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• Supervised by Prof. Karon MacLean and Prof. Margo Seltzer

• Supervised by Prof. Karon MacLean
• Research in HCI, affective computing, and AI — emotion recognition through touch and haptic interaction
• Trainee at Designing for People 2020 cohort

• Switched majors to Systems Engineering

• MCB 63: Introduction to Functional Neuroanatomy (A)
• PSYCH C120: Basic Issues in Cognition (A−)

Developed web scraping modules in Python for a Research & Development project.

The main objective of the program is to enable students to gain knowledge and skills required to be a Data Scientist through hands-on experience and independent studies. My responsibilities involved using the CRISP-DM methodology and Python to develop solutions to Tenbu's projects.

My main responsibilities were to document ongoing projects, research state-of-the-art techniques for Computer Vision-related problems, and develop Proof of Concepts using OpenCV and scikit-learn.

At AppProva, my responsibility was to provide the Customer Success team with the necessary data to guide strategic decisions. Through my internship, I developed strategic, operational and tactical dashboards using Tableau and SQL; delivered user engagement reports to clients; and analyzed client surveys with Python.

• IT Sector: solve common computer issues, advise on software and hardware acquirement, and manage company's server and email client.
• Quality Sector: assist on the Quality Management System, complying with ISO/IEC 17025:2005; identify nonconformities and improvement opportunities; perform data entry using LIMS and Microsoft Office.
• Financial Sector: organize financial documents; update finance management software; schedule payments, bank routines and payrolls.

Department of Computer Science / UBC

Research at the intersection of HCI, affective computing, and AI. MSc investigated how touch and haptic interaction can be used to recognize naturalistic human emotions. Published at the World Haptics Conference and CHI. See projects for details.

MINDS Lab / DEE / UFMG

During this assistantship, I studied how Machine Learning algorithms can be applied to Computer Vision problems. I had the opportunity to learn the basics of Keras, TensorFlow and OpenCV. My research focused on understanding how to apply Deep Learning to aid in sign language recognition tasks. This project unfolded into my undergraduate thesis.

In my second assistantship, I studied the general theory of Machine Learning and how ML algorithms are being applied in the context of Big Data. In particular, I researched Random Forests adaptations applicable to Big Data problems. I also worked with Dr. Tamires Rezende to analyze the application of Random Forests in Sign Language Recognition.

Publication: Guerra, Rúbia Reis, et al. "Facial Expression Analysis in Brazilian Sign Language for Sign Recognition." Anais do XV Encontro Nacional de Inteligência Artificial e Computacional. SBC, 2018.

e-Speed / DCC / UFMG

During my first assistantship, I investigated different approaches for the hierarchical single-label classification of Transposable Elements' taxonomy. In particular, this project aimed to compare standard flat multi-class classification and local hierarchical classification methods. Dr. Gisele Pappa introduced me to Python, R, WEKA and the basics of conducting academic research.

The team's activities consisted of planning, arranging and maintaining the infrastructure for TEDx UFMG 2016. I was responsible for deploying and updating the event's website, and for guaranteeing the smooth running of projections and visual components during the talks. Event date: Nov. 25th, 2016 — Theme: Community.

GDM researches and discusses dynamics, characteristics and socio-cultural context of marginalized groups. In 2015, we organized Cadê a Cor, Engenharia? (2015.12), one of the first social awareness events held at the School of Engineering. In 2016, I assumed the leadership of the group and organized discussion sessions.

PET is about community outreach and bringing Electrical Engineering outside the classroom. My main activities were: plan and arrange Engineering-related workshops, fairs and events; coordinate the Autonomous Robots Competition (CoRA); and promote the study of Engineering in marginalized communities and schools.

Projeto AUXI proposed to place students as protagonists of education, aiming to level Brazil's education system. AUXI aimed to create an educational platform to connect students from all over Brazil so that they can exchange knowledge, narrowing the gap in Brazilian education from state to state.

Merit award for having the second-highest grade within the graduating class of 2019.

Incentive to perform research activities at UFMG.

Project: Machine Learning for Image Classification.

Project: Facial expression analysis in Brazilian Sign Language for sign recognition.

• Project: Grün Beer, a concept using genetically modified yeast to produce beer that glows in the dark.
• Team: Cibele Zolnier, Felipe Caixeta, Lethicia Andrade, Rúbia Guerra, Thiago Sanches, Vitor Almeida.

• Selected as best project in the Software Development track with the app ParaCasa (XAML/C#), which enabled users to browse Brazil's National Curriculum from their phones.
• Team: Rafael Ocelli, Rúbia Guerra.

Online courses

• Big Data Specialization (5/6) @ Coursera • 2018.09 — Certificate
• Machine Learning @ Coursera • 2017.05 — Certificate

Microsoft's Student to Business Program

• Software Development Track • 2015.10 – 2015.12
• IT Infrastructure Track • 2016.05

Language courses

• Basic Mandarin Chinese I, II & III @ Confucius Institute • 2016.03 – 2018.11
• Learn to Speak Vietnamese Like a Native @ Udemy • 2016.02 — Certificate