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Universidad San Francisco de Quito

Diego de Robles y Vía Interoceánica

Quito, Ecuador

RIIAA brings together the Latin American artificial intelligence (AI) ecosystem. Emphasizing the interaction between regional actors, as well as the academic and industrial communities, in order to catalyze the development, adaptation and use of AI in the region.


This year, RIIAA will be hosted simultaneously in Mexico City, Mexico, as well as in Quito, Ecuador. Like in previous years, there will be a virtual component to complement the in-person events.


SEPTEMBER 28 - Academic AI

On the first day we will host external speakers who will share their research and efforts in advancing and democratizing AI research.

Teatro Shakespeare, Register 8:30 - 9:00 am

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Applying machine learning to computer vision

9:30 am

Ph.D., Electrical Engineering, University of Manchester, Manchester, U.K M.Sc, Electrical Engineering, University of Manchester, Manchester, U.K Ing. Electrónica y Control, Escuela Politécnica Nacional, Quito, Ecuador Investigador "Programa Prometeo“, SENESCYT (2012-14) Senior Research Engineer, Centro de Investigación y Tecnología de Bosch en Pittsburgh, PA, EE. UU. (2007-2012) Academic Visitor en Institute for Complex Engineered Systems, and INFERLab, Carnegie Mellon University, Pittsburgh, PA, EE. UU. (2007-2012) Postdoctoral Research Associate Sensing, Imaging and Signal Processing Research Group, School of Electrical and Electronic Engineering, University of Manchester (2005-2007). Autor de más de 130 artículos científicos en conferencias y revistas de alto impacto (+3450 citaciones) Posee 26 patentes internacionales relacionadas con tecnología en EE. UU. y Europa Senior Member IEEE, Presidente de la Sección de IEEE Ecuador (2018-2019). IEEE R9 Educational Activities Commitee Chair (2022) Premio a la trayectorio Profesional IEEE Ecuador (2021) Miembro de la Academia de Ciencias Ecuador General and Technical Chair en varias conferencias IEEE


Model resets in supervised and reinforcement learning


Aaron Courville is an Associate Professor in the Department of Computer Science and Operations Research at the Université de Montréal. He received his PhD from the Robotics Institute, Carnegie Mellon University. He is one of the early contributors to Deep Learning, and is a founding member of the Mila AI institute and a fellow of the CIFAR program on Learning in Machines and Brains. Together with Ian Goodfellow and Yoshua Bengio, he co-wrote the seminal textbook on Deep Learning. His current research interests focus on the development of DL models and methods. He is particularly interested in deep generative model and multimodal ML with applications such as computer vision and natural language processing. Aaron holds a CIFAR Canadian AI chair and his research has been supported in part by Microsoft Research, Samsung, Hitachi and a Google Focussed Research Award.


Building Artificial Visual Recognition Systems that Learn from Text and Images

12:00 pm

Vicente Ordóñez-Román is Associate Professor in the Department of Computer Science at Rice University. His research interests lie at the intersection of computer vision, natural language processing and machine learning. His focus is on building efficient visual recognition models that can perform tasks that leverage both images and text.



Evolving the Global LatinX in AI (LXAI) Ecosystem

Laura is a scientist and engineer turned serial entrepreneur and startup advisor. She leads tech social impact and ethical AI development as the founder and Managing Partner of Accel Impact Organizations, including Accel AI Institute, Latinx in AI (LXAI), and Research Colab. Her academic background is in Biology, Physical Science, and Human Development. Her current research interests include reducing bias data representations in machine learning models, the effects of artificial intelligence development for developing countries, and paralleling biological and synthetic neural networks seen in mycology, entomology, and computational science.

sara hooker.jfif

The Shape of Our Ideas: Lessons from Computer Science History

15:00 pm

Sara Hooker leads Cohere For AI (C4AI), a non-profit research lab that seeks to solve complex machine learning problems. C4AI supports fundamental research that explores the unknown, and is focused on creating more points of entry into machine learning research. Before Cohere, Sara was a research scientist at Google Brain working on training models that go beyond top line metrics to fulfill multiple desired criteria -- interpretable, efficient, fair and robust. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good. She grew up in Africa, in Mozambique, Lesotho, Swaziland, South Africa, and Kenya.


