PlantBot: Your Friendly AI Gardener

Developed PlantBot, an AI-powered gardening assistant designed for busy city dwellers. Using a Raspberry Pi, sensors for soil moisture and sunlight detection, and an ML model trained on plant health datasets, PlantBot proactively alerts users when their beloved houseplants need care. With cheerful notifications like “I’m thirsty—your fern speaking,” PlantBot has humorously saved countless houseplants from neglect, turning plant care into an engaging, enjoyable experience.

Over the last two years, I've been dedicated to promoting the integration of AI among scientists at the globally acclaimed marine research institution, Plymouth Marine Laboratory (PML). PML's core research program is focused on critical areas such as climate change, marine pollution, and sustainability. In a recent research study published in July 2023, I developed Machine Learning approaches to ascertain the primary factors influencing the phytoplankton carbon-to-chlorophyll ratio in the Atlantic Basin.These methods provided valuable insights into the environmental factors affecting phytoplankton carbon and chlorophyll levels in the corresponding figures.

Created Roboista, a witty barista robot who serves coffee with a dash of humor and personality. Roboista uses computer vision to identify customers, machine learning to remember their coffee preferences, and natural language processing to engage in cheerful banter. Built on a Jetson Nano platform, Roboista combines TensorFlow Lite for real-time customer interaction and Arduino-driven robotics for precise brewing operations. The robot didn’t just improve the cafe’s efficiency—it became a beloved community figure, boosting both sales and smiles.

Achieved an 80% accuracy rate in classifying celestial objects as Stars, Galaxies, or Quasi-Stellar Objects using a machine learning model that analyzes solar data. It was done through Data Collection, Data Preprocessing, Model Optimization, Iterative Improvement and Documentation.I Gathered a comprehensive dataset of solar data, including various features and attributes. I then Cleaned the dataset by handling missing values and outliers. Further Chosing appropriate machine learning algorithms for classification tasks and then Fine-tune hyperparameters of the model using techniques like grid search or random search to improve its performance. Lastly, Documented the entire process, including data sources, preprocessing steps, model architecture, and results, to facilitate reproducibility and collaboration.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.