If you are interested in joining the lab, you need to meet the following requisites.

  • Have at least taken the Data Structures class
  • Have a CS GPA of at least 3.00
  • Love to code.
  • Be willing to learn new programming languages
  • Be interested in Cybersecurity, Science Education, or High Performance computing
  • Have at least 9 hours weekly to dedicate to the lab work.
  • Be willing to spend Fridays in the lab, for group meetings and work.
  • Be willing to meet on Wednesdays at the Universal Hour.
I am currently interested in students that would like to implement web based visualizations and statistical algorithms for data anomaly detection.

Here is an incomplete list of past projects.


Abstract. Toa is an open source web based NetFlow data network monitoring system (NMS). Toa consists of a collection of scripts that automatically parse NetFlow data, store this information in a database system, and generate interactive line charts for network visualization an- alytics. The system is pseudo real time, meaning that it continuously updates the interactive charts from NetFlow data that is generated ev- ery five minutes. The system allows the users to generate customized charts from the data stored in the database system.

Current students:

Alfredo Valles, Julio de la Cruz, Ian Dávila

Past students:

Albert Maldonado, Eric Santos, Gilberto Ramos and Juan Rodriguez

Link: https://github.com/cslab-uprrp/toa

Periodic Arrays

Results on the exhaustive exploration of 3D arrays over the elementary Abelian group.

3 D Costas ( 3 x 3 x 8 ): Note that the arrays does not contain the leading *. There are 96 distinct 3D costas arrays of size 3x3x8. We characterized the group of symmetries of this 3x3x8 arrays using algebraic symmetries.

3D Sonar ( 3 x 3 x 9 ): Note the use of -1 as the non defined value of the 3D array. There are 5832 distinct 3D sonar arrays of size 3x3x9.

Link: https://github.com/cslab-uprrp/toa

Netflow Cube

Abstract: The monitoring and analysis of big data networks to detect abnormal network behavior is a complex task for network administrators, which can not be accomplished without the aid of visualization analytics. In this work, we present a web based implementation of the three dimensional cube known as the Spinning Cube of Potential Doom using WebGL and the Three.js library. With this implementation our goal is to provide an easy to use and access cube interface, without the need to install and configure additional graphic software in a single machine. This application allows the system administrators to visualize distinct network events such as network and port scanning. In our current implementation we use data from NetFlows stored in a remote server, and provide the system administrator with a control panel that allows the selection of past data, and filtering by networks classes. The computing needed for the filtering and parsing of the data is done in the server side, the web browser is only used for the visualization.

Past Students:

Jhensen Grullon and Eric Santos