Jonathan Sullivan

Portfolio with Bio

Bio Pic

I love tech innovations, especially in the consumer services and electronics space. Interest include mobile development, web development, and machine learning.

Jonathan Sullivan

Portfolio

Below is a Portfolio of my work that I have done.

Robotics Engineering Projects

Robots are enhancing productivity, safety, and efficiency across whole industries—from manufacturing and healthcare to agriculture, construction, and transportation. In the process, we are being freed to pursue more creative, ambitious, and intellectual achievements. Incredible new opportunities are emerging, but they require specialized skills.

Search and Sample Return Project

This project is modeled after the NASA sample return challenge. In a simulated environment, I programed a space rover to search for environmental samples using some basic computer vision techniques. With just a few lines of Python code I got a chance to experience the three main steps in the robotics process: perception, decision making, and actuation. I carried out this project in a simulator environment built with the Unity game engine.

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Technologies Used

Deep Learning Engineering Projects

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress.

Face Generation

In this project, I used deep convolutional generative adversarial networks to generate new images of faces and numbers.

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Technologies Used

Translation Project

In this project, I took a peek into the realm of neural network machine translation. I trained a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.

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TV Script Generation

In this project, I generated my own Simpsons TV scripts using RNNs. I usied part of the Simpsons dataset of scripts from 27 seasons. The Neural Network I build will generate a new TV script for a scene at Moe’s Tavern.

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Image Classification

In this project, I classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset was to be preprocessed, then trained on a convolutional neural network. I normalized the images, one-hot encoded the labels, build a convolutional layer, max pool layer, and fully connected layer. At the end, you can see its predictions on the sample images.

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First Neural Network

In this project, I got to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, I have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before mastering higher level toolssuch as Tensorflow. I also got to see how to apply these networks to solve real prediction problems!

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Artificial Intellegence Engineering Projects

Artificial intelligence is the future of computer science and technology. Its impact will be almost immeasurable. The field is wide open today, with so much to learn, and so many ways to contribute. Global innovations in the field of artificial intelligence (AI) are going to redefine virtually every aspect of our lives. By mastering AI skills I will play a critical role in helping determine how this incredible technology impacts our future.

Facial Keypoint Detection

In this project, I combined my knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system. Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition. My code is able to take in any image containing faces and identify the location of each face and their facial keypoints.

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Mimic Me!

In this project, I learned to track faces in a video and identify facial expressions using Affectiva. As a fun visualization, I taged each face with an appropriate emoji next to it. I then turned this into a game where the player needs to mimic a random emoji displayed by the computer!

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Time Series and Text Generation with RNN

Used Recurrent Neural Networks (RNN) to build RNNs that can generate sequences based on input data - with a focus on two applications: With the first I used real market data in order to predict future Apple stock prices using an RNN model. The second one is trained on Sir Arthur Conan Doyle’s classic novel Sherlock Holmes and will generate wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel!

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Dog Breed Classifier

Used Convolutional Neural Networks (CNN) to build a pipeline to process real-world, user-supplied images. Given an image of a dog, my algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Along with exploring state-of-the-art CNN models for classification, I made important design decisions about the user experience for your app. My goal was to understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. My imperfect solution will nonetheless create a fun user experience!

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Technologies Used

BookWorm

In this project, I used IBM Watson’s NLP Services to create a simple question-answering system. I first used the Discovery service to pre-process a document collection and extract relevant information. I used the Conversation service to build a natural language interface that can respond to questions.

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Alexa History Skill

In this project, I built a fully functional skill for Amazon’s Alexa that provides year-dated facts from AI History (or some other History of my choosing). Through the process, I got to use the Alexa Skills Kit (ASK) - a current state of the art API for building voice systems.

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Sign Language Recognizer

In this project, I built a system that can recognize words communicated using the American Sign Language (ASL). I was provided a preprocessed dataset of tracked hand and nose positions extracted from video. My goal was to train a set of Hidden Markov Models (HMMs) using part of this dataset to try and identify individual words from test sequences.

As an optional challenge, I am incorporating Statistical Language Models (SLMs) that capture the conditional probability of particular sequences of words occurring. This should help me you improve the recognition accuracy of my system.

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Technologies Used

Pacman Search Agent

In this project, my Pacman agent found paths through his maze world, both to reach a particular location and to collect food efficiently. I built general search algorithms, such as breadth first search, depth first search, Uniform Cost Search, Astar Search and applied them to Pacman scenarios, such as finding corners and eating all of the food.

