Dr. Jarana Manotumruksa
Jarana’s research area is recommendation system. In particular, he aims to generate personalised venue recommendation to users based on their historical feedback and their current context (e.g. time of the day and their current location). We aim to exploit various techniques such as MF and BPR, a collaborative filtering technique that is widely used in previous literature, to effectively model the user’s preferences and to generate high quality of venue suggest. Recent, he explored the effectiveness of Deep Neural Networks (e.g. multi-layer perceptron and RNN) to capture the short-term preference of users based on their sequential order of checkins.
Qiang’s research interests lie in the areas of statistical machine learning and deep learning with applications to sequential data, such as natural languages, human behaviours and physiological signals. He is also interested in multimodal data fusion and integration. Currently he focuses on machine learning methods for automatic verification of textual facts on the web.
Sahan’s interests lie on the theme: “Improving Recommendations of Educational Contents to Lifelong Learners”. He is a recipient of multiple research fellowships including UCL Advances (twice), Cisco CIIP and UCL Departmental Scholarship. Sahan currently contributes to the X5GON project. His current research is motivated towards developing scalable, automatic quality assurance models for educational resources and modelling the knowledge state of lifelong learners to identify the most helpful educational resources that will enable them to achieve their learning goals.