Research interests: machine learning, (probabilistic) inductive logic programming and applications in bioinformatics, natural language processing and software engineering.
|Degree||Institution||Started (year)||Finished (year)|
|PhD||Imperial College London||2012||in progress (2015)|
|MSc||Imperial College London||2010||2011|
|BSc||"Alexandru Ioan Cuza" University, Iaşi||2007||2010|
During my PhD, I will develop novel methods for probabilistic logical learning, exploring probabilistic extensions of established inductive logic programming (ILP) paradigms, abductive frameworks and answer-set programming (ASP) systems. I am searching for suitable applications in domains such as bioinformatics, natural language processing and software engineering. I am co-supervised by Alessandra Russo and Krysia Broda.
My MSc project aimed to develop a parameter learning framework in probabilistic logic programming (PLP) that could be used in a probabilistic model-checking scenario. The goal was to learn the parameters (i.e. probabilities) of a probabilistic model (e.g. a discrete-time Markov chain - DTMC) such that a set of probabilistic requirements (expressed in probabilistic computation tree logic - PCTL) are satisfied. It was considered a distinguished project. I was supervised by Alessandra Russo.
During my MSc, I completed an Individual Study Option (ISO) project on "Artificial Neural Networks for First-Order Logic Learning". I was supervised by Krysia Broda.
The topic of my BSc project was "Kernel Methods. Applications on Cancer and miRNA datasets". I was supervised by Liviu Ciortuz.