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 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 mailing list webpage. Anyone can attend the seminars; no registration is necessary. If you would like to present something, please send us an email.

The seminars are usually held every other 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!

Tuesday, February 2nd 2016, 13h00 - 14h00

José Bioucas-Dias (Instituto de Telecomunicações/IST)



Instituto Superior Técnico - Alameda


Image segmentation is fundamentally a discrete problem. It consists in finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. Very often, the partitions are obtained via integer optimization, which is NP-hard, apart from a few exceptions. We sidestep the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a set of hidden real-valued random fields informative with respect to the probability of the partitions. Armed with this model, and assuming a supervised scenario, the original discrete optimization is converted into a convex problem, which is solved efficiently using the SALSA solver. In the semi-supervised scenario, we are lead to a nonconvex problem which is addressed with alternating optimization. The effectiveness of the proposed methodology is illustrated in simulated and real segmentation problems.


Bio: José Bioucas-Dias received the EE, MSc, PhD, and ``Agregado" degrees from Instituto Superior Técnico (IST), Portugal, in 1985, 1991, 1995, and 2007, respectively, all in electrical and computer engineering. Since 1995, he has been with the Department of Electrical and Computer Engineering, IST, where he is an Associate Professor. He is also a Senior Researcher with the Pattern and Image Analysis group of the Instituto de Telecomunicações, which is a private non-profit research institution. He has introduced scientific contributions in inverse problems, signal and image processing, pattern recognition, optimization, and remote sensing. He is included in Thomson Reuters' Highly Cited Researchers 2015 list.