that can generalize from minimal training examples using meta-learning and transfer learning techniques
- AI Agents & Automation: Building autonomous intelligent systems capable of complex decision-making and real-world problem solving through reinforcement learning and planning algorithms