AI Engineer & Researcher | Agents • Diffusion Models • Energy-Based Models • LLMs • Computer Vision
I hold an M.Sc. in Artificial Intelligence with research and applied experience in Machine Learning, Computer Vision, and Natural Language Processing (NLP).
My work spans Large Language Models (LLMs), Agents, Diffusion Models, and Energy-Based Models (EBMs), with a strong interest in how these systems can generalize, reason, and generate across diverse domains.
I am particularly motivated by building robust multimodal AI systems that integrate vision, language, and decision-making for impactful applications in healthcare, automation, and beyond.
Previously, I have developed AI models for image recognition, object detection, video analysis, and transformer-based NLP.
Currently, my focus is on AI agents, diffusion models, and energy-based models, combining reasoning, planning, and generative modeling to design more generalizable and capable AI systems.
My research focuses on designing generalizable AI systems with capabilities in reasoning, planning, and multimodal generation.
Broadly, I am interested in:
- AI Agents – autonomous, adaptive decision-making systems
- Generative AI – diffusion models, energy-based models (EBMs), LLMs, and multimodal generation
- Cognitive AI – studying reasoning mechanisms in LLMs and agent architectures
- Computer Vision – perception-driven AI for real-world applications
- Optimization & Scalability – making AI systems efficient, robust, and production-ready
- AI Ethics – building responsible, interpretable, and transparent AI systems
- Programming: Python, C/C++, MATLAB
- ML/AI Frameworks: PyTorch, TensorFlow, Keras
- Libraries & Tools: Hugging Face Transformers, scikit-learn, OpenCV, spaCy, NLTK
- Generative AI: Diffusion Models, Energy-Based Models, Stable Diffusion, LangChain, Agentic Systems
- Data Science: Pandas, NumPy, data preprocessing & visualization
- Deployment & Engineering: Git, GitHub, Model Optimization & Scalability, production-ready pipelines
