João Pereira

JP
25 | Portuguese
Download CV

About me


I am a PDEng candidate in data science at Eindhoven University of Technology, where I develop data science projects for industry. Most of the time I do machine learning and work on problems involving time series, images, videos, text, and graphs, coming from various application domains, spanning energy, retail, manufacturing, and healthcare. Prior to joining TU/e, I developed applied deep learning research at the Institute for Systems and Robotics in Lisbon and obtained my MSc degree in Electrical and Computer Engineering at Instituto Superior Técnico (University of Lisbon). Here you can find an up-to-date list of data science related projects I have worked on and here a list of my publications. Feel free to contact me!

Education


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PDEng in Data Science

Jan. 2019 - Jan. 2021

Eindhoven University of Technology

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MSc & BSc in Electrical and Computer Engineering

Sept. 2013 - Nov. 2018

Instituto Superior Técnico

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Exchange Student

Sept. 2016 - Jan. 2017

Université Catholique de Louvain

News

  • May 2020 Gave a talk on "Anomaly Detection with Variational Autoencoders" (Video & Slides)
  • January 2020 Started my final project with the Belastingdienst on object detection.
  • December 2019 Gave a workshop on deep learning for the PDEng Data Science. Check out the materials (slides & code) here.
  • October 2019 We introduce Solar Scan! (blog post)
  • July 2019 I'll be lab monitor at the Lisbon Machine Learning School - LxMLS'19
  • June 2019 Participating in the DeepFake Detection Challenge - Presentation and Code

Projects

Object Detection and Image Classification

Object Detection Image Classification Neural Networks
TensorFlow Flask Python JavaScript

Solar Scan

Solar Potential Image Segmentation CNNs
Python JavaScript C# Google Cloud Platform Microsoft SQL GIS Leaflet

Prediction of Occupancy in Working Spaces

Neural Networks Embeddings Regression
TensorFlow Python

Detection of Suspicious Cases of Money Laundering

Graph Analysis Community Detection
Microsoft Azure PowerBI

Unsupervised Particle Identification in Reticle Images

Image Processing Peak Detection
OpenCV Scikit-image

Recommendation System for Product Cross-Sell

Matrix Factorization SVD
Python Plotly & Dash

DeepFake Detection

Video Classification CNNs RNNs
TensorFlow Python JavaScript

Peak Power Forecasting in Electrical Substations

Neural Networks Time Series Forecasting
Matlab

Analyzing the Part Lifecycle in the Supply Chain Process at ASML

Process Mining
Disco Python & Dash

Anomaly Detection in Solar Generation Time Series

Time Series Data Variational Autoencoders Neural Networks Unsupervised Learning
TensorFlow Python

Experience


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Data Scientist

Jan. 2020 - Jan. 2021

Belastingdienst

  • Working on my PDEng final project for the Fiscal Intelligence and Investigation Service (FIOD) of the Dutch Tax Authorities (Belastingdienst).
  • Developing an application for large-scale object detection and analysis based on deep learning.

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PDEng Trainee in Data Science

Jan. 2019 - Jan. 2021

Jheronimus Academy of Data Science (JADS)

  • Developing data science projects for industry. Check out the Projects section!
  • Companies: 2x ASML, Van Lanschot, TE Connectivity, Heijmans.
  • Project topics: machine learning, computer vision, recommendation systems, time series processing, graph analytics

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Lab Monitor

July 2019

Lisbon Machine Learning School (LxMLS)

  • Part of the organizing team of LxMLS'19 as a monitor. I had responsibilities in the organization of the school and helped the students solving the exercises during the lab sessions, implementing machine learning algorithms such as naive Bayes, hidden Markov models, conditional random fields, recurrent neural networks, and reinforcement learning. The school is mostly focused on Natural Language Processing (NLP).
NLP Deep Learning Neural Networks Reinforcement Learning CRFs HMMs RNNs

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Researcher

Jan. 2018 - Dec. 2018

Institute for Systems and Robotics (ISR-Lisbon)

  • Developed applied machine learning research focusing on deep learning and applications to time series anomaly detection and representation learning.
  • Publications
Deep Learning Neural Networks Anomaly Detection Time Series Data

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Intern

Sept. 2017 - Nov. 2018

C-Side

  • Developed my master thesis project in partnership with C-Side on deep learning for anomaly detection in time series data.
  • Worked with solar energy generation time series.
Neural Networks Load Forecasting Substations

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Intern

{July 2017, July 2016}

EDP Group

  • Developed a data science project on load forecasting using neural networks, within the Planning Department of EDP Distribuição.
  • Worked on design of medium/low voltage networks within the Network Studies team
Neural Networks Load Forecasting Substations

Publications


Unsupervised Anomaly Detection in Energy Time Series Data using Variational Recurrent Autoencoders
Conference Paper
Oral Presentation
João Pereira
, Margarida Silveira
Orlando, Florida, USA

17th IEEE International Conference on Machine Learning and Applications (ICMLA'18)

Solar Energy Generation Photovoltaic Panels Deep Learning Time Series Data Variational Autoencoders Reconstruction Probability Recurrent Neural Networks Variational Self-Attention Mechanism

Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection
Conference Paper
Oral Presentation
João Pereira
, Margarida Silveira
Kyoto, Japan

2019 IEEE International Conference on Big Data and Smart Computing (BigComp'19)

Electrocardiogram Deep Learning Heartbeats Variational Autoencoders Wasserstein Distance Latent Space Detection

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Unsupervised Anomaly Detection in Time Series Data Using Deep Learning
M.Sc. Thesis
João Pereira
Lisbon, Portugal

Instituto Superior Técnico, University of Lisbon

Anomaly Detection Time Series Data Variational Autoencoders Recurrent Neural Networks

Unsupervised Representation Learning and Anomaly Detection in ECG Sequences
Journal Paper
João Pereira
, Margarida Silveira

International Journal of Data Mining and Bioinformatics (IJDMB)

Electrocardiogram Heartbeats Variational Autoencoders Wasserstein Distance Latent Space Detection

Reviewing and Program Committees

  • Journals: IEEE Access; Wireless Communications and Mobile Computing (Wiley).
  • Conferences: International Conference on Artificial Intelligence, Information Processing, and Cloud Computing; International Conference on Time Series and Forecasting; ALLDATA2020.

Talks


"Anomaly Detection with Variational Autoencoders"

May 2020

Deep Learning Sessions Lisbon

Slides

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"On Deep Learning - An Overview"

Oct. - Nov. 2019

Jheronimus Academy of Data Science

Slides

Get in Touch!

mail@joao-pereira.pt