Hello! I am Diego Escobedo, and I recently graduadated from MIT with a BS in Computer Science and Engineering. I am deeply interested in machine learning, specifically its applications to medical research and financial markets. In my free time, I love DJing, messing around on my film camera, and playing basketball (poorly). Please don't hesitate to email diegoescobedo ατ mit døt edu if you have any questions, ideas, or just want to chat. Hope you enjoy my website!
Class of 2022
GPA: 4.7 | Major GPA: 4.9
Clubs: Phi Delta Theta, Amphibious Achievement, DanceTroupe, College Diabetes Network
Final project for 6.864, MIT's class called "Advanced Natural Language Processing". 
                      I made a Variational Autoencoder with byte-level byte-pair encoding and nucleus sampling decoding to generate funny tweets. 
                      Trained on a custom dataset created by scraping Gen-Z Twitter's funniest tweets. 
                      
 
                      
                      View a static version
                        here, including all implementation details.
                    
Capstone project for my STEP Internship. Along with two other interns, we created a fantasy
                      basketball engine. The idea behiond it wasd to be able to create a "fantasy" team with players
                      from any era and any team. My part of the project involved creating a neural network
                      that could effectively predict the outcome of games WITHOUT using any team-level data (since we
                      only have player-level data about these fantasy teams). I succesfully managed to create a model
                      that worked, and in fact beat a few scientific papers (detailed in this
                        meta study ). Created the model in Tensorflow and then deployed to TensorFlow.js on Google
                      App Engine. 
 
                      View the
                        website here. I made the landing page and the "Simulate" page, the rest were created by
                      other team members.
                    
Final project for 6.S083, MIT's class called "Computational Thinking in Julia". I made a
                      walkthrough on how to use gradient descent to fit real-world COVID data to a SEIR
                        Model. Fitting this data is extremely important, as these models give us powerful insights
                      about the spread of the disease, but finding the right parameters for the differential equations
                      is not an easy task. 
 
                      View a static version
                        here.
                    
N-dimensional minesweeper. Probably not as good-looking as Microsoft's version, but I don't think you can play their version in a hypercube. Made for 6.009, MIT's Fundamentals of Programming course. Web app not available, but feel free to download the source code!
                      To run the app from your computer, unzip the files, right click server.py and go to properties, and copy the location. Then, open up the terminal, and type "cd" and then paste the location of server.py. Then, type "python3 server.py", wait a little bit, and then type "localhost:8000" on your web browser.
Inspired by the timeless Bloons Tower Defense, I made a small tower defense game. It's a little slow and buggy, but it works alright. Made for 6.009, MIT's Fundamentals of Programming course. Web app not available, but feel free to download the source code!
                      To run the app from your computer, unzip the files, right click server.py and go to properties, and copy the location. Then, open up the terminal, and type "cd" and then paste the location of server.py. Then, type "python3 server.py", wait a little bit, and then type "localhost:8000" on your web browser.
Had a lot of fun working on my very first open-source project, a webscraper for the website Basketball Reference. The repo for the scraper can be found here. As you can see from my pull requests (#5 and #14) and my raised issue, I was actively working towards improving this project and managed to find a serious bug. I used this scraper when I was first learning how to use ML techniques to analyze NBA data, and it has been extremely helpful since then.