Dr Jonathan Tepper is a principal lecturer and Learning and Teaching Coordinator (LTC).
Module Leader for the Data Analytics specialisations.
Jonathan’s research interests and activities broadly focus on machine learning techniques for modelling and representing data and behaviour. He is particularly interested in the area of neurally-inspired computing methods applied to problem domains such as natural language processing, temporal sequence processing and more generally, data analytics. He is actively involved in the adaptation and application of supervised and unsupervised recurrent neural network architectures for tasks such as corpus-based syntactic parsing, automatic extraction of linguistic structure from sequential input, and non-linear modelling of financial data.
Jonathan’s work has also led to the synergy of machine learning techniques and constructivist learning and teaching theory to formulate a metric for constructive alignment - a methodology for aligning the main components of an educational design. The metric helps teaching practitioners to better align their course / module designs to promote deep student learning.
Jonathan is a member of the Computational Intelligence and Applications (CIA) Research Group. The group has substantial experience and is well-equipped to carry out research on non-linear data modelling, data mining using machine learning techniques, and natural language interfaces.