• Reinforcement Learning • Spatial Data Mining

 🎓 PhD IIIT-D | 🚀 Author: “Reinforcement Learning Explained”
🔬 University of South Dakota AI Research Lab | 📜 Google Scholar | 💼 LinkedIn

• Currently a Postdoctoral Researcher at the University of South Dakota (USD).
• Research interests: Reinforcement Learning (RL), Embodied AI, World Models, Machine Learning in Healthcare, and Pattern Mining.
• Author of the upcoming textbook:
  Reinforcement Learning Fundamentals: From Theory to Practice (with companion code repo).
• Passionate about teaching, mentoring, and community building in AI.

• ✍️ Writing a comprehensive RL textbook (LaTeX source + reproducible code).
• 🔎 Exploring world models and sample-efficient embodied RL.
• 📊 Working on AI for biomedical computation with collaborators at USD.
• 🎤 Organizing academic events like the AI Symposium @ USD.

• 📚 Reinforcement-Learning-Explained-Code – Companion code for my RL textbook.
• 📖 AI-Symposium – Website for USD AI Symposium.
• 📝 Research paper repositories and proposals (in progress).

• Languages: Python, LaTeX, SQL
• Libraries: PyTorch, TensorFlow, scikit-learn
• Tools: Overleaf, Git, Power Automate, HPC (Lawrence)

• Advanced world model architectures (DreamerV3, AdaWorld).
• Deep RL alignment techniques for LLMs.

• 🌐 Website: AI Research Lab @ USD
• 💼 LinkedIn: linkedin.com/in/srikanth-baride
• 🎓 Google Scholar: Scholar Profile

I enjoy teaching meditation 🧘 alongside AI research — helping people cultivate both clarity of mind and clarity of models.

⭐️ Check out my pinned repositories for active work!