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Submission for the Climate Change AI Hackathon 2019 using the Extreme Weather dataset
We want to show the world how changes to their climate will affect them in their day-to-day life: in terms of risks in driving through their local neighborhoods
Simulate propagation of wildfires on satellite images of forests based on predicted wind speed a wind direction using an LSTM neural network.
Predicting Hurricane Casualities using ExtremeWeather Dataset and The International Disaster Database
We trained a neural network to predict Canadian federal election results in Quebec based on weather station-derived climate variables, and it gives interesting results!
Helping farmers, one crop at a time.
Swift, Accurate, Low Latency Mobile-Based Plant Disease Detector which not only identifies the present disease but also might give early detection with correction methods
An app for environmentally friendly reviews
Real time plant disease recognizer and treatment finder
Farmer's dashboard for disease information and detection in crops
Reinforcement learning for lower carbon home heating
We have created an application that uses Deep Learning to detect objects and classifies the method for waste management (recycling, compost, trash)
persistant realtime collaborative code editing on mobile that integrates with source control.
This project provides core support for performing object detection on navigation datasets. Support for 3D object detection and domain adaptation are in experimental phase.
HackMIT 2019
take care of yourself by taking care of a pet
activity tracker for intelliJ
given a subject, generates a list of similar subjects.
A tool that gives you a curated Spotify playlist that perfectly matches everyone's taste.
Where ever disaster strikes, resources that come in from relief groups are often scarce. Our tool utilizes machine vision to make sure these scarce resources are being distributed properly.
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Wanna pass, go to class! Use the BoilerPace app, Easy Wrap!
Learning new language vocabulary through an interactive AR experience.
Organizing Chaos in Data Repositories
