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Hi, I'm

Aditya Gaikwad

Software Enthusiast

Education

M.Tech in Computer Science & Engineering (2025-2027)

Indian Institute of Technology, Bombay

Working as a Teaching Assistant for the course CS101.

B.E. in Computer Engineering (2021 - 2025)

Vivekanand Education Society's Institute of Technology, Mumbai

CGPA: 9.84/10

HSC (2019 - 2021)

PACE Junior Science College, Thane

Percentage: 88.50%

SSC (2019)

Vidya Niketan, Dombivli

Percentage: 88.80%

Recent Projects

Flameberry Game Engine

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Flameberry Engine is a cross-platform 3D game engine built in C++ with Vulkan. It features a custom ECS, Physically Based Rendering, cascaded shadow mapping with PCSS Shadows, Bloom, Environment Mapping, and integration with JoltPhysics. With support for custom materials, asset management (on separate thread), and scripting via C#.

Rust based C Compiler

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A work-in-progress C compiler written in Rust, targeting a subset of the ISO C17 standard. It currently supports comment preprocessing, lexing of nearly all C tokens, and parsing of a wide range of declarations, statements, and expressions. The compiler builds a basic AST, performs semantic analysis with type checking, implicit casting, symbol table management, and handles variable shadowing. Building the base for code generation and optimization in the future.

Cloud Based Simulation of Onion Routing

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Designed a cloud-based onion routing architecture as part of a two-person Cloud Computing Mini-Project using AWS. Deployed EC2 instances as relay nodes within isolated VPCs connected via Transit Gateways, and configured each with Danted to act as HTTP proxies. Used Kali Linux with ProxyChains to simulate layered routing, enabling secure, anonymized traffic through the custom relay network.

Dehazing of Satellite Images using Deep Learning

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Worked in a team of four in BE Third Year Mini-Project focused on dehazing satellite images using deep learning. The project involved training and experimenting with neural network architectures such as AOD-Net, DehazeNet, and LD-Net on the Haze1k dataset to perform comparative analysis.

Profit Prediction for Businesses using Machine Learning Algorithms

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Led a team of four in BE Second Year Mini-Project to predict future business profits using machine learning. Implemented and compared algorithms such as Multiple Linear Regression, Support Vector Regression, Random Forest Regression, and Neural Network Regression. The project centered on a comparative analysis using a mathematically generated dataset designed to simulate real-world trends.

Contact Me

Email Me Here: 25m0745@iitb.ac.in