Latin American Meeting
In Artificial Intelligence

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The best of AI for Latin America

Artificial Intelligence has the potential to dramatically improve the lives of people and increase the prosperity of businesses, but solving a problem as hard as intelligence will require a diversity of thought and the best minds from every corner of the globe. Our mission is to support the advancement of Latin American talent, research, and companies in AI through an annual event.

Khipu 2019 will be take place Nov 11-15 at the Facultad de Ingeniería at the Universidad de la República in Montevideo, Uruguay. The primary goals of the event are:
• To offer training in advanced machine learning topics, such as deep learning and reinforcement learning.
• To strengthen the machine learning community by fostering collaborations between Latin American researchers, and creating opportunities for connections and knowledge exchange with the broader international community.
• To grow awareness around how AI may be used for the benefit of Latin America

In the hopes of inspiring wide and diverse participation, there will be no registration fees for accepted students and Khipu will provide financial assistance for travel expenses to select applicants. The event format is largely inspired by the success of our friends at the Deep Learning Indaba. We are very thankful to our sponsors and speakers for their invaluable support.


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29th mar 2019
Application Open
28th june 2019
Application Deadline
12th august 2019
Acceptance Notifications
11TH-15TH of November 2019

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Jeff Dean

Google AI

Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow and SVP of Google AI and Health, overseeing Google’s research and healthcare teams. He and his collaborators are working on machine learning and AI techniques for speech recognition, computer vision, language understanding, robotics, healthcare and various other tasks. He is a co-designer and co-implementor of many software systems, including several generations of Google query serving and advertising systems, MapReduce, BigTable, and Spanner, and the open-source TensorFlow system for machine learning.

Jeff received a Ph.D. in Computer Science from the University of Washington in 1996 and a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the U.S. National Academy of Engineering, and the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award.


Yoshua Bengio


Yoshua Bengio is a Professor at the University of Montreal, and the Scientific Director of both Mila (Quebec’s Artificial Intelligence Institute) and IVADO (the Institute for Data Valorization). He is Co-director (with Yann LeCun) of CIFAR’s Learning in Machines and Brains program. Bengio received a Bachelor’s degree in electrical engineering, a Master’s degree in computer science and a Doctoral degree in computer science from McGill University.

Bengio’s honors include being named an Officer of the Order of Canada, Fellow of the Royal Society of Canada and the Marie-Victorin Prize. His work in founding and serving as Scientific Director of the Quebec Artificial Intelligence Institute (Mila) is also recognized as a major contribution to the field. Mila, an independent nonprofit organization, now counts 300 researchers and 35 faculty members among its ranks. It is the largest academic center for deep learning research in the world, and has helped put Montreal on the map as a vibrant AI ecosystem, with research labs from major companies as well as AI startups.

Yoshua Bengio, Geoffrey Hinton and Yann LeCun shared this year's Turing Award, the world's top prize in computer science, for their breakthroughs in Artificial Intelligence.


Nando de Freitas


Nando was born in Zimbabwe, with malaria. He was a refugee from the war in Mocambique. Thanks to his parents getting in debt to buy him a passport from a corrupt official, he ended up living in a small volcanic rock hut in Madeira, Portugal, without water and electricity, before the EU got there, and without his parents who were busy making money to pay their debt.

At the age of eight, he joined his parents in Venezuela and began school in Catia; a neighbourhood of Caracas. He moved to South Africa after high-school and sold beer illegally in black-townships for a living until 1991; learning to solve ODEs in his free time. Apartheid was the worst thing he ever experienced.

He obtained his BSc in electrical engineering and MSc in control at the University of the Witwatersrand, under the guidance of amazing teachers and friends, and where he strived to be the best student to prove to racists that anyone can do it. He was privileged to obtain a PhD on Bayesian methods for neural networks at Trinity College, Cambridge University, thanks to scholarships by benevolent people who donate and invest in education.

He did a postdoc in Artificial Intelligence at UC Berkeley, and became a Professor at the University of British Columbia, Canada, in 2001 and subsequently at the University of Oxford, UK, in 2013. In 2017, he joined DeepMind full-time as a principal scientist to help with the vision of solving intelligence so that future generations can have a better life. Nando is also a Senior Fellow of the Canadian Institute for Advanced Research and has been the recipient of several academic awards.


Lucia Specia

Imperial College London

Lucia Specia is Professor of Natural Language Processing at Imperial College London (since 2018). Her research focuses on various aspects of data-driven approaches to language processing, with a particular interest in multimodal and multilingual context models and work at the intersection of language and vision. Her work can be applied to various tasks such as machine translation, image captioning, quality estimation and text adaptation. In the past she worked as Professor of Language Engineering at the University of Sheffield (2012-2018), Senior Lecturer at the University of Wolverhampton (2010-2011), and research engineer at the Xerox Research Centre, France (2008-2009, now Naver Labs). She received a PhD in Computer Science from the University of São Paulo, Brazil, in 2008.


Oriol Vinyals


Oriol Vinyals is a Sr Staff Research Scientist at Google DeepMind, working in Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, BBC, etc., and his articles have been cited over 38000 times. Some of his contributions are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.


Chelsea Finn

Google AI / Stanford

Chelsea Finn is a research scientist at Google AI and a post-doctoral scholar at UC Berkeley. In September 2019, she will be joining Stanford's computer science department as an assistant professor. Finn's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, Finn has developed deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for scalable acquisition of nonlinear reward functions, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelors degree in Electrical Engineering and Computer Science at MIT. Her research has been recognized through an NSF graduate fellowship, a Facebook fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. With Sergey Levine and John Schulman, Finn also designed and taught a course on deep reinforcement learning, with thousands of followers online. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across three universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.


