Conoce a los ganadores de los Premios de Investigación de Google para América Latina

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Por 5° año consecutivo, Google® avisó hoy una nueva generación de 27 examinadores en el área de Ciencias de la Computación que recibirán, durante los próximos dos años, 600 mil dolares para llevar a cabo sus proyectos. Desde que lanzamos los Premios Latin American Research Awards (LARA) en el año 2013, el software ha beneficiado a 46 proyectos y a mas de 100 investigadores, incorporando estudiantes de maestría y doctorado y sus profesores.

Este año recibimos 281 proyectos de 9 paises de América Latina. Es representativo y muy positivo el aumento en el numero de examinadores de toda la región enfocados al repaso del aprendizaje automático, un campo que nos acepta educar a las maquinas a estudiar sin que éstas sean explícitamente programadas. De los 27 proyectos elegidos, 14 se enfocan en aprendizaje automático.

Por 1ª vez, realizamos el aviso de los ganadores en el Campus São Paulo, un espacio de Google® dedicado a los emprendedores. De este modo, intentamos fomentar una discusión entre los académicos, la sociedad de startups y directivos de universidades sobre uno de los enormes motores para la innovación en el que, como región, tenemos profundo espacio para mejorar: el acercamiento entre la academia y la sociedad de emprendedores.

Además de creatividad y capacidad empresarial, las industrias de tecnología mas exitosas e innovadoras del planeta tienen en común una alta intensidad de conocimiento. Cuanto mayor sea la intensidad de conocimiento de una empresa, mayor será su capacidad para escalar sus negocios. Hoy entendemos que América Latina tiene varios de los ingredientes necesarios para inventar negocios que puedan transformar el mundo. Propiciar el diálogo y el intercambio de conocimiento entre todas las partes nos admitirá cumplir ese potencial como región.

Los ganadores de los Premios LARA 2017 y sus organizaciones son:

Marcos André Gonçalves
Clebson C. A. de Sá
Universidade Federal de Minas Gerais, Brasil
Optimizing Ensembles of Boosted Additive Bagged Trees for Learning-to-Rank
The goal is to optimize a ranked list of documents related to specific information needs by training a model with documents already defined as relevant by specialists  using a “Learning-to-Rank”  process based on a combination of Machine Learning techniques.
Pedro Olmo Stancioli Vaz de Melo
Túlio Corrêa Loures
Universidade Federal de Minas Gerais, Brasil
Discussion-Based Entity Representation
The goal is to develop a method for extracting relevant information through comments from discussions made online. Eventually will create a herramienta that could create a summary about comments from the same topic, and group them with other related subjects.
Jorge Arigony-Neto
Guilherme Tomaschewski Netto
Universidade Federal do Rio Grande, Brasil
Low-cost autonomous stations for measuring the impacts of climate change on glaciers
The goal is to record glacier weather parameters and improve the resolution of glacier melt measurements through low-cost sensors.
Marcus Ritt
Alex Gliesch
Universidade Federal do Rio Grande do Sul, Brasil
Heuristic algorithms for fair land distribution and districting problems
The objective is to develop a computational method for dividing parcels of lands into smaller lots considering geographical, political and equitable qualities.
José Correa
Raimundo Sanoa
Universidad de Chile, Chile
Adaptive and Personalized Sequential Posted Prices
The goal is to design welfare maximizing mechanisms in an online setting. Such mechanisms should make irrevocable decisions when faced to a customer. Therefore, to achieve good performance, they should incorporate two basic ingredients: personalization meaning that different customers may be treated differently; and adaptivity meaning that the treatment of a customer may depend on the acquired knowledge until that point.
Gonzalo Navarro
Patricio Huepe
Universidad de Chile, Chile
Engineering Compressed Random Access Memories
This proposal aims at creating a layer between programs and the RAM memory they use so that all the stored data is automatically compressed in a way that is transparent to the program. In this way, the vigente memory offered by the device is virtually expanded. While such a layer may introduce some performace overhead, we expect it to be much lower than the one introduced by virtual memory systems, which resort to external memory. Further, it can be used in devices with no external memory, like cellphones, sensors, and other low-end devices.
Edgar Emmanuel Vallejo Clemente
Kevin Islas Abud
Tecnológico de Monterrey, México
Predicting Zika epidemics using social and vectorial contact networks (project extension)
The main goal is to develop a method that could predict outbreaks and infections of the Zika virus through human-mosquito simulated interactions.
Mirko Zimic
Jorge Coronel
Universidad Peruana Cayetano Heredia, Peru
Improvements to facilitate the diagnostics of tuberculosis in low resources settings using mobile technologies and artificial intelligence
This project aims to improve a faster detection of tuberculosis (within dos or tres days) through a computational system which could have an online data analysis system to aid the diagnosis.
Luis Carlos González Gurrola
Ricardo Manuel Carlos Loya
Universidad Autónoma de Chihuahua, México
Learning Roadway Surface Disruptions patterns to improve transportation Infrastructure
This project objective is to develop a system for detecting road anomalies through drivers’ moviles sensors and GPS. Also, with machine learning, classify and rank the data of the roads based on size, severity, damage, time unattended, etc. With this, correspondent offices can focus their efforts, and resources more efficiently.
Moacir Ponti
Patricia Bet
Universidade de São Paulo, Brasil
Mobile inertial sensors for fall risk screening and prediction
This project aims to do a research about the use of movement sensors from mobile devices for fall risk prediction in the elderly. The developed methodology could be implemented in mobile platforms such as moviles or smartwatches. Sensors represent a viable monitoring option aiding carers and health professionals.
Fernando Magno Quintão Pereira
Junio C. Ribeiro da Silva
Universidade Federal de Minas Gerais, Brasil
Intelligent DVFS
This project aims to create a prototype for reducing energy consumption of Android® applications at a minimum price in program’s performance. This prototype will use reinforcement learning to adapt itself to the different ways in which those applications can be used.
Juan Pablo Galeotti
Iván Arcuschin Moreno
Universidad de Buenos Aires, Argentina
Evolutiz: Multi-objective Test Generation for Testing Evolving Android® Applications
This project aims to develop an open source herramienta for testing new Android® applications, helping developers to identify errors or missing functionality. In addition, it intends to create a publicly available database of defects in Android® applications.
Anderson Rocha
José Ramon Trindade Pires
Universidade Estadual de Campinas, Brasil
Automated Data­-Driven Screening of Diabetic Retinopathy – Extension
In this research, we aim to design methods to recognize discriminative patterns of diabetic retinopathy stages, providing an advanced and robust severity decision; and incorporate such information into a final higher-level (and more refined) decision of referable DR. We also intend to explore possible forms of understanding the decisions taken by the devised solutions toward accountable decision-making methods.
Pablo Arbelaez
Andres Romero
Universidad de los Andes, Colômbia
Learning Dynamic Action Units for Three-dimensional Facial Expression Recognition
Automated understanding of facial expressions is a elemental step towards high-level human-computer interaction. This project plans to model human facial expressions through the analysis of temporal variations in the pattern of activations of three-dimensional Action Units. The automated facial expression analysis opens the door to multiple application domains beyond emotion classification, such as pain, drowsiness, intoxication, facial unspoken language, depression, or lie detection.
Rodrigo Coelho Barros
Jônatas Wehrmann
Pontifícia Universidade Católica do Rio Grande do Sul, Brasil
Order Embeddings and Character-level Convolutions for Multimodal Retrieval and Synopsis Generation
After a year of research, the team is now able to retrieve images that semantically match a query written in natural language in a very efficient way, as well as retrieving pre-stored descriptions given a query image. For the second year, the project aims to make use of this approach for being capable of retrieving videos given textual descriptions, as well as automatically describing them in natural language.
César Rennó-Costa
Ana Claudia Costa da Silva
Universidade Federal do Rio Grande do Norte, Brasil
Bringing sleep research into the realm of machine learning: optimization of a biologically-sound deep learning SLAM algorithm through offline self-organization of vertices