Perspectives on knowledge acquisition and mobilization with neural networks

16:30 pm

Hugo Larochelle is a Research Scientist at Google Brain and lead of the Montreal Google Brain team. He is also a member of Yoshua Bengio's Mila and an Adjunct Professor at the Université de Montréal. Previously, he was an Associate Professor at the University of Sherbrooke. Larochelle also co-founded Whetlab, which was acquired in 2015 by Twitter, where he then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, he was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. He is an Editor-in-Chief and founder for the Transactions on Machine Learning Research and a member of the NeurIPS and ICML Boards. Finally, he has a popular online course on deep learning and neural networks, freely accessible on YouTube.

Panel Academic day


Aaron, Sara, Hugo, Vicente, Diego, Laura

17:30 pm

Our last day is dedicated to a "summer" school, where students can learn from area experts about various AI topics.


SEPTEMBER 29- Applied AI

In our second day we will host a series of panels exploring the use of AI in various sectors. We have invited key local players from each of these sectors to discuss challenges and opportunities.

Experiences in Banking and Financial Services






                  Sergio Sotomayor                                      Daphné Repain                               Drichelmo Tamayo


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Advanced Analytics Lead

Produbanco Grupo Promerica

VP Customer Experience

Banco Pichincha

Advanced Analytics Lead


9:00 -10:00 am

Experiences in Retail and Services






               Juan Sebastián Araujo                                 Amarilis Loor                                       Andrés Abad



Head of Data Science and Analytics

Digital Transformation Regional Manager


Metropolitan Touring
Grupo KFC

10:00-11:00 am


Entrepreneurship based on Artificial Intelligence












Co-Founder / Manager Director


Leslie Jarrín

Francisco Gallegos

Fernando Moncayo

Government and Artificial Intelligence

Juan Carlos Palacios


Gisela Montalvo


Lorena Moreno

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Subsecretario de Gestión de Información, Investigación y Evaluación

Directora Ejecutiva 

Coordinadora de  Métricas y Análisis de la Información

Secretaría Técnica Ecuador Crece sin Desnutrición Infantil

The afternoon of the 29th  




Salón Azul USFQ

Also known as CirCos, they are self-organized, informal, interactive discussion sessions inspired by Birds of Feather (BoF). CirCos provides a space for interested attendees to meet in small groups to exchange ideas and form allies on issues of common interest. These topics may be directly or indirectly related to the topics covered during the RIIAA conference.

Hidden Geographies in AI: cheap labor and new subjugations of female bodies in the formation and mitification of global Artificial Intelligence.


In this CirCo, we will discuss and expand on the already exhausted discussion about the ethics of Artificial Intelligence.
We will analyze the geography of the territories where this technology is created and also its socio-economic, religious and political characteristics, perhaps from the debates around data and its classification, the place of labor rights in the face of automation, to a mapping of the related power relations at each point of the value chain of artificial intelligence.
It is important to not just talk about certain technical achievements of algorithms, and to begin to understand how artificial intelligence is created in the broadest sense, part of that means analyzing what the natural resources are in power, the energy it consumes, the hidden work of the supply chain, and the vast amount of data that is extracted from every platform and device we use every day.

Some recommended reading for this CirCo are:


Rosalind Williams, wrote a text back in 1993 on the "Cultural Origins and Environmental Implications of Large Technological Systems." . She talks about highways as corridors of power that divide as much as they connect.
I would like to think that code, as infrastructure, like these lines function in the same way.

Catherine D'Ignazio, and her book called "Data Feminism" .

Kate Crawford with her book "Atlas of Artificial Intelligence " .
Carissa Véliz with her book "Privacy is Power ".

Unfortunately all the readings are in English, I will try to find the Spanish translations.

In this space, it would be interesting to find alternative ways, collaborations, to codify methods to dismantle inequality and injustice. The world needs more decoders !

Feel free to come and raise your voice =)


This space will be hosted by Diana Mosquera.

Special thanks to Pablo Escudero for his support for helping me shape my ideas for this CirCo.

To Francisco Gallegos and Diversa for the support and help in allowing me to explore these topics .

To Pierre Belanger for suggesting decoding readings.

