Rust software engineer at Vaultree. I like everything related to data security, databases and distributed systems.
◉ 12/2022 – Current: Rust Software Engineer at
Vaultree
(Remote)
At Vaultree’s engineering team, I contribute to the development of
always-encrypted database management solutions for different SQL flavors.
The Vaultree's FFDUE (Fully Functional Data-in-use Encryption) technology provides a
Software Development Kit (SDK) that enables businesses to efficiently work with
fully encrypted databases. In that sense, I've been accumulating experiences developing
database drivers (JDBC, Python DB API, etc) and different management products to
onboard and evolve customers' database schemas. The tools include
database migration, database replication, shell REPL environments, and more.
◉ 03/2022 – 12/2022: Performance Engineer at Dell Technologies (Remote)
At Dell, I developed tools to assess Non-Functional Requirements (Performance – Capacity –
Scalability – Availability – Reliability – etc) of the Dell's Finance
Services. For that purpose, I worked developing Python automations using Flask, Dynatrace, Docker, SQL,
etc. Also, I had to use NeoLoad to run stress tests in order to collect metrics that were automatically
processed afterwards in order to generate official performance reports.
◉ lt.rs - Learning to Rank for Rust
`lt.rs` is a Learning to Rank library written in Rust and based on the
famous RankLib library. The main goal of this project is to provide a
simple, fast, and memory-safe Learning to Rank library which
implements a wide variety of LTR models. Currently, lt.rs supports
only the AdaRank model. The source code can be found here:
https://github.com/marcosfpr/ltrs.
◉ Twitter Trending Hashtags and Mentions: Analytics
In this small project, I’ve developed a real-time streaming processing
that consumes the Twitter API through Apache Spark and then plots Web
analytics about the most commented Hashtags and Mentions on the
platform. For this project, I’ve used Docker, Docker Swarm, Apache
Spark Streaming, and Python. The source code can be found here:
https://github.com/marcosfpr/spark_streaming_twitter/.
◉ ATRI: Experimental Information Retrieval Tool
The Atri project is an experimental search engine, implemented
from scratch. In this project, a few classic IR models were implemented
to calculate similarity between the documents of a collection and a user query: Boolean,
Vector Space, Probabilistic, BM25, Belief Network, Extended Boolean, Generalized
Vector Space, DFRee, and PL2. In addition, ATRI allows the creation of
a benchmarking environment for reproducible evaluation of the
effectiveness and performance on IR through automatic creation of
ensembles, visualization of effectiveness metrics, and support to
scientific collections. The source code can be found here:
https://github.com/marcosfpr/atri_ui.
◉ Solver - Foundations of Computational Agents
A project designed to study the design of intelligent computational agents
following concepts from this book.
In this project, I've implemented path finding agents using algorithms like A-Star,
DFS, BFS, etc. The source code can be found here:
https://github.com/marcosfpr/solver/.
◉ Whooshy - A custom fork of Whoosh
Whoosh is a Pure-Python full-text search library that implements all the tooling
to build a search engine, including a fast indexing and retrieval of documents.
This original version of the library contains only a few scoring algorithms (e.g. BM25F).
The fork implements morre classic information retrieval scoring methods:
Boolean, Vector Space, Probabilistic, BM25, Belief Network, Extended Boolean,
Generalized Vector Space, DFRee, PL2, and PageRank. The source code can be found here:
https://github.com/marcosfpr/whooshy/.
◉ 2023 – Current Masters Degree in Computer Science at Universidade Federal de Ouro Preto, Ouro Preto, MG.
◉ 2018 – 2022: Bachelor of Computer Science at Universidade Federal de Ouro Preto, Ouro Preto, MG.
◉ 2015 – 2017: Technical and high school at Instituto Federal de Minas Gerais (IFMG), Ouro Preto, MG.
◉ Júnior, Marcelo Trajano Alves, Marcos Felipe Pontes Rezende, and Guilherme Tavares de Assis. "DEVELOPMENT OF A FOCUSED WEB PAGE CRAWLER BASED ON GENRE AND CONTENT." WWW/INTERNET 2021 AND APPLIED COMPUTING: 77.