Education and Research
Andrea graduated from Columbia University in May of 2016 with a B.S. in Biomedical Engineering and minors in Hispanic Studies and Mechanical Engineering. As an undergraduate, Andrea worked at the Neurotrauma and Repair Laboratory at Columbia University, where she used MATLAB to analyze the stretch rate and the stretch distance on in-vitro brain tissue to find a relationship between repeated stretch injury and cell death after blast, as well as the difference in long-term potentiation from brain tissue slices submitted to repeated blast injuries. Following graduation, Andrea worked as a Business Operations Manager at OpenBCI, Inc for two years.
Andrea began her Mechanical Engineering Masters of Engineering at Cornell University in Spring 2018, specializing in Robotics and Machine Learning. In March 2018 she joined joined the Micro/Nanofluidics Laboratory where she worked on bioimage analysis of endocytosis, as well as machine learning and computer vision techniques in the context of spot detection in biological images.
Html & CSS
These are some of the things I've worked on:
Study Buddy Robot
Using EEG to predict the attention level of a user to a specific task and when the user got distracted, the system would alert the user by the movement of a robot. Hidden Markov Models were used to accurately predict the state of the user and provide the user with adecuate feedback. The project was done using Python and ROS.
Is it Trump? Tweetbot
Predicting if a tweet sent by the 45th president's twitter account is from the president or from his staff. In 2016 it was hypothesized that President Trump tweeted only from his android phone and his staff tweeted from his account using an iphone. This was addressed by their administration already and now all the tweets are sent from iphones. This project was done using the scikit-learn library and Python.
Final project for Software Carpentry class. Hangman is a words guessing game for one player. The computer generates a word at random and the user has to suggest letters one by one. Every time the word contains a letter, the computer opens it to the user to see (all of them, if there are a few). Every time the word doesn't contain a letter, the computer gives a penalty point for the user. If there are seven penalty points, the user loses. If there are no hidden letters anymore, the computer loses. The GUI was made using the pygame library and Python.
Diagnosis of refractive error requires a formal eye exam to be performed by a professional eye care provider, such as an optometrist. This makes is harder for people to access proper eye care when no optometrist is close. To address this issue an innovative and low-cost medical device that accurately measures refractive error (glasses prescription) in a patient's eye that could be used by an untrained healthcare worker was developled. The design was made by using Creo and was constructed using 3D-printing and low cost lenses.
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