To RIIAA for allowing us to give a space to talk about this important topic


How to start a career in AI


Organizing Committee for RIIAA - Quito

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Eduardo Alba


Ruben Villegas

Google Brain
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Belén Sánchez


Pablo Samuel Castro

Google Brain
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Diana Mosquera


SEPTEMBER 30 - Summer School

Our last day is dedicated to a "summer" school, where students can learn from area experts about various AI topics.


Introduction to Auto-Machine Learning

Belén Sánchez

We will take a tour of the life cycle of a solution based on machine learning and see how automation can speed up this process through DataRobot.

This workshop will allow you to understand basic concepts required to build predictive models.

Requirements for participants


Morning Session

9:00-10:30 am

Edificio Hayek USFQ


Introduction to Reinforcement Learning

Pablo Samuel Castro

9:00-12:00 pm


In this course we will learn the definition of reinforcement learning, learn to recognize the type of problems for which it is suitable, and the mathematical bases of the most common techniques. Finally, with the use of Google Collaboratory we will learn how to code a reinforcement learning agent with neural networks.

Tools that we are going to use

Any browser, but ideally Google Chrome

Requirements for participants

 Knowledge of linear algebra and probability. Computer and internet connection ideal, but not necessary.

Design and Artificial Intelligence

Diana Mosquera

9:00-11:00 am


Design, machine learning, and user research are one of the most impactful intersections in technology and society.
Come join us for a conversation and interactive workshop on how to use design for the public good and solve societal problems with AI as a tool. 

Tools that we are going to use

Any browser, but ideally Google Chrome, if you don't have a computer, no problem we work in pencil and sheets of paper

Requirements for participants

Knowledge of figma o any  other design tool. Computer and internet connection ideal, but not necessary.


Evolution in Artificial Intelligence


Daniel Riofrío


We will take a tour through the history of Artificial Intelligence. We will discuss important concepts such as what we understand as intelligence and we will analyze historical events in both mathematics and computer science that allow us to create models of the world. As part of the model building we will talk about different paradigms that allow modern artificial intelligence applications to be built. This workshop will allow you to reflect on different types of models, their advantages and disadvantages, and perhaps open your eyes to the future of Artificial Intelligence.

Requirements for participants

A personal computer, access to a Prolog interpreter and a Python 3 interpreter.


Afternoon Session

Introduction to Transformers

Pablo Samuel Castro



The transformer architecture has caused a revolution in the world of artificial intelligence, and has allowed the development of models that generate language (GPT-3, PaLM), images (Dall-E, Parti), and music (Music Transformer, MuseNet ). In this course we will analyze the components of this architecture, including the fundamental components such as the concept of "attention". With the use of Google Collaboratory we will learn how to code a neural network that uses Transformers for language prediction.

Tools that we are going to use

Any browser, but ideally Google Chrome

Requirements for participants

Knowledge of linear algebra and probability. Computer and internet connection ideal, but not necessary.


Artificial Intelligence for Artist and Creators

Diana Mosquera


Scaling Machine Learning in Distributed Environments with Spark MLlib


Representation is a rather interesting area within the world of Artificial Intelligence, but not so much talked about. 
What is behind the concepts? the meaning? Why should this be of interest to us when using tools like AI? How accurate are these representations?
How do models like Stable Diffusion use these representations to convert images to text?
What does this have to do with art? 
This workshop is aimed at people interested in how concepts are formed, artists and creators who work with representations in everyday life.

Tools that we are going to use

Any browser, but ideally Google Chrome

Requirements for participants

Computer and internet connection ideal, but not necessary.


Israel Pineda


In this course we analyze the different execution environments on which we can execute training and inference processes of machine learning algorithms. We use Apache Spark as a distributed processing tool and experiment with different algorithms for data analysis and interpretation.

Tools we are going to use

Spark 3

Requirements for participants

Computer, Internet connection


Artificial Intelligence Applied to Business


Anita Sancho


In this workshop we will see how to identify business opportunities that arise within the latest innovations in the Artificial Intelligence industry. We will learn what the latest innovations are, we will go through a product design methodology and we will practice how to detect problems or better said, business opportunities with the fishbone methodology. We have reached a point where Artificial Intelligence can solve problems caused by Artificial Intelligence.

Students will require their laptops to take notes and internet connection to develop the activity

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