Machine Learning Engineer | Physics-Informed ML, Forecasting, Anomaly Detection, Retrieval Systems
Building machine learning systems for science, simulation and reasoning.
- Sole-author paper on bounded latent degradation dynamics submitted to Engineering Applications of Artificial Intelligence and under review
- Building real-time anomaly detection and document intelligence systems at NTT DATA's AI Center of Excellence
- Research interests: physics-informed neural networks, time-series forecasting, anomaly detection, knowledge systems
Learning Bounded Latent Degradation Dynamics for Stable Rollout and Remaining Useful Life Prediction Sole author. Submitted to Engineering Applications of Artificial Intelligence (under review). Bounded/unbounded latent decoupling for degradation forecasting. 53% improvement over best baseline on Lorenz-63 chaotic system, 5.7% blowup rate vs 100% for all baselines. Evaluated on 5 real datasets: NASA C-MAPSS turbofan, PHM milling, IMS bearings, NASA batteries, Beijing air quality. SSRN Preprint
Neural Simulation of Quantum Interactions in a Confined System ResearchGate preprint. Physics-informed neural network embedding Schrodinger's equation directly into the network architecture. 99.9% agreement with analytical benchmarks. ResearchGate
Logos-SIE: Synthetic Information Ecosystem for Truth Discovery and Retrieval Co-authored technical whitepaper, TwinSimLabs. Large-scale synthetic benchmark modeling the lifecycle of information formation for controlled experimentation in retrieval and trust evaluation. GitHub
| Project | What it does |
|---|---|
| Bounded Latent Degradation Dynamics | Degradation modeling and RUL prediction across 5 real-world datasets (EAAI submission, SSRN 7180558) |
| Latent Decoupling Framework | Core bounded/unbounded latent decoupling method with Lorenz-63 chaotic system benchmark |
| TinyEarth | Controlled benchmark comparing S4D, Transformer, ConvLSTM, and Mamba for Earth-surface forecasting on EarthNet2021 (958 test sequences) |
| Quantum-Simulator | PINN for quantum particle simulation via embedded Schrodinger equation |
| Logos-SIE | Synthetic information ecosystem benchmark for truth discovery and retrieval |
| Softmatter-State-Detection | Physics-informed ML for soft matter phase classification from molecular dynamics simulations |
| Linux-Kernel-Driver-Evaluation-System | Kernel module + Python evaluation pipeline (C, systems programming) |
| Domain | Tools |
|---|---|
| ML / DL | PyTorch, TensorFlow, JAX, scikit-learn |
| GPU / HPC | CUDA, RAPIDS (cuML, CuPy, cuDF) |
| Scientific | NumPy, SciPy, networkx, LAMMPS |
| Vision | OpenCV, MediaPipe, YOLO |
| Languages | Python, Go, C/C++, R |
| Infrastructure | Docker, Azure, Databricks, Linux |
- Associate Engineer, NTT DATA AI Center of Excellence (promoted from intern)
- BE in Computer Science and Business, Thapar Institute of Engineering and Technology (2022--2026)