Data Science Researcher | ML/DL Engineer
Bridging Machine Learning, Deep Learning with real life applications
I am a Master's student in Data Science at UMass Dartmouth with a Bachelor's degree in Mechanical Engineering and an Advanced Certification in AI & ML from IIT Kanpur. I bring a strong academic foundation and deep enthusiasm for statistical modeling, mathematics, and using data to solve real-world problems.
My approach spans the entire data science lifecycle from data ingestion and preprocessing to model deployment and performance optimization grounded in both theory and practical application. I excel at translating complex, ambiguous domain problems into clear, impactful machine learning tasks, specializing in building interpretable, scalable, and production ready models that deliver measurable results.
I have a strong interest in research and enjoy working at the intersection of data, experimentation, and domain expertise to develop innovative, AI driven solutions. For me, data science is more than just a discipline it is a powerful tool for transforming ideas into actionable strategies and real world impact.
Developed physics-aware ML framework for FFF-processed ABS materials. Applied Symbolic Regression Analysis and trained DNN surrogate models. Implemented NSGA-II multi-objective optimization achieving Pareto-optimal process windows for strength, toughness, and energy absorption.
Designed end-to-end ML pipeline predicting mechanical properties of polyurethane composites. Achieved R² > 0.99 with DNNs, outperforming traditional empirical models. Published in Journal of Composite Science (2025).
Developed ML models for materials and engineering research. Processed datasets for regression tasks involving mechanical properties. Collaborated on validating ML results against experimental benchmarks and supported publication workflows.
Designed propulsion systems for artillery rockets. Performed CFD and thermal simulations using Ansys. Built mathematical models with Python and MATLAB for performance prediction and design validation.
Developed predictive maintenance models for automotive manufacturing. Analyzed machine data to identify equipment failure indicators. Applied statistical techniques for maintenance forecasting and reliability improvement.
Collaborated with CV engineers on robotics projects. Designed mechanical end effectors for vision-based robotic manipulation. Contributed to autonomous apple-harvesting robot integrating robotics and vision systems.
Taught Python programming, ML/DL fundamentals, and mathematical foundations for AI. Created learning content on vectors, matrices, gradients, and optimization concepts.
Collaborated on AI-focused engineering challenges. Explored supervised learning, regression, and classification through research. Applied linear algebra and probability to AI model formulation.
LLM-powered AI application automating resume customization and cover letter generation using prompt engineering and NLP pipelines.
View Details →RAG-based conversational AI enabling Q&A over video transcripts using FAISS, ChromaDB, and LangChain with OpenAI/Anthropic APIs.
View Details →LSTM + NLP system predicting S&P 500 momentum by combining price data with financial news sentiment analysis.
View Details →NLP pipelines for spam detection, sentiment classification, and complaint resolution using GloVe, FastText embeddings and LSTM networks.
View Details →High-performance deep learning pipeline using CNN, RNN, LSTM on spectrograms with parallel computing for accelerated training.
View Details →Full-stack supply chain management app with real-time dashboards, role-based access, and analytics using Streamlit and Plotly.
View Details →Loan default prediction using LightGBM, CatBoost, and FFN neural networks with comprehensive feature engineering.
View Details →ML pipelines for customer subscription, emission prediction, interest rate forecasting, and counterfeit medicine sales analysis.
View Details →CNN pipelines for face detection, transfer learning, MNIST classification, and medical image diagnosis from X-rays.
View Details →Interactive dashboards, supply chain tracking, multi-view visualizations, and CT scan analysis using D3.js, Streamlit, Plotly.
View Details →Journal of Composite Science, 2025
Developed ML models achieving R² > 0.99 for predicting mechanical properties of polyurethane composites, outperforming traditional empirical approaches.
Read Paper →Interactive skill map — drag nodes to explore, hover for details
Expected May 2026 | GPA: 4.0
Coursework: Advanced Statistics, AI, Deep Learning, Machine Learning, HPSC, Data Visualization, Advanced ML, Databases
June 2023 | GPA: 3.64 | Ranked 9 in University
Coursework: AI & ML, Data Analytics, Numerical Methods, Programming & Problem Solving
IIT Kanpur | Government of India Endorsed
Python fundamentals, Pandas, NumPy, Statistics, GLMs, Decision Trees, Bagging & Boosting, SVM, kNN, Neural Networks, Text Mining, Ensemble Methods, Pipelines, Git, Feature Engineering
Gradient Descent, CNNs, RNN/LSTM/GRU, Autoencoders, GANs, Computer Vision (Object Detection, Face Recognition), NLP, Chatbots with Rasa, Audio Processing, Image Captioning
SQL fundamentals, Data Types, Aggregations, Functions, Sub-queries, DML/DDL, Joins, Views
Interested in collaboration, research opportunities, or just want to connect?
📍 Boston, MA, USA