Mario Matos

Led team and directly worked on a data pipeline that would connect to a streaming service for temperature readings.

Added the data to a Kafka topic then processed with Spark Streaming and sent back to output Kafka topic.

Data was made available with a schema in Hive, visualizations were created in Jupyter Notebook aggregating different aspects of the data.

Convolutional Neural Network that learned how to colorize grayscale images. I directly worked on designing and implementing the convolution layers necesary to achieve this. One nice idea was to include a predefined layer for object detection.

Sped up results by including 'imagenet' pretrained object detection layer into our custom designed layers.

Leveraged the power of various Python libraries for manipulation and preparation of images. For example, matplotlib, keras.preprocessing.image, skimage.color.

As part of development, created tools and functions to easily convert and compare images from grayscale to colored images. This would speed up the learning process and shorten the training time.

One example of our results can be seen here: https://github.com/marioscience/image-colorizer-notebook/blob/master/download%20(2).png.

The entire process was documented in the mock research paper: https://github.com/marioscience/image-colorizer-notebook/blob/master/Research%20Paper.pdf

Using cosine similarity matrix (same algorithm used by Netflix) movies are recommended to users depending on their taste or current trends.

Consumed OMDB API, which is similar to IMDb. This allowed for movie posters and user ratings to be used in data analysis.

Leveraged the power of Python data science libraries such as sklearn, surprise, and pandas.

Created entire application architecture with data, services, API and UI layers.

Designed and implemented Docker and docker-compose configuration for quick containerized deployment.

Created UI using React.js reusable components. Responsive layout was created using compact and elegant CSS3 flex layout.

Using Spark and Scala, created a distributed system to compute bootstrapping statistics and distributed aggregation of data.

Utilized Scala for creating the code that would run in multiple instances of Apache Spark.

Created distributed algorithms to deal with data that is split in multiple storage locations.

Combined power of Scala, Spark and server configurations to process large input files

Hadoop Distributed System running distributed map reduce algorithms. As part of this project, Docker configuration and supporting bash scripts were created. The programming language used is Python.

Created Hadoop configurations for each type of server in a typical hadoop cluster.

Created Hadoop MapReduce algorithms using Python.

Containerized application by writing Docker configuration.

Worked on implementation and deployment of features for a CRM module that integrated with Sage ERP System

Developed features at all levels of the web application stack

Participated in all stages of the software development lifecycle

Worked in advanced architecture that included technologies such as Node.js, Express.js, PM2, MySQL, Angular, Bootstrap, SQL Server

Homegenius is a marketplace for real estate properties. I worked on creating a complete framework that automated many of the development tasks and put me and my team ahead of schedule for many sprints.

Developed framework for development of features that automated many development tasks such as production and development builds, dependency management, media asset management, code linting, automated testing, reusable UI components, and advanced software architecture.

Designed and implemented main front end feature which consisted of a Google Map V3 with many features on top of it. These included filtering of properties by desired criteria, showing colors in markers depending on price, custom selection of area, neighboorhood delineations, and pulling Google information about amenities nearby.

Built many listing and detail pages with advanced functionality

Designed and integrated many REST APIs

[Github: https://github.com/UCHIC/iUTAHData]

Main website of iUtah project. An ambitious research project to collect and analyze environmental hydrologic data. The website has a D3.js dashboard that displays data processed in the backend. This data comes from sensors deployed in the field measuring a wide range of variables.

Built from simple requirements all the way to deployment and maintenance.

Created my first website by leveraging the power of Django.

Included an admin site where users could add and modify content in different pages.

[Github: https://github.com/marioscience/asteroid_exploder]

Asteroid shooting game. Using game design principles and techniques, NodeJS, HTML5 Canvas, JavaScript, etc. Deployed using Heroku.

Built from simple requirements all the way to deployment and maintenance.

Created my first website by leveraging the power of Django.

Included an admin site where users could add and modify content in different pages.

The project is internal to the Utah Water Research Laboratory but you can see what the website looks like here: http://data.iutahepscor.org/mdf/static/mdfserver/images/development/equipment_management.png

Inventory Management System to keep track of sensor equipment utilized for collecting environmental hydrologic data at sites for research. This system kept track of all equipment, wethere it was deployed, where it was deployed, maintenance, and any other activity related to them.

Worked on entire level of the software development lifecycle. From requirements to deployment and maintenance.

Combined Python, Django, jQuery, and Bootstrap to create modern web application

Wrote DDL and DML advanced SQL queries to design and create schemas or complex data retrieval.

Implemented open source Wikimedia for internal wiki for employees at the USU Campus Store.

Understanding of open source and identification of solution to common problem in my work environment.

Full deployment and maintenance to local Linux server.

Implemented cameras that detected movement and recorded video utilizing low disk space.

Created camera system that was robust and deployed to local Linux servers.

Utilization of open source technologies.

Engineered scripts to support desired behavior.