My name is Eliezer Silva, I am a PhD Research Fellow in the Data and Artificial Intelligence Group, at the Department of Computer Science, The Norwegian University of Science and Technology. In my doctoral studies, I am researching probabilistic / Bayesian/statistical modelling approaches and scalable (approximate) inference algorithms for personalization problems (Collaborative Filtering, Recommender Systems, Link Prediction, etc), under the supervision of Prof. Helge Langseth and Prof. Heri Ramampiaro. I have studied and worked with Poisson-gamma (matrix/tensor) factorization models, Point process models (Hawkes model for example) and hybrid neural-probabilistic models for recommender systems and user modelling with contextual information (spatio-temporal traces, social networks, sessions, etc).
I obtained my M.Sc. degree in Computer Engineering from the School of Electrical and Computer Engineering (Unicamp – State University of Campinas) under the supervision of Prof. Eduardo Valle, with a dissertation with the title “Metric space indexing for nearest neighbour search in multimedia context” (slides, full-text). This research project focused on data structures and algorithms for similarity search in general metric spaces resulting in a proposal for locality sensitive-hashing in generic metric spaces.
I obtained a B.Sc. in Computer Engineering at the Federal University of Espirito Santo, in 2011. My undergraduate dissertation was on content-based text retrieval using Latent Semantic Indexing (LSI) and Vector Space Model. During my bachelor degree, I’ve worked also with Classical Mechanics Simulation using Functional Programming Language (Scheme).
My general research interest is focused on designing efficient algorithmic methods for Machine Learning and Information Retrieval problems, including search, inference and modelling, with a special interest in probabilistic data structures, probabilistic algorithms and probabilistic models.
Feel free to browse through this website and contact me if you have interest in anything I’ve been working. I try (and mostly fail) to update my research blog, discussing new ideas, methods and stuff.