This project is inspired on the biology of sleeping as an activity of self-organization, aiming to apply it to machine learning improvements. Sleeping can optimize deep learning networks.
Cristina Nader Vasconcelos
Felipe Moure Cícero
Universidade Federal Fluminense, Brasil
Skin lesion classification, segmentation and dermoscopic patterns detection using deep learning
This project aims to develop new herramientas of skin image analysis which could make automated detection and classification (as a benign or malignant) of a skin lesion which would aid a medical diagnosis for early detection of skin cancer.
Eduardo Alves do Valle Junior
Michel Fornaciali
Universidade Estadual de Campinas, Brasil
Reliable Automated Melanoma Screening for the Real World
This project aims to speed-up real-world adoption of computer-aided melanoma screening, both by enhancing the Machine Learning models used to detect the disease, and by interacting with doctors to identify and remove barriers that prevent the adoption of the technology.
Hiram Eredín Ponce Espinosa
José Guillermo González Mora
Universidad Panamericana, Mexico
Transfer Learning Using Artificial Hydrocarbon Networks: A Case Study in Robotics
This research project aims to design a strategy using previous knowledge to solve new but parecido problems quickly and effectively (transfer learning) based on artificial hydrocarbon networks. It will be implemented on robots for task learning. The research project is part of an ongoing research for developing a biomechanical robot for rescuing.
Teodiano Freire Bastos-Filho
Alexandre Luís Cardoso Bissoli
Universidade Federal do Espírito Santo, Brasil
Multimodal Assistive Domotics Including Augmentative and Alternative Communication
Last year, this project developed a new assistive system to be used by people with severe motor disabilities making them able to dominio casa devices and communicate with people around or through a movil right from his/her wheelchair and the signals captured from muscles or eyes. In the current year, volunteers will take casa the system to test. To make this possible, this year will be configured the system for each volunteer, and proposed a new device controller that will transmit the information to the casa devices by internet.
Rodrigo F. Cádiz
Andrés Aparicio
Pontificia Universidad Católica de Chile, Chile
Auditory graphs: conveying data through sound for the visually-impaired
This project’s main goal is to convey quantitative information contained in visual graphs via sound, a technique called sonification, and when particularly applied to graphs, known as auditory graphs. This approach would allow blind or visually-impaired people to access and comprehend this information, otherwise only available in a visual form. This approach has many advantages over the standard method of tactile-graphs, as they are easy to implement with general-purpose computer equipment available today for educational purposes. Of particular interest to us is the development of herramientas to be used along with widespread web(www) technologies such as images search engines or spreadsheets. We would like to add the capability of not only seeing the data using these tools, but also to hear it.
Anselmo Frizera-Neto
Andrés Alberto Ramírez-Duque
Universidade Federal do Espírito Santo, Brasil
Multimodal Interaction Environment based on Computer Vision and Robotic Device for Assisting the Diagnosis of Children with Autism Spectrum Disorder
Although there is no cure for autism spectrum disorder (ASD), intensive and early intervention is crucial for increasing child’s level of functioning in daily life. Many research groups are working on technology-based diagnosis and intervention herramientas for ASD. Nevertheless, the real benefits of their use in systematic clinical practices are still inconclusive. This research aims at developing an open technology-based herramienta to support clinicians in the diagnosis and therapeutic interventions for children with ASD. A smart room composed of a robotic device and a multi-camera computer vision setup for analyzing the behaviors of children with ASD is currently under development. In this manner, the researchers seek the development of herramientas to aid clinicians identify risk factors in children with ASD and provide resources for the next-generation of therapeutic interventions. This work is developed by a multidisciplinary team composed of clinical professionals (neuro-pediatricians and psychologists) and experts in biomedical engineering.
Wagner Meira Junior
Roberto C. S. N. P. Souza
Universidade Federal de Minas Gerais, Brasil
Hot Spot Mining from Case-Control Trajectories
The goal is to detect hotspots, that is, regions where the chance of occurrence of a target event (e.g., being infected by a disease) is higher compared to the rest of the area under analysis, based on trajectories recorded by personal devices.
Winston S. Percybrooks
Pedro Juan Narvaez Rosado
Universidad del Norte, Colômbia
Towards large scale, intelligent, computer-aided auscultation for remote primary-care settings
This proposal aims to build a low-cost, mobile phone-based digital stethoscope capable of recording and labeling heart sounds. It also looks to use Machine Learning to develop a method for automatic diagnosis of heart-related conditions from the recorded sounds. We expect the system would be suitable for use in telemedicine scenarios, particularly to support non-specialized medical practitioners at remote locations.
Sidarta Ribeiro
Ana Raquel Torres
Universidade Federal do Rio Grande do Norte, Brasil
Non-semantic graph analysis for automated assessment and early diagnosis of cognitive disabilities in the school environment
The goal consist in creating a low-cost automated assessment of cognitive disabilities that impair a full academic development of young students, using herramientas from natural language processing to contribute early detection and adequate intervention.
Maria da Graça Campos Pimentel
Raiza Tamae Feminino Hanada
University of São Paulo, Brasil
A Dwell-Free Eye typing intelligent herramienta for motor disabilities users
This project has already developed models able to suggest possible eye-typed words from severe motor disabilities users. This year is intended to improve the interface, perform experiments with potential real users, create datasets and deliver a low-cost open source software tool.
André R. A. Grégio
Fabricio Ceschin
Universidade Federal do Paraná, Brasil
Identifying Concept-Drift in Malware Classifiers and the Applicability of Deep-Learning Detectors for APTs
In this project, we intend to develop novel, adaptive models to identify whether a program is benign or malicious during its execution, even when its behavior is subtle, creating a real-time infection detector. We also intend to create a public dataset holding as many features from malicious programs as possible, which will benefit from our proposed techniques to be always up-to-date. The models will rely on deep learning for multi-stage classification, as well as innovative concept-drift detection techniques.

Publicado por Berthier Ribeiro Neto, Director de Ingeniería de Google® para América Latina



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