Data Science student with hands-on experience in projects involving Python, Machine & Deep Learning, LLMs Fine-Tuning, Retrieval-Augmented Generation (RAGs), and Agentic AI, alongside academic research, which I'm very passionate about. I aim to deepen my expertise in LLMs research, particularly in the unexplored and ambiguous areas that are shaping the future of the field. I'm committed to expand my knowledge and education through theoretical and hands-on research in new and uncharted areas.
Languages, Packages & Tools: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, Matplotlib, Seaborn), C, C#, SQL, Jupyter, VS Code, Unity Engine, Power BI
Machine & Deep Learning: Data Preprocessing, Regression, Classification, Ensemble Methods, Neural Networks, CNNs, RNNs, Dimensionality Reduction (eg, PCA), NLP (Embeddings, Transformers, LLMs), Model Evaluation & Hyperparameter Tuning
LLMs & Agentic AI: LangChain, smolagent, LiamIndex, LangGraph, Fine-Tuning, Retrieval-Augmented Generation (RAGs)
Soft Skills: Analytical Thinking, Problem Solving, Fast Learner, Teamwork & Adaptability, Communication Skills, Attention to Details, Work Under Pressure
Awarded 3rd place: in a national-level hackathon on Agentic AI and Retrieval-Augmented Generation (RAGs), competing against teams from universities across the country.
Completed a Hugging Face course on AI Agents: gaining practical experience in building and deploying intelligent agent systems.