🙌I'm open to any types of collaborations, be it Research or projects

I am a versatile AI enthusiast driven by the challenge of building autonomous intelligent systems that are mathematically robust, computationally efficient, and ethically aligned. My work lies at the intersection of deep/reinforcement learning research and scalable software engineering/machine learning systems. I don't just build models; I architect the environments they live in and the rigorous frameworks that evaluate their success.

▶Modeling & Intelligence
◆Reinforcement Learning: Proficient in PPO, DQN, and Contextual Bandits for autonomous decision-making.
◆Neural Architectures: Deep experience with Transformer/Attention mechanisms and custom deep learning models.
◆Classical ML: Experience in Supervised/Unsupervised learning, Feature Engineering, and Gradient Descent optimization.

▶Engineering & Infrastructure
◆Frameworks: PyTorch, TensorFlow, Stable-Baselines3, Scikit-learn, LightGBM, and XGBoost.
◆Data Systems: Advanced Python, SQL, Pandas, and NumPy for high-performance data manipulation.
◆Deployment & Ops: FastAPI for model serving, AWS (EC2, S3), and experiment tracking with Weights & Biases.

▶Research & Trustworthy AI
◆Algorithmic Fairness & Trustworthy AI: Investigating the socio-technical implications of AI systems, with specific research into identifying "invisible bias" and structural inequalities in high-stakes automated decision-making.
◆Generalization & Evaluation Methodology: Focused on the rigorous quantification of model performance through out-of-distribution (OOD) testing, drift detection, and the mitigation of optimism bias in benchmark evaluation.
◆Computational Modeling & System Dynamics: Exploring the optimization of complex, multi-objective systems through a combination of statistical modeling and discrete-event simulation to improve real-world operational efficiency.
◆Interpretability & Explainable AI (XAI): Committed to decoding "black box" architectures using SHAP and LIME to ensure transparency and accountability in predictive modeling.


🌐 Beyond the Code
I believe that the best AI is built with a deep understanding of its real-world impact. My project experiences enable me to approach engineering problems with scientific rigor, ensuring that models are not only accurate but also generalizable, reproducible, and fair.
🎓 Academic: B.Tech in CS (AI & ML)'2027 with a 9.15 CGPA.
🏆 Honors: Recognized on the Vice Chancellor's List and 3× Academic Excellence Award winner.