Seminars


The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the group webpage. Anyone can attend the seminars. If you would like to present something, please send us an email.

The seminars are usually held every other Thursday (previously on Tuesday), from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Thursday, May 2nd 2019, 13h00 - 14h00

Mariana Almeida (Feedzai)

Fraud Prevention with Deep Learning models

PA2

Instituto Superior Técnico - Alameda

Abstract:

Feedzai is a scale up company with one mission: making banking and commerce safe. For that purpose, Feedzai develops methods for fraud prevention that should simultaneously be accurate, scalable and work within low latencies. In this talk will cover Feedzai’s research challenges, focusing on the use of neural networks for fraud prevention.

For a long time, best solutions for fraud detection combined tree-based methods and handmade profile features that represented the user behaviour (ex: number of transactions of a user within the previous week). This approach, however, require handmade engineered features which take time to be computed and which consumes a lot of memory when in production. More recently, with the use of Deep Learning (DL) models with RNNs, we are able to train end-to-end models that encapsulate the feature engineering step in the model itself. These models not only allow for skipping the feature engineering step, but also lead to improvements in typical baseline metrics for fraud prevention, such as transaction recall at 1% false positive rate.

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Bio: Mariana Almeida is a Senior Data Scientist at Feedzai Research team, applying and developing machine learning techniques to stop fraud in financial transactions. She obtained a PhD degree in the Electrical Engineering Department of IST, in 2010, for her work on image processing. Before joining Feedzai, Mariana did a Postdoc on image processing and worked for 4 years on natural language processing at Priberam Labs. She received the Vidigal award 2004/2005 and a honorable mention at the 2010 Portuguese IBM Scientific Prize.

Eventbrite - Fraud Prevention with Deep Learning models