About me
I am a Data Scientist at adidas in the Digital Data Science team in Amsterdam where I work on developing demand forecasting models and productionizing them in highly scalable, cloud-native data pipelines. Prior to joining adidas, I completed my Professional Doctorate (PDEng) in Data Science at Eindhoven University of Technology, where I worked on data science projects for multiple companies (ASML, Van Lanschot, TE Connectivity, Heijmans, FIOD). Before the PDEng, I conducted deep learning research focusing on anomaly detection in time series data and completed my Masters and Bachelors degree in Electrical & Computer Engineering. I am broadly interested in the field of machine learning and its endless applications and passionate about turning cutting-edge research into products. Here you can find a list of my previous projects and here a list of publications. Feel free to contact me!
News
- March 2021I joined adidas as a Data Scientist in Amsterdam ///.
- 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
Experience
Data Scientist
March. 2021 - Nowadidas
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In the Digital Data Science team, my main responsibilities have been:
- Developing production-ready machine learning models that forecast demand for thousands of adidas products (PyTorch).
- Building and maintaining highly scalable, robust, cloud-native data pipelines deployed on AWS (SageMaker Training, Inference, Processing, Pipelines; EMR; Step and Lambda functions)
- Integrating MLOps components into the forecasting products (such as automated model monitoring)
- Optimizing big data processing workflows (Spark, Hadoop)
- Implementing SW engineering best practices (unit and integration testing, linting, CI pipelines, code reviews)
- Working in an international team responsible for creating data products that provide better trading for adidas eCom business (.com and apps)
Data Scientist
Jan. 2020 - Jan. 2021FIOD-Belastingdienst
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In my 12-month-project with FIOD-Belastingdienst, I worked on the following tasks in collaboration with FIOD's data science team (CoDE):
- Developed custom object detection and image classification models using TensorFlow and deployed them as REST APIs using Flask and Docker.
- Created a web application for the end-users - FIOD Image Intelligence - written in JavaScript, NGINX, D3.JS, MapBox.
Professional Doctorate Candidate in Data Science
Jan. 2019 - Jan. 2021Jheronimus Academy of Data Science (JADS)
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In the PDEng Data Science I worked on 7 projects for several companies: 2x ASML, Van Lanschot, TE Connectivity, Heijmans. Check project here.
- Coached/supervised students and professionals.
Lab Monitor (Volunteer)
July 2019Lisbon 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).
Student Researcher
Jan. 2018 - Dec. 2018Institute for Systems and Robotics (ISR-Lisbon)
- Within the Signal and Image Processing group of ISR my research focused on deep learning and anomaly detection in time series data.
- Proposed an approach for time series anomaly detection based on variational autoencoders, recurrent neural networks, and attention mechanisms. This approach is unsupervised, making it suitable for applications where obtaining labels is expensive or time-consuming (fraud detection, medical diagnosis, fault detection, ...).
- Publications
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
Education
Professional Doctorate (PDEng) in Data Science
2019 - 2021Eindhoven University of Technology
Masters and Bachelors in Electrical and Computer Engineering
Sept. 2013 - Nov. 2018Instituto Superior Técnico
Exchange Student
Sept. 2016 - Jan. 2017Université Catholique de Louvain
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
Publications
Google Scholar Citations
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
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.
