Skip to content
View dhananjayjm's full-sized avatar
2 followers · 0 following

Block or report dhananjayjm

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 is supported. This note will only be visible to you.
Report abuse

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

Report abuse
dhananjayjm/README.md

Hi, I'm Dhananjay Mandalkar

Animated introduction

Scientific Software | Medical Imaging | Proton Therapy | Computer Vision | Machine Learning

I combine physics, mathematics, scientific computing, and software development to work on scientific, engineering, and healthcare-related problems.

Physics and Scientific Computing Proton Therapy in Oncology Medical Imaging OpenCV PyTorch

About Me

I have a strong academic background in Physics and experience in scientific programming, mathematical modelling, data analysis, and software development.

My interests lie at the intersection of scientific computing, medical physics, medical imaging, artificial intelligence, and software engineering.

I am particularly interested in computational methods for proton therapy, radiation oncology, medical image analysis, dose calculation, and research software development.

Scientific and Medical Interests

  • Scientific computing
  • Computational physics
  • Medical physics
  • Medical imaging
  • Proton therapy
  • Radiation oncology
  • Dosimetry
  • Monte Carlo simulation

Software and AI Interests

  • Python and C++ development
  • Machine learning
  • Deep learning with PyTorch
  • Computer vision with OpenCV
  • Scientific data analysis
  • React frontend development
  • Research software engineering
  • Linux and Docker

Proton Therapy in Oncology

I am developing my knowledge of proton therapy and its computational applications in oncology.

Proton therapy uses charged particles to deliver radiation to tumours. Its characteristic depth-dose behaviour makes it possible to concentrate dose within the treatment target while reducing unnecessary exposure to surrounding healthy tissue.

My interests include proton interactions with matter, treatment simulation, dose calculation, medical imaging integration, dosimetry, treatment planning, and computational methods for radiation oncology.

Physics and Treatment Concepts

  • Proton interactions with matter
  • Bragg peak and depth-dose behaviour
  • Energy loss and dose deposition
  • Treatment planning fundamentals
  • Absolute and relative dosimetry
  • Target volumes and organs at risk
  • Range and setup uncertainties
  • Radiation protection

Computational Applications

  • TOPAS Monte Carlo simulations
  • Geant4 fundamentals
  • CT-based patient geometries
  • DICOM radiotherapy data
  • Proton dose calculation
  • Dose distribution analysis
  • Deep learning for dose prediction
  • Scientific visualisation

Medical Imaging and OpenCV

I am developing practical skills in medical image processing, image analysis, and computer vision.

Medical Imaging

  • CT image analysis
  • DICOM data handling
  • 3D Slicer
  • Medical image visualisation
  • Image preprocessing
  • Radiotherapy imaging data
  • Segmentation fundamentals
  • Hounsfield unit analysis

OpenCV and Computer Vision

  • Image reading and manipulation
  • Filtering and noise reduction
  • Contrast enhancement
  • Thresholding
  • Edge detection
  • Contour analysis
  • Feature extraction
  • Geometric transformations

My goal is to combine classical image-processing methods with machine learning and deep learning techniques for medical image analysis.

Current Focus

Currently Working On

  • Deep learning with PyTorch
  • Medical image analysis
  • OpenCV image-processing applications
  • Scientific Python projects
  • TOPAS proton therapy simulations
  • DICOM data analysis

Currently Learning

  • Geant4 application development
  • Deep learning for medical imaging
  • Proton therapy dose calculation
  • Radiotherapy treatment planning
  • Docker for scientific applications
  • Research software engineering

Physics Background

My foundation in physics helps me approach computational problems through mathematical modelling, numerical analysis, scientific reasoning, and careful interpretation of results.

Physics and Mathematics

  • Computational physics
  • Applied mathematics
  • Numerical methods
  • Mathematical modelling
  • Linear algebra
  • Differential equations

Scientific Practice

  • Scientific programming
  • Simulation methods
  • Data analysis
  • Data visualisation
  • Physics-based problem solving
  • Technical documentation

My previous teaching experience has also strengthened my communication, mentoring, and technical explanation skills.

Technical Skills

Scientific Computing and AI

  • Python
  • C++
  • MATLAB
  • PyTorch
  • OpenCV
  • scikit-learn
  • NumPy
  • pandas
  • Matplotlib

Software Development

  • JavaScript
  • TypeScript
  • React
  • HTML and CSS
  • PHP
  • MySQL
  • Git and GitHub
  • Docker
  • Linux

Medical and Scientific Tools

Medical Physics and Imaging

  • TOPAS
  • Geant4
  • 3D Slicer
  • DICOM
  • CT image analysis
  • Proton therapy simulation

Development Tools

  • Visual Studio Code
  • Jupyter Notebook
  • GitHub
  • Docker, Kubernates
  • Ubuntu
  • LaTeX

Tools and Technologies

Tools and technologies

Areas of Interest

Scientific Computing

  • Computational physics
  • Numerical methods
  • Simulation
  • Scientific software

Medical Technology

  • Proton therapy
  • Radiation oncology
  • Medical imaging
  • Dosimetry

Artificial Intelligence

  • Machine learning
  • Deep learning
  • Computer vision
  • Medical AI

Connect With Me

GitHub profile LinkedIn profile

Combining physics, scientific computing, medical imaging, proton therapy, and software development to build meaningful solutions.

Pinned Loading

  1. Certificates-of-Volunteer-Appreciation-Fall-Semester-2023-ReDI-School-NRW Certificates-of-Volunteer-Appreciation-Fall-Semester-2023-ReDI-School-NRW Public

    My teaching of Web Dev, Machine Learning and AI at ReDi School, Düsseldorf

    1

  2. Achievements-Wissenschaftliche-Hilfskraft-at-the-University-of-Wuppertal Achievements-Wissenschaftliche-Hilfskraft-at-the-University-of-Wuppertal Public

    I worked as a Wissenschaftliche Hilfskraft at the University of Wuppertal, specializing in Natural Language Processing and Mathematical Information Retrieval, contributing to arXiv data tables and …

    1

  3. Predictive-Quality-for-Arc-Welding---Time-Series Predictive-Quality-for-Arc-Welding---Time-Series Public

    I have implemented the four supervised machine learning algorithms Random Forest, Decision Tree, Logistic Regression, and 𝑘-Nearest Neighbors on sensor data from 32 welding procedures with differen…

    Jupyter Notebook 7

  4. Analyzing-The-Large-Hadron-Collider-data Analyzing-The-Large-Hadron-Collider-data Public

    This project aims to analyze LHC (Large Hadron Collider) data within a Docker container. The primary objective is to measure the mass of the Z-Boson, a fundamental particle in particle physics.

    Python 1

  5. redi-school-machine-learning-ai-2025-2026 redi-school-machine-learning-ai-2025-2026 Public

    Volunteer teaching materials, Python notebooks, assignments, and course resources developed for the ReDI School of Digital Integration Machine Learning / AI program during Fall 2025 and Spring 2026.

  6. dssg-berlin-awo-project dssg-berlin-awo-project Public

    Mitwirkung an einer Machbarkeitsstudie für den AWO Bundesverband e.V. zur Analyse der Datenqualität der Einrichtungsdatenbank. Entwicklung einer automatisierten Datenpipeline mit NLP und LLM zur Er…