I am a B.Tech student at the Dhirubhai Ambani Institute of Information and Communication Technology in Computer Science and Engineering.
My research interests lie in the intersection of robotics, reinforcement learning, and autonomous systems. I'm particularly fascinated by robot learning and how we can develop algorithms that can enable robots to learn complex behaviors through interaction with their environment.
I also worked at the University of New South Wales, Business School, under the guidance of Dr. Alba V. Olivares-Nadal, focusing on deep learning models.
Previously, I was a Research Intern at Georgia Tech Financial Services Innovation Lab (FSIL), where I developed quantitative trading strategies and worked on portfolio optimization techniques.
I'm always happy to chat about anything related to my research interests. Feel free to reach out to me via email if you'd like to discuss these topics or potential collaborations!
News
- 03/2025: Attended the Solvay Business Game in Brussels
- 12/2024: Started internship at UNSW Business School with Professor Alba V. Olivares-Nadal
- 05/2024: Started internship at Georgia Tech Financial Services Innovation Lab
Selected Projects
LMFusion Framework Implementation
Implemented the LMFusion framework to extend pretrained language-only LLMs with multimodal generative capabilities, integrating modality-specific attention and feedforward modules for text and image processing.
RAG Pipeline with Local LLM
Built a Retrieval-Augmented Generation system using GEMMA-2B-IT. This system enhances the capabilities of local language models by retrieving relevant information from a knowledge base, enabling more accurate and contextually appropriate responses.
RL-Based Drone Stabilization
Designed a drone simulation using reinforcement learning algorithms. This project demonstrates how RL can be applied to control systems, enabling drones to maintain stability in various environmental conditions without explicit programming.
Investment Portfolio Optimization
Created an MPT-based asset allocation model with Monte Carlo simulations. This tool helps investors optimize their portfolios based on risk tolerance, expected returns, and market conditions, providing visualizations of potential outcomes.