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Solving the N-Queens problem

Explored Constraint Satisfaction Problems by implementing the N-Queens problem using symbolic constraints in a Jupyter notebook, and solving it using the Backtracking Search algorithm from AIMA.

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In this project, I defined a group of problems in classical PDDL (Planning Domain Definition Language) for the air cargo domain. I then set up the problems for search, experimented with various automatically generated heuristics, including planning graph heuristics, to solve the problems, and then provided an analysis of the results. Additionally, I wrote a short research review paper on the historical development of planning techniques and their use in artificial intelligence.

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Technologies Used

Isolation Game-playing Agent

In this project, I developed an adversarial search agent to play the game “Isolation”. Isolation is a deterministic, two-player game of perfect information in which the players alternate turns moving a single piece from one cell to another on a board. Whenever either player occupies a cell, that cell becomes blocked for the remainder of the game. The first player with no remaining legal moves loses, and the opponent is declared the winner. These rules are implemented in the isolation.Board class provided in the repository.

This project uses a version of Isolation where each agent is restricted to L-shaped movements (like a knight in chess) on a rectangular grid (like a chess or checkerboard). The agents can move to any open cell on the board that is 2-rows and 1-column or 2-columns and 1-row away from their current position on the board. Movements are blocked at the edges of the board (the board does not wrap around), however, the player can “jump” blocked or occupied spaces (just like a knight in chess).

Additionally, agents will have a fixed time limit each turn to search for the best move and respond. If the time limit expires during a player’s turn, that player forfeits the match, and the opponent wins.

I modified code in the game_agent.py file to complete the project. Additional files include example Player and evaluation functions, the game board class, and a template to develop local unit tests.

My analysis concludes with a comparison of different heuristics and my reasoning for choosing the heuristic I ultimately used in my agent.

The second part of this project I read a seminal paper(Game Tree Searching by Min / Max Approximation by Ron Rivest, MIT) in the field of Game-Playing and wrote a simple one page summary on it.

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Technologies Used

Diagonal Sudoku Solver

In this project, I wrote code to implement two extensions of a sudoku solver. The first one implements the technique called “naked twins”. The second one modifies existing code to solve a diagonal sudoku. My goals were to implement the naked twins function and write an AI agent that solves the Diagonal Sudoku game.

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Technologies Used

Solving the Traveling Salesman Problem with Simulated Annealing

In this exercise I demonstrate understanding of simulated annealing by implementing the algorithm in a Jupyter notebook and using it to solve the Traveling Salesman Problem (TSP) between US state capitals.

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Technologies Used

Machine Learning Engineering Projects

Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. I enjoy applying predictive models to massive data sets in fields like finance, healthcare, education, and more.

Stock Price Predictor

Used supervised learning to study 5 high-valued equities in the technology sector and make price predictions. This is the capstone project for the Udacity’s Machine Learning Nanodegree.

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Train a Smart Cab

Used reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time.

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Creating Customer Segments

Use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.

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Predicting Boston Housing Prices

Built an optimal model based on a statistical analysis to estimate the best selling price for a client’s home. Used the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities.

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Student Intervention System

Constructed an intervention system that leverages supervised learning techniques. Developed a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary.

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Titanic Survival Exploration

Created decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age.

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iOS Development Projects

Swift is a new language for programming iOS and OS X apps that was first introduced in June 2014. I build iOS apps sophisticated and polished user interfaces with UIKit components, implementing Core Data for local persistence of an object structures and external APIs to make use of third-party data

Virtual Tourist

Developed an app that allows you to drop pins on a map and pull up Flickr images associated with that location. Stored locations and images using Core Data.

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Technologies Used

On the Map

Developed an iOS app that adds my current location to a map of locations for other Nanodegree students it also posts a link to my own portfolio for others to see. This is the third project in the Udacity iOS Nanodegree Program.

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MemeMe

Developed an iOS app that takes a photo, and overlay text to meme-ify your friends. Then share on your favorite social channels!

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Pitch Perfect

Developed an iOS app that records a message and plays the audio back through user-selected filters.

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Web Development Projects

In today’s web building beautiful, responsive websites optimized for mobile and desktop performance and complex server-side web applications that use powerful relational databases to persistently store data is must. I used HTML and the Bootstrap CSS framework to create my portfolio, and jQuery’s DOM manipulation to dynamically populate your resume. I also developed my own arcade game in JavaScript, built complex mapping applications using the Knockout framework, and used the Jasmine testing framework to develop and maintain applications. I’ve hone my understanding of how the web works, developed complex relational databases used to store applications data, secured and configured my own Linux-based servers, and builtcomplete web applications using Python, HTML, CSS, JavaScript and SQL. My portfolio clearly demonstrate key skills mastery.

