📐 Mathematics for AI
Linear algebra, probability theory, statistics, PCA, Bayesian inference, optimization, and information theory — the backbone of AI.
AI Research
Foundational and applied artificial intelligence — mathematics, machine learning, deep learning, large language models, autonomous agents, and future intelligence architectures.
Core AI Research Areas
Built on strong mathematical foundations and real-world experimentation
🔥 Deep Learning & PyTorch
Neural networks, CNNs, RNNs, transformers, training dynamics, optimization strategies, and hands-on PyTorch experimentation.
🧠 Large Language Models
Transformers, attention, fine-tuning, RAG, prompt engineering, evaluation, and real-world LLM applications.
🤖 Autonomous AI Agents
Tool-using agents, planning, memory systems, self-reflection loops, multi-agent coordination, and decision-making architectures.
🚀 Future AI Directions
Neuro-symbolic AI, biologically inspired intelligence, AI + genetics, reasoning systems, and long-term artificial general intelligence research.