M.Tech in Hydraulics & Water Resources Engineering at
Indian Institute of Technology Kanpur
I am an engineering postgraduate interested in using computational modelling, numerical simulation, data analysis and machine learning to solve real-world engineering problems.
- π M.Tech β IIT Kanpur
- ποΈ B.Tech Civil Engineering β NIT Kurukshetra
- π¬ Working on numerical modelling of convective transport in layered porous media
- π Interested in computational engineering and data-driven problem solving
- π Academic Excellence Award, IIT Kanpur
- π First Prize β Hall 14 Cricket Tournament, IIT Kanpur
Fluid Mechanics β’ Hydraulics β’ Hydrology β’ CFD β’ Numerical Modelling β’ Data Analytics β’ Machine Learning
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Python β’ SQL β’ Statistics β’ Power BI Analysis of 539,747 U.S. domestic flights to investigate delays, cancellations and operational performance. Highlights
π View Project β |
ANSYS Fluent β’ CFD 3D investigation of turbulent flow separation and reattachment through a 2:1 sudden expansion. Highlights
π View Project β |
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EPA SWMM β’ Python Urban drainage modelling with rainfall, imperviousness and conduit-capacity sensitivity analysis. Highlights
π View Project β |
COMSOL Multiphysics β’ MATLAB Numerical investigation of convective transport in vertically confined layered porous media. Studying the influence of:
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Machine Learning for Engineering
Developed machine-learning models for heterogeneous porous-media data.
Methods
Random Forest β’ Gradient Boosting β’ SVR β’ Feature Engineering β’ Cross-Validation
Hydrological Analysis using ERA5
Worked with long-term ERA5 atmospheric and precipitation datasets for:
- Rainfall frequency analysis
- IDF curve development
- Probability distribution fitting
- FAO-56 Penman-Monteith evapotranspiration
- Atmospheric lapse-rate analysis
Trainee Engineer
Exposure to construction execution, surveying, engineering drawings, quality checks and infrastructure implementation.
- Computational Engineering
- Scientific Computing
- Numerical Simulation
- Engineering Data Analytics
- Machine Learning for Engineering Applications