Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.
Today, querying the massive amounts of images available in online databases such as Instagram can be a time-consuming experience. Researchers from the King Abdullah University of Science and Technology (KAUST) and the University College London, have developed a new tool that generates image queries based on a geometric description of objects in spatial relationships with potential applications in computer graphics, computer vision and automated object classification.
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.
An IVUL Ph.D. student (Victor Escorcia) participated in the Deep Learning Summer School. He was selected among many students/researchers worldwide to attend this event, where he had the opportunity to interact with and learn from leaders in the field of deep learning.
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.
An IVUL Ph.D. student (Fabian Caba) participates in the Computer Vision Summer School.
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.
Two IVUL Ph.D. students (Fabian Caba and Victor Escorcia) participate in the annual MSR Summer School.
Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.
Existing systems for predicting protein structure are outperformed by a newly developed method.
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.
A new mathematical model explains how random factors affect the production of proteins within the cells.
Our workshop proposal on large-scale human activity understanding was accepted to CVPR2016. We will be releasing the results of the 1st annual ActivityNet challenge during the workshop. This venue allows researchers in the field to evaluate their activity classification and detection techniques on a large-scale benchmark of in-the-wild video sequences.
A combination of state-of-the-art sensors and standardized data analysis will transform research for tracking animals and humans.