About me
I am Gargi Vipat, a Master’s student in Artificial Intelligence at Northeastern University, passionate about applying AI to solve challenges in areas like mental health, conservation, and creativity.
With hands-on experience in NLP, machine learning, and computer vision, I have developed impactful projects such as real-time poacher detection and sentiment analysis for mental health. My goal is to design innovative, scalable, and ethical AI solutions that make a difference.
Elevator Pitch
Hey, I’m Gargi! I’m really passionate about using AI to solve real-world problems. Whether it’s predicting healthcare costs, detecting poachers with computer vision, or mentoring students in algorithms, I love working on projects that have a real impact. I enjoy the challenge of turning complex data into meaningful insights and am always looking for new ways to push the boundaries of what AI can do.
Tell me about yourself
I’ve always been fascinated by how AI can turn raw data into powerful insights. From predicting lung cancer treatment costs to detecting poachers using computer vision, I love building AI solutions that solve real problems. Beyond technical work, I enjoy mentoring and breaking down complex concepts, which led me to become a Teaching Assistant. At my core, I’m a problem solver, always looking for ways to optimize, innovate, and create AI-driven impact.
Featured Work
Explore a diverse collection of projects showcasing my expertise in machine learning, deep learning, and data analysis. From predictive modeling to neural network implementations, these works highlight my ability to solve real-world problems using AI.
Home Loan Approval Classification
Developed a classification model using machine learning to predict home loan approval, involving data preprocessing, exploratory analysis, and model optimization for accurate predictions using Python and Scikit-learn.
Malaria Cell Image Classification
Built and trained a CNN for image classification tasks, demonstrating proficiency in convolutional layers, activation functions, and deep learning concepts using Python and TensorFlow libraries.
Car Price Prediction
Implemented a Linear Regression model to predict car prices based on various features, showcasing skills in data analysis, preprocessing, feature engineering, and predictive modeling using Python and Scikit-learn.
Time Series Forecasting
Designed an RNN model to forecast frozen dessert production, showcasing expertise in time-series analysis, deep learning architectures, and sequential data processing using Python and TensorFlow.
