I am currently a Research Intern at the Cloud Computing Lab and HIPC Lab, IIT Delhi, where I focus on building and evaluating LLM-based tools that automatically generate OpenAPI specifications from source API code.

Before joining IIT Delhi, I completed my M.Sc in Mathematics and Computing at Banaras Hindu University (BHU), Varanasi, where I worked at the DST-CIMS. My Master’s thesis was centered around Discrete Differential Geometry and its Applications, supervised by Prof. Bankteshwar Tiwari.

My academic journey began with a B.Sc (Hons) in Applied Mathematics from Jamia Millia Islamia, New Delhi. Along the way, I also pursued a Data Science Specialization through NPTEL (IIT Madras), strengthening my skills in Programming, Data Analytics, Machine Learning and Large Language Models.

My research interests lie at the intersection of:

• Discrete Differential Geometry and its Applications
• Modeling Complex Systems and Simulations
• Data Science and Machine Learning

I’m deeply fascinated by how mathematical models and machine learning tools can work together to solve real-world problems and advance research in scientific computing and AI-driven automation.

Research Experiances

0. Research Inter (IIT Dehli)

Cloud Computing and HIPC Lab

• LLM + OpenAPI Specification

1. Quantum Research Intern (QWorld)

Online

• Implemented the HHL algorithm using Qiskit to solve partial differential equations (PDEs), focusing on the Wave Equation.
• Designed and executed quantum circuits on both simulators and IBM Quantum hardware, scaling computations up to 50+ qubits.
• Explored advanced quantum algorithms such as Variational Quantum Algorithms (VQA), and Shor’s Algorithm etc.

2. Machine Learning Intern (Devtern)

Online

• Developed accurate ML models using Logistic Regression and Decision Trees for Heart Disease Prediction and House Price Estimation, achieving over 90% accuracy.
• Built end-to-end ML pipelines, incorporating model design, training, optimization, and deployment via API development.
• Performed data preprocessing, including cleaning, feature transformation, and exploratory data analysis (EDA) to uncover insights from complex datasets.
• Applied techniques such as feature engineering, hyperparameter tuning, and model evaluation to enhance performance and interpretability of solutions.

Skills

• Programming : Python, C/C++, MATLAB, Julia, Qiskit, Pennylane.
• Tools and Frameworks : PyTorch, FastAPI, MLFlow, PDEToolBox
• Artificial Intelligence : Building, Training, Evaluating and Deplyment of Models and LLMs based Tools and Function Workflow Design.
• Data Driven Decision Making : Statistical methods, Optimization methods, machine learning methods and deep learning methods.
• Soft Skills:

Languages

1. Mother toung - Awadhi + Bhojpuri
2. Hindi
3. English
4. Sanskrit
5. Urdu