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Bennett University
Data Scientist
June 19, 2026 – Present
predictive-maintenance
April 29, 2026 – Present
predictive-maintenance — GitHub repository
View ProjectPaper-mid_summarizer
April 16, 2026 – Present
An advanced Agentic RAG platform for research papers. Features high-fidelity LlamaParse extraction, hybrid Qdrant search (Dense/Sparse), and Gemini-powered "Expert Researcher" briefings. Includes a smart context-aware chatbot with multi-turn memory and a high-speed parallelized ingestion pipeline. Built with FastAPI, LangGraph, and MongoDB.
View ProjectImage-Super-Resolution-Comparison
November 15, 2025 – November 20, 2025
Compare classical and lightweight deep-learning image super-resolution (Nearest/Bilinear/Bicubic, custom SRCNN, pretrained FSRCNN). Data prep (HR→LR), training, evaluation (PSNR/SSIM/precision/recall/F1), and outputs.
View ProjectAI-Medical-Report-Assistant-
November 11, 2025 – Present
AI-based system that reads medical reports and X-rays, explains results in easy language, and predicts health risks using explainable AI.
View Projectair-canva
March 24, 2025 – April 29, 2025
The E-Whiteboard is a digital collaborative tool that enables users to draw, annotate, and share ideas in real-time. Designed for education, brainstorming, and remote collaboration, it provides an interactive canvas for seamless visual communication.
View ProjectFacial_Recognition_Criminal_Detection
March 13, 2025 – April 23, 2025
This project aims to enhance public safety by integrating real-time facial recognition with a criminal database. The system captures facial images from live camera feeds, processes them using deep learning (CNN), and matches them against stored criminal records. If a match is found, the system triggers an alert for security personnel.
View ProjectPlantDiseasePrediction
November 3, 2024 – Present
PlantDiseasePrediction — GitHub repository
View ProjectCustomerInsightPredictor
November 1, 2024 – December 3, 2024
An ML-powered tool for segmenting retail customers and predicting sales trends. Using clustering and regression algorithms, this project helps businesses optimize marketing strategies, improve sales forecasts, and enhance customer engagement.
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong interest in applying AI/ML to real-world problems across various domains (security, retail, healthcare, research). This diversity suggests an inquisitive mindset and a potential fit for an innovative and problem-solving culture. The personal nature of all projects indicates self-motivation and initiative. However, the lack of team-based projects or professional experience beyond a current 'Data Scientist' role with a future start date makes it difficult to fully assess cultural fit in a collaborative work environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying various AI/ML techniques. The diversity of projects suggests adaptability and a willingness to tackle different problem domains. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.