CV

Profile

Pro-active, organized and ease working with teams. Objectivity, discipline and leadership are also important traits. Assiduous student, with good academic performance, alwayes willing to learn and share.

I’ve drifted a bit from the regular paths of my graduation to become a full stack software engineer and data scientist.

Education

• (Aug/2016 - Dez/2018) Masters degree in Computational Intelligence from the Electrical Engineering department by the Federal University of Minas Gerais (UFMG)
• (2009 - 2015) Undergraduate degree in Control and Automation Engineering by the Federal University of Minas Gerais (UFMG).
  • (Sep/2012 – Feb/2013) Academic Exchange Program in Mechanical Engineering by Fachhochschule Schmalkalden, Germany.
  • (Jan/2014 – Jan/2015) Academic Exchange Program in Electronic Engineering by Newcastle University, United Kingdom.
• (2006 - 2008) Electrotechnical and Industrial Automation Course integrated with High School by Federal Center of Technological Education of Minas Gerais (CEFET-MG).
  • Mandatory intership completed in PROJELET (CGC: 05.140.192/0001-55).

Work experience

• (Aug/2018 - current) Software engineer at Loggi.
• (Jan/2016 - Jul/2018) Software engineer at WorldSense.
  • Continue to help to build up and develop the product from the software engineer standpoint as well as developing better machine learning models to support our solution.
• (Mai/2015 - Dec/2015) Software engineering intern at WorldSense.
  • Started as an intern as WorldSense was being born as a startup, helped the company to build to produt from the ground, working on all necessary steps through big data processing, machine learning and full stack software engineering.
• (Jun/2012 - Dec/2015) Undergraduate Researcher in the Engineering School of UFMG.
  • Working under mentorship of Prof. André Paim Lemos in the R&D Project “Development and Implementation of Evolving Systems for Adaptive Fault Detection and Diagnosis in Processes and Equipments”.
  • Researched and studied Machine Learning techniques, specally methods based on Statistical Inference.
• (Mar/2011 – Dec/2011) Undergradute Teaching Assistant of Computer Programming and Numerical Analysis in the Exact Sciences Institue (ICEX) of UFMG.
  • Aid the students with different needs and backgrounds in their assigments.
  • Aid the professors with their assigments, whether programming, marking exercises or developing materials.
• (Mar/2009 – Nov/2009) Intern in PROJELET – Projeto de Sistemas Prediais (CGC: 05.140.192/0001-55).
  • Design electrical, telecommunication, CCTV, fire detection and alarm systems infrastructure for commercial and residential buildings.

Skills

• Programming: Python, Scala, Kotlin, JavaScript, TypeScript, Java, R, MatLab, LaTeX.
• Tools and frameworks: AWS, Elastic stack, Docker, Spark, TensorFlow/Keras, Django, Micronaut, Camel, Kafka, React.
• Languages:
  • Portuguese: native
  • English: advanced/fluent (IELTS: 9.0 listening, 8.5 reading, 7.0 writing and 7.0 speaking)
  • German: very basic
  • Japanese: starting to study

Publications

A Batch-Incremental Process Fault Detection and Diagnosis Using Mixtures of Probabilistic PCA

T. A. Nakamura, A. P. Lemos. "A batch-incremental process fault detection and diagnosis using mixtures of probabilistic PCA", EEE Conference on Evolving and Adaptive Intelligent Systems (2014), 1–8.

Adaptive Fault Detection and Diagnosis Using Parsimonious Gaussian Mixture Models Trained with Distributed Computing Techniques

T. A. Nakamura, R. M. Palhares, W. M. Caminhas, B. R. Menezes, M. C. M. M. de Campos, U. Fumega C. H. de M. Bomfim, A. P.Lemos. "Adaptive Fault Detection and Diagnosis Using Parsimonious Gaussian Mixture Models Trained with Distributed Computing Techniques", Journal of the Franklin Institute (2017), Volume 354, Issue 6, 2543-2572.

An anatomy for neural search engines

T. A. Nakamura, P. H. Calais, D. C. Reis, A. P.Lemos. "An anatomy for neural search engines", Information Sciences (2019), Volume 480, 339-353.

Extra activities

• “Artificial Intelligence: Reinforcement learning in Python” online by Udemy (current).
• “Data Science Specialization” online by John Hopkins University on Coursera (partial).
• “R Programming” by John Hopkins University on Coursera (2015).
• Presentation on IEEE Conference on Evolving and Adaptive Intelligent Systems 2014 (IEEE EAIS14).
• Home brewing (2013).
• “Machine Learning” by Stanford University on Coursera (2012).
• Personal Economics (2004).