I am currently a PDEng Trainee in Data Science at Eindhoven University of Technology (TU/e), where I develop data science projects for industry. Most of the time I do machine learning and I am broadly interested in the fields of data science and artificial intelligence.
My interests consist of applying cutting-edge AI techniques to solve difficult problems raised in the context of real-world applications. I have been working on AI applications involving time series, text, images, and videos, in the context of areas like energy, healthcare, manufacturing, and AI for social good.
Prior to joining TU/e as a PDEng trainee, I completed a M.Sc. degree in Electrical and Computer Engineering at Instituto Superior Técnico (University of Lisbon), where I did my master thesis on deep learning. Meanwhile, I developed applied research on deep learning within the Signal and Image Processing Group at the Institute for Systems and Robotics (ISR-Lisbon).
If you want to know more about my work I invite you to check out the Projects section. Please feel free to discuss and share any comments about them with me ;)
I have been developing data science projects for the industry. Check out the project section.
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. [Website]
Developed applied research within the Signal and Image Processing Group on deep learning for time series data, in the context of energy and healthcare applications.
In particular, my research was in the astonishing area of deep learning and its applications to sequential data. Specifically, it was focused on investigating how deep generative models (such as variational autoencoders), recurrent neural networks and attention mechanisms can be successfuly used for time series data modeling and anomaly detection.
Conducted a machine learning project on load time series forecasting.
Designed and projected medium/low voltage networks.
Proposes a general, unsupervised, and scalable, approach for anomaly detection in time series data using a cutting-edge deep learning approach.
Anomaly detection in solar generation time series using a variational autoencoder model and recurrent neural networks.
Introduced the Variational Self-Attention Mechanism.
J. Pereira, M. Silveira, Unsupervised Anomaly Detection in Energy Time Series Data using Variational Recurrent Autoencoders with Attention
Learning representations of ECG time series for anomaly detection.
J. Pereira, M. Silveira, Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection
Proposes a variational recurrent autoencoder for learning representations of electrocardiogram (ECG) sequences. Downstream task is anomaly detection.
J. Pereira, M. Silveira, Unsupervised Representation Learning and Anomaly Detection in ECG Sequences
Topics: Artificial Intelligence, Machine Learning, Digital Signal Processing, Image Processing and Computer Vision, Robotics, Control Systems
Courses: Machine Learning, Image Processing and Vision, Instrumentation and Sensors, Electric Power Systems.