Khipu General Committee

Khipu 2019 Committee

And a Special Thanks to

Martín Rocamora
Marcelo Fiori
Pablo Cancela
Álvaro Gómez
Ignacio Ramírez
Gregory Randall
Andre Saraiva
Salvador Vazquez
Shakir Mohamed

We would also thank Easy Planners for their assistance in the event's organization.

11-15TH. of November 2019

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Danielle Belgrave


Danielle Belgrave is a Researcher at Microsoft Research Cambridge in The Healthcare Machine Learning Group. She also holds a tenured Research Fellowship (Assistant Professor) at Imperial College London. Her research focuses on developing probabilistic and causal graphical modelling frameworks to understand disease progression over time. The aim of this research is to use machine learning to identify distinct subtypes of disease evolution and to understand the underlying mechanisms of these subtypes so as to develop personalized disease management strategies. She has a BSc in Business Mathematics and Statistics from the London School of Economics and an MSc in Statistics from University College London. She was awarded a Microsoft PhD Scholarship to complete her PhD in Statistics and Machine Learning applied to Health (2010-2013) at The University of Manchester. She received a Medical Research Council (UK) Career Development Award in Biostatistics (2015 – 2020) for the project “Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies”.

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Martín Arjovsky


Martin Arjovsky is PhD at New York University, advised by Léon Bottou. He is from Buenos Aires, Argentina and did his undergraduate and master's in the University of Buenos Aires. He did many internships in different places (Google, Facebook, Microsoft, Université de Montréal, and DeepMind). His master's thesis advisor was Yoshua Bengio, who also advised him during my stay at UdeM. In general he’s interested in the intersection between learning and mathematics, how we can ground the different learning processes that are involved in different problems, and leverage this knowledge to develop better algorithms. Along this line, he’s worked in many different areas of machine learning, including optimization, unsupervised learning, out of distribution generalization, and exploration in reinforcement learning.

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Meire Fortunato


Meire Fortunato is a senior research scientist at DeepMind since 2016, working in Deep Learning and Artificial Intelligence. She is originally from Corbélia, Paraná, Brazil and received her undergraduate and master's degrees in Mathematics at UNICAMP, in Brazil. She holds a PhD in Mathematics from the University of California, Berkeley. Recently, Meire has focused her work to understand the role of memory in agents: how to explicitly test it and how to integrate working and external memory modules in order to tackle different time scale dependencies.

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Federico Lecumberry


Federico Lecumberry was born in Montevideo. He received the B.Sc., M.Sc. and Ph.D. degrees in Electrical Engineering from the Universidad de la República, Uruguay. Currently holds a position as Associate Professor in Signal Processing with the Electrical Engineering Department (Instituto de Ingeniería Eléctrica, Facultad de Ingeniería), at the Universidad de la República, Montevideo, Uruguay. He is also Principal Investigator and Head of the Signal Processing Laboratory at the Institut Pasteur de Montevideo. He works in signal and image processing and machine learning fundamentals and applications to biomedical images and signal and various industrial applications. In the past he worked on geometric partial differential equations in computer vision applied to object segmentation, on disparity computation in stereo images and image and video codification. His research interests include Signal and Image Processing, Computer Vision, Machine Learning and Cryo-Electron Microscopy.

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Pablo Sprechmann


Pablo Sprechmann is currently a senior research scientist at DeepMind working on various aspects of artificial intelligence including deep reinforcement learning, continual learning and memory augmented neural networks. He received the EE and the MSc degrees from the Universidad de la República, Uruguay, in 2006 and 2008, respectively. In 2012 he received the PhD degree in electrical engineering from the University of Minnesota under the supervision of Prof. Guillermo Sapiro. He was a postdoctoral researcher with the ECE Department at Duke University in 2013 working with Prof. Guillermo Sapiro. From 2014 to 2016 he was a postdoctoral researcher at the CILVR (Computational Intelligence, Learning, Vision, and Robotics) Lab at New York University working with Prof. Yann LeCun.

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Mauricio Delbracio


Mauricio Delbracio received the B.Sc degree in electrical engineering from the Universidad de la República (UdelaR), Montevideo, in 2006, and the M.Sc. and Ph.D. degrees in applied mathematics from École Normale Supérieure de Cachan (ENS-Cachan), France, in 2009 and 2013 respectively. He is currently an Assistant Professor with the Department of Electrical Engineering, UdelaR. From 2013 to 2016 he was a postdoctoral researcher with the ECE Department at Duke University. His research interests include image and signal processing, computer graphics, computational imaging, and machine learning. His current research focuses on algorithms, data analysis and applications of machine learning to image and signal processing. In 2016 he was awarded the Early Career Prize from the Society for Industrial and Applied Mathematics (SIAM) Activity Group on Imaging Science in 2016 for his important contributions to image processing.

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Pablo Muse


Pablo Musé received the electrical engineering degree from Universidad de la República, Uruguay, in 1999 and the Ph.D. in applied mathematics from ENS Cachan, France, in 2004. From 2005 to 2006 he was a Senior Researcher with Cognitech, Pasadena, CA, USA, where he worked on computer vision and image processing applications. In 2006 and 2007, he was a Postdoctoral Scholar with the Seismological Laboratory, California Institute of Technology, Pasadena, working on remote sensing using optical imaging, radar and GPS networks. Since 2008, he has been with the Division of Electrical Engineering, Universidad de la República, where he is currently a Full Professor of signal processing. His research interests include machine learning, image restoration and analysis, computational photography and remote sensing.

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Alicia Fernandez


Alicia Fernández is a Full Professor of Signal Processing at the Electrical Engineering Department (Instituto de Ingeniería Eléctrica (IIE)), Facultad de Ingeniería, Universidad de la República. Since 1989, she works at the IIE, in telecommunication and signal processing areas. Her main research interests are signal processing and pattern recognition with focus in biomedical image analysis, biometric identification, anomaly detection and big data analysis.

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Gabriella Rojas


Gabriella Rojas is a software engineer at Google. She previously studied at Cornell University and the Universidad de Chile.

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Diego de Las Casas


Diego de Las Casas is a Research Engineer at DeepMind since 2016, where he has worked with several topics in Deep Learning and Reinforcement Learning. He is currently working in tools for scaling up research in DeepMind and sharing it with the broader community, and he has great interest in how Artificial Intelligence can be leveraged for the greater good. He has a B.Sc degree in Psychology from Universidade Federal de Minas Gerais (UFMG), Brazil, and a M.Sc. degree in Computer Science, also from UFMG.

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José Lezama


José received his MSc and PhD in applied maths from École Normale Supérieure Paris-Saclay. After a postdoc at Duke University, he is now a postdoc and part-time assistant professor at Universidad de la República in Montevideo. José's research passions are in the areas of machine vision and intelligence.