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rmanicav/README.md

Dr. Rajesh Manicavasagam

Research Software Engineer specializing in scientific computing, high-performance computing, and applied machine learning.

PhD in Computer Engineering with 14+ years of experience developing scalable software systems and research software for complex data-driven environments.

My work focuses on building reproducible experiments, HPC-enabled simulations, and applied ML systems used in scientific and cyber-physical domains.

Research & Technical Focus

  • High Performance Computing (MPI, CUDA, SLURM)
  • Research Software Engineering
  • Applied Machine Learning
  • Scientific Computing
  • Cyber-Physical Systems & Smart Grid Analytics
  • Time-Series Forecasting & Anomaly Detection
  • Reproducible Computational Experiments

Selected Research Projects

HPC Flood Simulation

Parallel flood propagation simulation implemented in C++ with MPI for distributed computing environments.

Repository
https://github.com/rmanicav/hpc-flood-simulation-mpi

Smart Grid Demand Response Forecasting

Machine learning models for forecasting demand response behavior in cyber-physical energy systems.

Technologies
Python, NumPy, Pandas, ML models

Repository
https://github.com/rmanicav/smart-grid-demand-response-forecasting

Smart Grid Intrusion Detection

Machine learning techniques for detecting anomalous behavior in industrial control systems.

Technologies
Python, scikit-learn, PyTorch

Repository
https://github.com/rmanicav/smart-grid-intrusion-detection-ml

Publications

Selected peer-reviewed publications:

• Relating Network Behavior to Demand Response during DDoS Attack in the Smart Grid
Future Technologies Conference (FTC), 2023

• Testbed for Evaluating Smart Grid Behavior in Demand Response Scenarios
ICUMT 2022

• Drug Repurposing for Rare Orphan Diseases Using Machine Learning Techniques
FLAIRS Conference, 2022

Google Scholar
https://scholar.google.com/citations?user=2XswkUcAAAAJ

Technical Skills

Programming
Python, C++, SQL, Bash, C#

Machine Learning
scikit-learn, PyTorch, TensorFlow

Scientific Computing
NumPy, Pandas

Parallel Computing
MPI, CUDA, SLURM

Systems
Linux, Docker, Kubernetes

Professional Background

PhD in Computer Engineering (Applied Machine Learning)

14+ years of software engineering experience building distributed and data-intensive systems.

Experience collaborating with interdisciplinary research teams to translate research ideas into reliable software systems.

Contact

GitHub
https://github.com/rmanicav

Google Scholar
https://scholar.google.com/citations?user=2XswkUcAAAAJ

Pinned Loading

  1. ml-deep-learning-genai-portfolio ml-deep-learning-genai-portfolio Public

    Experimental machine learning and deep learning models for research prototyping.

    Jupyter Notebook 2

  2. rmanicav rmanicav Public

    Profile

    2

  3. smart-grid-demand-response-experiments smart-grid-demand-response-experiments Public

    Machine learning and time-series forecasting for smart grid demand response analysis.

    Python 2

  4. Smart-Grid-Intrusion-Detection-using-Machine-Learning Smart-Grid-Intrusion-Detection-using-Machine-Learning Public

    Machine learning-based anomaly detection for smart grid and industrial control systems.

    Python 3

  5. drug-repurposing-ml drug-repurposing-ml Public

    Machine learning approaches for drug repurposing in rare disease research.

    Python 1

  6. hpc-flood-simulation-mpi hpc-flood-simulation-mpi Public

    Parallel 2D flood simulation implemented in C++ using MPI for high-performance scientific computing.

    C++ 1