AI & Time-Series Analysis Researcher | Digital Twin & Computational Engineering Specialist

Advancing the intersection of time-series forecasting, brain-computer interfaces, AI-powered finance, and physics-informed machine learning.

Building intelligent systems that learn from temporal patterns while maintaining rigorous physical consistency through digital twins and computational simulation.

Active interest in building expertise across:

• ⏱️ Time-Series Analysis & Forecasting - LSTMs, Transformers, Physics-Informed Neural Networks
• 🧠 Brain-Computer Interface (BCI) - Neural signal processing & real-time AI applications
• 💰 AI-Powered Finance - Predictive modeling, risk analysis, market dynamics
• 🔄 Digital Twins & Simulation - Real-time system monitoring and predictive maintenance
• 🤖 Large Language Models (LLMs) - Prompt engineering and AI-assisted research workflows

Research Associate at Texas State University's Ingram School of Engineering, specializing in:

• Computational mechanics and multiphysics simulations with scientific rigor
• Hybrid modeling approaches combining classical physics with machine learning
• Physics-Informed Neural Networks (PINNs) for engineering applications
• LLM integration in research workflows and knowledge synthesis

With 5+ years of research experience spanning finite element analysis, high-performance computing, and intelligent systems, I'm building an integrated skill set that connects temporal dynamics, neural networks, and physical simulation—positioning myself at the intersection of modern AI and rigorous engineering science.

• LSTM / GRU Networks - Sequential pattern recognition
• Transformer Architectures - Attention-based temporal modeling
• Physics-Informed Neural Networks (PINNs) - Constraint-based learning
• Temporal Forecasting - Univariate & multivariate prediction

• PyTorch - Primary framework for research & prototyping
• TensorFlow/Keras - Production-grade implementations
• scikit-learn - Classical ML & preprocessing

• Python - NumPy, SciPy, Pandas for numerical computing
• Finite Element Analysis - ABAQUS/CAE, code-based FEA
• High-Performance Computing - MPI, GPU acceleration

• Multiphysics Modeling - Heat transfer, structural mechanics, fluid dynamics
• Digital Twin Development - Real-time system replication
• Scientific AI - Embedding physics constraints in neural networks

• Prompt Engineering - Leveraging LLMs for research assistance
• Git & Version Control - Reproducible research practices
• Documentation - Clear scientific communication

Building neural decoders and signal processing pipelines for brain-computer interfaces. Interest in real-time temporal dynamics and interpretable models for neurophysiological data.

Developing predictive models for financial time-series using advanced deep learning. Focus on risk modeling, market dynamics, and robust forecasting under uncertainty.

Creating neural networks that respect physical laws and constraints. Bridging classical simulation with modern AI for more efficient and generalizable models.

Leveraging real-time simulations and AI for system monitoring, anomaly detection, and predictive maintenance across engineering domains.

• 🚀 Physics-Informed Neural Networks (PINNs) - Multi-domain applications in heat transfer, structural analysis, and dynamics
• 📈 Time-Series Forecasting - NASA turbofan engine data, financial markets, signal prediction
• 🧠 BCI Signal Processing - Neural decoding with deep learning
• 💻 AI-Assisted Research - LLM workflows for literature synthesis and documentation
• 🎯 Portfolio Building - Showcasing end-to-end ML projects with scientific rigor

Education:

• Ph.D. in Mechanical Engineering (Computational) | Virginia Tech
• Strong foundation in mechanics, materials science, and computational methods

Professional Experience:

• Lecturer, Texas State University (Current)
• Oak Ridge National Laboratory
• University of Alabama

I'm actively building my portfolio in time-series analysis and AI applications. Open to:

• 🤝 Research collaborations (PINNs, BCI, AI-finance)
• 💬 Technical discussions on deep learning & simulation
• 📚 Contributing to open-source ML/scientific computing projects

Let's build something impactful at the intersection of AI and physics!

Last updated: November 2025

Feel free to reach out if you'd like to collaborate, discuss research, or connect professionally.