Kyunghyun Cho

New York University

Kyunghyun Cho is an assistant professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at University of Montreal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.


Luciana Ferrer

Universidad de Buenos Aires / CONICET

Luciana Ferrer is a researcher at the Computer Science Institute, from the National Scientific and Technical Research Council (CONICET) and the University of Buenos Aires (UBA), Argentina. Prior to her current position, Luciana worked at the Speech Technology and Research Laboratory, SRI International, USA. Her current research interests include speaker and language identification, mental state detection, and pronunciation scoring for second language learning. Luciana received the B.S. degree from the University of Buenos Aires, Argentina, in 2001, and her Ph.D. degree from Stanford University, USA, in 2009.


Soumith Chintala

Facebook AI Research

Soumith Chintala works in artificial intelligence as an engineer, primarily focusing on building tools such as PyTorch. He dabbles with other research in generative modeling, robotics and stuff.


David Lopez-Paz

Facebook AI Research

I’m a research scientist at Facebook AI Research, Paris, France. Prior to joining Facebook in 2016, I received my PhD from the Max Planck Institute for Intelligent Systems and the University of Cambridge, advised by Bernhard Schölkopf and Zoubin Ghahramani. Before that, I obtained a degree in computer science from the Universidad Autonoma de Madrid in 2011. During my studies, I interned at various industries, including the European Space Agency, Google Research and the Red Bull Formula 1 team.

The goal of my research is to develop theory and algorithms for unsupervised causal inference, and their use to build machines which are able to reason and learn about the world using less data.


Danielle Belgrave

Microsoft Research / Imperial College London

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”.


Bianca Zadrozny

IMB Research, Brazil

Bianca Zadrozny is a research manager at IBM Research Brazil, leading the Natural Resources Analytics group. The group's mission is to conduct research projects in data-driven analytics for decision making in the areas of oil&gas and mining, with a great focus in developing new machine learning workflows to aid geoscientists in the discovery of natural resources. Bianca got her PhD in Computer Science from University of California, San Diego in 2003. After that, she has worked as a researcher at IBM T.J. Watson Research Center, New York and as a professor at Federal Fluminense University, Brasil. In 2011, she joined IBM Research Brazil. Bianca is an active researcher in the machine learning and data mining communities, having published more than 40 papers in these areas. She has served in the editorial board of the Journal of Machine Learning Research (JMLR) and the Data Mining and Knowledge Discovery (DMKD) journal and also in the program committees of conferences such as ICML, KDD, ECML, SDM and SBBD.


Guillermo Sapiro

Duke University

Guillermo Sapiro was born in Montevideo, Uruguay, on April 3, 1966. He received his B.Sc. (summa cum laude), M.Sc., and Ph.D. from the Department of Electrical Engineering at the Technion, Israel Institute of Technology, in 1989, 1991, and 1993 respectively. After post-doctoral research at MIT, Dr. Sapiro became Member of Technical Staff at the research facilities of HP Labs in Palo Alto, California. He was with the Department of Electrical and Computer Engineering at the University of Minnesota, where he held the position of Distinguished McKnight University Professor and Vincentine Hermes-Luh Chair in Electrical and Computer Engineering. Currently he is a James B. Duke Professor with Duke University. G. Sapiro works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. He has authored and co-authored over 450 papers in these areas and has written a book published by Cambridge University Press, January 2001.

He was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991, the Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992, the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, the National Science Foundation Career Award in 1999, and the National Security Science and Engineering Faculty Fellowship in 2010. He received the test of time award at ICCV 2011. G. Sapiro is a Fellow of IEEE, SIAM, and the American Academy of Arts and Sciences (AAAS). G. Sapiro was the founding Editor-in-Chief of the SIAM Journal on Imaging Sciences.


René Vidal

John Hopkins University

Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. He has secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos.


Enzo Ferrante

Universidad Nacional del Litoral / CONICET

Enzo completed his PhD in Computer Sciences at Université Paris-Saclay (CentraleSupeléc / INRIA) in France, and worked as postdoctoral researcher at Imperial College London, in the UK. He returned to Argentina in 2017, where he is currently a researcher at CONICET, AXA Research Fund fellow and lecturer in Digital Image Processing at Universidad Nacional del Litoral, working in the sinc(i) lab ( His research interests span both artificial intelligence and biomedical image analysis, with focus on deep learning methods


Martín Abadi

Google Research

Martín Abadi is a research scientist at Google, in the Google Research team. He is also a Professor Emeritus at the University of California at Santa Cruz, where was a Professor in the Computer Science Department till 2013. He has held an annual Chair at the Collège de France, has taught at Stanford University and the University of California at Berkeley, and has worked at Digital’s System Research Center, Microsoft Research Silicon Valley, and other industrial research labs. He received his Ph.D. at Stanford University in 1987. His research is mainly on computer and network security, programming languages and systems, and specification and verification methods. It has been recognized with the Outstanding Innovation Award of the ACM Special Interest Group on Security, Audit and Control and with the Hall of Fame Award of the ACM Special Interest Group on Operating Systems, among other awards. He is a Fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Science (AAAS), and a member of the National Academy of Engineering (NAE). He holds a doctorate honoris causa from École normale supérieure Paris-Saclay.