Computer Science Graduate
Hi, I'm Michael Paglia
Currently a senior data analyst and team lead at Meta whilst pursuing my Master's degree in Computer Science. Passionate about spatiotemporal data and signal processing.
Download Resume
Currently a senior data analyst and team lead at Meta whilst pursuing my Master's degree in Computer Science. Passionate about spatiotemporal data and signal processing.
Download Resume
I began my career in computer science in August 2021 at the University at Albany and graduated Summa Cum Laude with my Bachelor of Science degree in May 2024.
Currently, I am employed as a senior data analyst at Meta, where I operate as a team lead and subject matter expert, collaborating with vendors to improve data labeling accuracy. My work involves performing data analysis, implementing scraping techniques to analyze vendor data, and curating reports to drive quality improvement while serving as the quality lead for multiple agentic workstreams. I regularly meet with FAIR researchers to align on new guidelines and enhance project outcomes. I plan to continue my education at the University at Albany for my Master of Science degree with an anticipated graduation date of May 2025.
Most recently, I was a member of the Data Management and Mining Lab at my university and a grant recipient from the National Science Foundation for a research fellowship. My areas of interest in research included spatiotemporal data and signal processing.
C
Java
Python
JavaScript
MySQL
Go
AWS
Photoshop
This project involves creating an interpreter for a simple programming language, focusing on parsing and semantic analysis mechanics.
A small Python script to attempt to solve New York Times' "Connections" game using Google's word2vec tool.
A showcase of my portfolio and blog, developed using HTML, CSS, and JavaScript for a dynamic and responsive user experience.
A sparse dictionary encoding Python library developed as an extension of several machine learning research papers.
Simulation of an operating system's functions including process scheduling, memory management, and file systems.
Bayesian optimization techniques applied to Premier League teams and players to analyze and predict hypothetical match scenarios to take advantage of vigorish
michaelpagliadev@gmail.com
Troy, New York