Doctoral researcher at the UGC-DAE Consortium for Scientific Research (Mumbai) specializing in Condensed Matter Physics and Materials Science. I develop robust Python solutions for lab automation, instrument control, and advanced data analysis.
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I am a Physics PhD scholar specializing in Condensed Matter Physics and Materials Science. My work focuses on developing Python-based solutions for scientific computing, including lab automation, instrument control, data analysis, and data visualization.
Repositories: My primary work is on GitHub. All public repositories are manually mirrored to GitLab every few days as a backup.
Contact: I'm open to collaborations. Reach me at prathameshnium[at]duck[.]com.
Here are the primary languages, libraries, and tools I use for research, data analysis, and simulation:
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My flagship project, PICA (Python-based Instrument Control and Automation), is a modular, open-source software suite designed to automate advanced transport measurements for electronic devices and chemical samples. It serves as a robust software platform that enables advanced high‑precision characterisation of materials. I am excited to announce the release of PICA v1.0.0! This is the first stable release of the software and includes many new features and bug fixes. You can download the latest release from the GitHub releases page. 
How to Cite PICAIf you use this software in your research, please cite it. This helps to credit the work involved in creating and maintaining this resource. Using BibTeXYou can use the following BibTeX entry for your reference manager (e.g., Zotero, Mendeley, JabRef). @software{Deshmukh_PICA_2025,
author = {Deshmukh, Prathamesh Keshao and Mukherjee, Sudip},
title = {{PICA: Advanced Python Suite for High Precision Instrumentation and Transport Measurement Automation}},
month = dec,
year = 2025,
publisher = {GitHub},
version = {1.0.0},
url = {[https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation](https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation)}
}
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My latest project is Kusanagi-AI, a free and open-source local AI toolkit designed for researchers, especially in Physics and Material Science. It empowers users with privacy-focused AI tools that run efficiently on standard laptops, leveraging Ollama for local large language model inference. The toolkit's flagship application, the "Research Assistant (Orochimaru)," enables Retrieval-Augmented Generation (RAG) with PDF documents, academic review generation, and more, all while ensuring data remains 100% private and offline. 
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Based on my 2018 preprint, TupperTransformer is an interactive demo that shows how to use Tupper's self-referential formula to manipulate bitmap images and create animations just by doing math on the giant k-value. You can draw pixels, transform images, create continuous "frame motion" animations, and try examples from the preprint interactively. 
How to Cite TupperTransformerIf you use this work, please cite the original preprint.Formatted Citation (APA Style) Deshmukh, P. (2018). Transformation of the pixels in Tupper's self-referential formula. Figshare. https://doi.org/10.6084/m9.figshare.6373046 @article{Deshmukh2018,
author = "P Deshmukh",
title = "{Transformation of the pixels in tupper's self-referential formula}",
year = "2018",
month = "6",
url = "https://figshare.com/articles/preprint/Transformation_of_pixels_pdf/6373046",
doi = "10.6084/m9.figshare.6373046.v2"
} |
TupperTransformer: Interactive Tupper's Formula Demo
| Project | Description | Core Technologies |
|---|---|---|
| prathameshnium | Config files for my GitHub profile. | |
| TupperTransformer | A novel image processing framework for applying transformations (translation, pixel ops) directly to Tupper's high-precision, integer-encoded bitmaps. | javascript, algorithm, math, image-processing, applied-mathematics, bigint, theory-of-computation, HTML |
| Kusanagi-AI | A toolkit of local, privacy-focused AI applications built with Python. Includes a RAG-powered research assistant for PDFs, various chatbots, and visualization scripts. | Python |
| Prathamesh_Deshmukh_Academic_Portfolio | Source code for the academic portfolio of Prathamesh K. Deshmukh (PhD Scholar, Physics). Built with HTML and Tailwind CSS. | resume, github-pages, portfolio, html5, physics, personal-website, cv, HTML |
| static-files | A central repository for static assets (images, logos) used in my projects. | |
| Porygons-pixel-lab | This repository is a digital workshop for creating beautiful, publication-ready figures, plots, and animations. Just as Porygon is built from code, all visuals here are generated using Python scrip… | Python |
| Python-for-OriginPro | A collection of Python scripts using the originpro library to automate plotting and data management tasks in OriginLab's Origin software. | python, pandas, data-visualization, scientific-computing, data-analysis, plotting, lab-automation, Python |
| Scripts | A personal collection of utility scripts for automating various tasks on Windows and Linux, including batch files for system updates and application launching. | windows, linux, shell, devops, automation, jupyter, scripts, Python |
| Scientific-Python-Snippets | A curated collection of small, reusable Python scripts for common data handling, file manipulation, and automation tasks, often with simple GUI interfaces. | python, snippets, gui, automation, utilities, scripting, pandas, Python |
| Ketron | An open-source archive of completed projects, shared with the hope they will be useful to the programming community. | python, portfolio, latex, physics, matlab, jupyter-notebook, scientific-computing |
| Latex-Templates | TeX |
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| Physics-Simulation-Toolkit | A collection of Python scripts and notebooks for simulating and visualizing phenomena in condensed matter physics, including magnetic ordering and dielectric models. | python, jupyter-notebook, scientific-computing, ising-model, magnetism, physics-simulation, materials-science, Jupyter Notebook |
| Solid-State-Physics-Calculators | A collection of Python scripts for analyzing experimental data from solid-state physics, including calculations for activation energy (Arrhenius model) and charge transport parameters (Mott-VRH mod… | python, physics, data-analysis, solid-state-physics, materials-science, conductivity, arrhenius-plot, Jupyter Notebook |
I also have several private repositories for my research and personal projects. These include:
- Physics_Data_Fitting_Toolkit: A toolkit of Python scripts and notebooks for fitting and analyzing experimental data, with a focus on models used in solid-state physics (magnetism, dielectrics).
- Physics-Data-Analysis-Scripts: A collection of Python scripts and notebooks for processing, converting, and analyzing experimental data from physics lab instruments (dielectric spectroscopy, magnetometry, etc.).
- Physics: Condensed Matter Physics, Materials Science, Multiferroics, Experimental Physics
- Programming: Scientific Computing, Python, PyVISA, Instrument Control, Lab Automation, Data Analysis, Data Visualization
- Libraries: NumPy, Pandas, SciPy, Matplotlib
- Software: COMSOL Multiphysics, MATLAB, Origin, LaTeX
- Techniques: Data Pipelines, Automation Scripts, GUI Development (Tkinter)