Teaching Hub · Physics + Computation
Teaching & Student Resources
As an Assistant Professor at UPES, Dehradun, I actively teach and mentor undergraduate and postgraduate students. My teaching philosophy emphasizes clarity, experimentation, and the integration of computation into modern physics education.
Hands-on simulations
Course-ready notes
Mentorship support
Continuously updated
📘 Courses & Resources
🤖 AI & Interactive Simulations in Teaching
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Course-wise AI Simulation Library
Last updated: May 2026
Simulations are organized below by course/module for quicker access during lecture and lab sessions.
PHYS4022P · Fundamentals of AstrophysicsB.Sc. Physics · Semiconductor / Solid-State Concepts*These AI-powered and interactive simulations are grouped course-wise to support concept-first teaching and guided lab activities.*
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Computational Astrophysics (M.Sc.)
Notebook index refreshed: May 2026
View Lecture Notebooks & Materials
- Foundations · Lectures 1-7
- Lecture 1
- Lecture 2: Data Sorting Algorithms
- Lecture 3
- Lecture 4 & 5
- Lecture 6
- Lecture 7
- Methods And Modeling · Lectures 11-17
- Lecture 11: Error Analysis
- Lecture 13: ODEs and Euler's Method
- Lecture 17: PDEs
- Advanced Applications · Lectures 19-26
- Lecture 19
- Lecture 20
- Lecture 21: Curve Fitting
- Lecture 22: Fourier Transforms
- Lecture 23: 2 Body Problem
- Lecture 24: N Body Problem
- Lecture 25A: Monte Carlo Methods
- Lecture 25B: Monte Carlo Methods
- Lecture 26: Shockwave Propagation
- Visual Explainers
- Lagrangian vs Eulerian
- Supernova Shockwave Animation
- Supernova Shockwave Animation 1
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Computational Techniques (B.Sc. Physics)
Last updated: May 2026
*Use ctrl+s to save and open Notebooks in Jupyter Lab or VS Code.*
- Laboratory & Specialized Courses Last updated: May 2026
- Fundamentals of Astronomy (B.Sc. Physics) Last updated: May 2026
💡 Key Topics Covered: Numerical integration, solving differential equations, Monte Carlo simulations, and the use of Linux, Fortran, C++, Gnuplot, and LaTeX.
📚 Courses Taught
- Python Programming: Data structures, logic building, and scientific computing
- Fortran Programming: Fundamentals and numerical applications
- C++ Programming: OOP and scientific modeling
- LaTeX for Science: Scientific writing and document preparation
- Computational Physics: Numerical techniques in physics simulations
- Machine Learning in Astronomy: Hands-on projects using real datasets
- Astrophysics: Stellar evolution, variable stars, cosmology
- Astronomy Laboratory: Observational techniques and simulations
- Planetary Science: Planet formation and solar system dynamics
- General Physics Labs: Mechanics, optics, electromagnetism experiments
👨🏫 Mentorship & Dissertation Guidance
Research Mentorship
Current projects with B.Sc. and M.Sc. students:
- RR Lyrae and Cepheid variable star classification
- Machine learning applications in astronomical time series
- Photometric data analysis using Python & neural networks
Dissertation Guidance
- Madhumita Singh: Timing and Spectral Analysis of Low Mass X-ray Binaries
- Aniket Prakash: Theoretical Modelling of Variable stars using MESA
- Shagun Thakur: Analysis of variable stars in the Gaia DR3 Era
- Divya Krishna: Precision Distance Determination of the SMC via Multiphase P-L Relations using the LMC as a Calibrator Galaxy
- Rushil Soni: Age and Metallicity of the open cluster Berkely 6
- Atharva Bhatele: Multiwavelength study of RR Lyrae stars
- Neelesh: Exoplanet parameter extraction using ExoFAST
🚀 If you are a student interested in working on a project or thesis related to astrophysics, computation, or data science, feel free to get in touch!