My name is Mark Fajet. I'm a software development engineer working for Amazon Web Services with a passion for mathematics and algorithms. I graduated from Florida International University with a Bachelors of Science in Computer Science and a separate Bachelors of Science in Mathematical Sciences with Summa Cum Laude. I enjoy learning and I enjoy helping others learn as well. I have interests in machine learning and functional programming. I spend my free time exploring these topics as well as playing guitar.
Designed, built, tested, and managed infrastructure and internal tooling to assist in maintaining service durability and availability. Responsible for mitigating problems with all Elastic File System services. Followed Scrum and Agile methodology.
Carried out deep learning benchmarking in MATLAB and Python frameworks such as Tensorflow, Keras, and MXNet across multiple systems: CPU, single GPU, and multiple GPUs. Conducted exploratory data analysis in MATLAB where I employed linear regression, K-Nearest Neighbors, decision trees, and SVM in order to build models for prediction and classification to diagnose long startup times.
I assisted in an interactive classroom for Fundamentals of Computer Systems. Course description: "Overview of computer systems organization. Data representation. Machine and assembly language programming." As well as another course called Structured Computer Organization that focused on caching, memory, and parallelism.
Built a website from scratch using Node and PostgreSQL as a backend and React for the frontend. Managed and developed many sites. Some of these were wordpress sites where I did a lot of work in PHP to add new requested functionality. Others used Angular and Firebase.
Taught Programming for Non-Programmers, a course where students learned basic concepts of programming such as variables, decision making, functions, and Object Oriented Programming (OOP).
This course provides a general overview of machine learning topics such as supervised vs unsupervised learning, model evaluation, and machine learning algorithms using Python and frameworks such as scikit-learn, SciPy, and NumPy
Group of five courses to teach the foundations of Deep Learning, how to build neural networks, and how to lead successful machine learning projects. The courses go into detail about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, and Xavier/He initialization. It includes case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. The courses teach not only the theory, but also how it is applied in industry.
This course covers elements of the functional programming style and learn how to apply them usefully in your daily programming tasks using Scala.
This course teaches the foundations of deep learning, the major technology trends driving Deep Learning, how to build, train and apply fully connected deep neural networks, how to implement efficient (vectorized) neural networks, and describes the key parameters in a neural network's architecture
This course focuses on how to get good results from neural networks. It covers best practices, various algorithms to optimize and improve performance, bias/variance tradeoff, and how to tune hyperparameters in a productive manner.
This course describes how to build a successful machine learning project. The principals and knowledge in this course provide an undesrtanding of how to diagnose errors in a machine learning system, and be able to prioritize the most promising directions for reducing error. It goes over complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance and how to apply end-to-end learning, transfer learning, and multi-task learning
This course is about how to build convolutional neural networks and apply it to image or video data. It provides practice using convolutional neural networks for visual detection and recognition tasks, and using neural style transfer to generate art.
This course demonstrates how to build models for natural language, audio, and other sequence data. It explained Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. It has assignments that apply sequence models to natural language problems, including text synthesis, and to audio applications, including speech recognition and music synthesis.
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers how to use some basic statistics of data sets, such as mean values and variances, compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, the course shows how to then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
Created a desktop application that allows users to download large batches of websites into a custom, compressed file format which allows for easy programmatic access and extracts the metadata and main content of downloaded articles using Natural Language Processing. In collaboration with MIT Lincoln Lab.View Project
Built and trained a deep learning, image recognition model in MATLAB to determine if one is displaying rock, paper, or scissors in real time using transfer learning techniques.No link because it was created at an internal MathWorks hackathon
Web application created as a recruitment analysis tool. It analyzes tweets from users in order to see if they'd be a good fit of a certain culture. I helped create the backend. I used the Twitter API and Node.js for my part of this project. In addition, I used child processes to run data analysis in R. This project was recognized as top 10 projects at MLH Prime 2016.View Project
An artificial reality game, Game description can be found in GitHub README. For my part, I wrote a Python microservice in order to retrieve and style a map obtained from the Google Static Maps API. In addition, I did some basic math in order to make a function that allows me to return adjacent areas to that original map so that there is a seamless transition from one map to another. This project won 2nd place at MIA Game Jam in 2016View Project
This project uses EMG and temperature sensors in order to detect muscle movement and breathing. The purpose was for people with disorders (like epilepsy or sleep apnea) to be able to have something monitoring them as they sleep. I made the mobile application that people could use in order to interface with the data collected by the sensors. I used React Native and MaterialUI. This project won 2nd place and Best Social Good Hack at MangoHacks 2017View Project
You can message me on LinkedIn