Classic Arcade Game Clone

Adapted remake of the classic frogger game given assets and game loop engine from Udacity.

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Technologies Used

Feed Reader Testing

Developed a series of jasmine.js test suites for a web-based application that reads RSS feeds.

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Interactive Resume

Developed an interactive resume application that reads my resume content from a JSON file and dynamically displays that content within a provided template.

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Movie Trailer Website

A simple movie trailer website that uses Python, JavaScript, and CSS.

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Neighborhood Map

Developed a single-page application featuring a map of my neighborhood including: map markers to identify popular locations or places you’d like to visit, a search function to easily discover these locations, and a listview to support simple browsing of all locations.

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Portfolio Site

Series of responsive portfolio sites designed from a given mockup from Udacity. This is the first project in the Udacity Frontend Nanodegree Program

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Tournament Results

Displayed information about faux tournament results using Python from a Postgres database.

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Data Science Projects

As a Data Analyst, I obtain, analyze, and effectively report on data insights ranging from business metrics to user behavior and product performance. My projects demonstrate my ability to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

Stroop Effect

Analyzed the Stroop effect, a classic result of experimental psychology using descriptive statistics and statistical test.

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Chopsticks

Analysis of the results of an experiment testing the optimal length of chopsticks.

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Bay Area Bike Share Analysis

Worked with real data provided by Bay Area Bike Share to determine differences in usage between commuters and tourists ridership patterns, average duration of trips, and more!

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About Me

Hello, my name is Jonathan Sullivan and welcome to my portfolio site. Below you will have a chance to learn more about me and see some of my work that I have done. I am very interested in Math and Computer Science. My latest work has been in the areas of Artificial Intellegence, Machine Learning, and Deep Learning. However I also love to build web and iOS apps. We as a society are athe the next major turning point in technology. We are leaving the digital age and entering the age of data and Machine Intellegence. My goals is to contribute to the next wave of technology that change the world for the better.

Education

Udacity

My recent educational experience has been with a company, that I love. Udacity provide free class in programming and software engineering and paid Nanodegrees that focus on various fields. As a millenial new college grad living with his supportive parents. I started to spend 8-10 hours on this site daily. I was so amazed at the amount of material in so many fields of software engineering. This was also my first time using python, Java was my language of choice at the time. I used these resources to learn how to build iphone apps, and website apps. I also learned skill in data science like Data Wrangling, Exploratory Data Analysis, Data Visualization, Machine Learning, Deep Learning, and Artificial Intellegence.

University of North Texas (UNT)

After leaving DCCCD were I had taken all possible math, Natural Science and computer engineering availible, I entered the university of North Texas. Here I majored in math and was pursuing a minor in Computer Engineering, Physics, Biology and Chemistry. Funny Story, my parent’s who paid my tutition, told me a semster before I took my last semester of Physics, Biology and Chemistryc ourses, that If I had the credit to graduate and didnt they I would have to pay for it. So I ended up graduating with a major in math and a minor in computer science.

Dallas County Community College (DCCCD)

The next step into futhering my technical experience took place almost 5 years later. At this time I also was a single father of a beutiful 4 years old little girl and a unborn son. While being a parent came with it on set of challenges, I had a support network which allowed me to enroll in Dallas County Community College District (DCCCD). I started to fall inlove with math. Not just because of the enthusiastic proffesor at DCCCD but also because of a man named Sal Khan and his organization Khan academy. This site allowed me to grab a firm understanding of advanded mathematics like Linear Algebra, Vector Calculus, and Differential Equations I started majoring in Computer Engineering but part way through I was convinced by my Differential Eqautions teacher to become a Math major.

School of Science and Engineering at Yvonne A. Ewell Magnet Center (SEM)

I have always had an intellectual curiosity especially when it comes to science. I would say that my first relevant educational experience began in High School. I was fortunate to go School of Science and Engineering at Yvonne A Ewell Townview Magnet Center. The school has been named among the highest-performing high schools in the country according to national \ publications’ rankings (U. S. News & World Report, Newsweek, Washington Post). In fact, when I graduated after attending school there for 3 years, we were ranked the number 1 school in the nation. In that same ,year the school was named a National Blue Ribbon School of Excellence by the United States Department of Education. Upon Graduation I had taken and made a score of ‘5’ on both the ‘AP Biology’ and ‘AP Computer Science A’. I also had taken a College Physics and General Chemistry course, allowing me to enter college at 16 with sophmore credits.

Experience

Sites I have worked on:

Skills

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