Skip to content
View rmanicav's full-sized avatar
2 followers · 1 following
  • Leipzig, Germany

Block or report rmanicav

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
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