AI/ML Research Intern, Technology Innovation Hub (TIH), IIT Guwahati
IBM Quantum Advocate • B.Tech Computer Science & Engineering, Gautam Buddha University

My research focuses on computational robustness, reliability, and uncertainty-aware modeling across non-stationary dynamical systems, noisy physical substrates, and quantitative risk environments:

• Open Quantum Systems & Quantum Machine Learning: Numerical simulation of non-Markovian dissipation, Lindblad master equations for extreme financial tail-risk, and empirical noise degradation in parameterized quantum circuits (QSVC vs VQC) under hardware-calibrated NISQ noise models.
• Quantitative Risk & Financial Time-Series: Institutional realized volatility forecasting (HAR, Sector-HAR, LightGBM, Diebold-Mariano hypothesis testing) and high-throughput sub-millisecond derivatives liquidity screening.
• Deep Learning & Edge Prognostics: Dilated causal temporal convolutional networks (TCN) and recurrent architectures for complex degradation modeling (battery SOH/RUL) with low-latency INT8/FP16 edge deployment via ONNX Runtime.
• Post-Quantum Cryptography & Security Verification: Algorithmic discovery of cryptographic assets, Mosca's theorem quantum risk assessment, and CycloneDX Cryptographic Bill of Materials (CBOM) generation.

• Scientific Computing & ML: Python, C++, PyTorch, Scikit-learn, LightGBM, NumPy, Pandas, SciPy, QuTiP, OpenCV.
• Quantum Development: Qiskit (1.x/2.x Primitives), Aer Noise Simulators, Parameterized Feature Maps (ZZFeatureMap, PauliFeatureMap), QTerm.
• Quantitative Finance & Data: DuckDB, FastAPI, Financial Time-Series Modeling (HAR, GARCH), Monte Carlo, Options Pricing (Black-Scholes).
• Engineering & Deployment: Docker, Git/GitHub, ONNX Runtime, Linux/Bash, Streamlit, PyTest.

• "The heaviest regrets are the words left unsaid."
• Tried something beyond my comfort zone.
• Uss din kyu nhi gaye shots
• No man left behind

_{Open to research collaborations, visiting researcher roles, and quantitative engineering opportunities.}