Research

Work & Projects

My work focuses on building practical AI systems that translate machine learning models into usable tools. I am particularly interested in explainable forecasting, AI-driven analytics, and systems that help users better understand model predictions.

Below are selected projects that reflect this work.

Projects

  1. 1. Live Applied AI Platform

    ForecastLens AI - Explainable Time Series Forecasting Platform

    An AI-powered forecasting platform that transforms raw time-series data into interpretable predictions. The system integrates automated pattern detection, anomaly identification, and model comparison to generate reliable forecasts and explain trends clearly for users.

    Built with a multi-model evaluation pipeline that compares forecasting approaches using metrics such as MAPE and RMSE, ensuring transparent and robust model selection. The platform also includes LLM-driven narrative insights that translate statistical outputs into human-readable explanations.

  2. 2. Live Productivity AI Application

    Voice-to-Action Productivity Assistant

    This project is an AI-powered web application that converts spoken ideas into structured productivity workflows. Users can record or upload voice notes, and the system automatically transforms the audio into actionable items such as tasks, reminders, calendar events, notes, or ideas.

    The goal of the application is to bridge the gap between unstructured voice input and structured productivity systems, enabling users to quickly capture thoughts and turn them into organized actions.

  3. 3. Research Build Fellowship Project

    Bioinformatics & Machine Learning: Biomarkers of Aging

    Developed machine learning workflows as part of the Build Fellowship program, focusing on practical applications of data-driven modeling and experimentation. The project involved designing data analysis pipelines, experimenting with predictive models, and translating model outputs into interpretable insights.

    The work emphasized bridging theoretical machine learning concepts with real-world analytical problems while collaborating with mentors and peers in a research-driven environment.

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