Jon Tepper

Jonathan Tepper

School Standards and Quality Manager

School of Science & Technology

Staff Group(s)
Computing and Technology

Role

Dr Jonathan Tepper is a Principal Lecturer and Learning and Teaching Coordinator (LTC).

Learning and Teaching Enhancement:

  • Strategy development and implementation
  • Coordinator and / or Lead for Enhancement Working Groups
  • Lead for Observation of Teaching Schemes
  • Organiser and / or Lead of staff L&T development and sharing practice events

Module Leader for:

  • ISYS10241- Systems Analysis and Design with Professional Development (40 cpt, Level 4 UG)
  • ISYS10242- Systems Analysis and Design (20 cpt, Level 4 UG)
  • COMP40551- Big Data and Its Infrastructure (20 cpt, Level 7, PG)
  • COMP40561- Practical Machine Learning Methods for Data Mining (20 cpt, Level 7, PG)

Teaching Contributions for:

  • COMP10081- Foundations in Computing and Technology
  • ISYS30221- Artificial Intelligence (responsible for machine learning methods for data mining)

Research areas

Dr Tepper'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.

Dr Tepper'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.

Dr Tepper 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.

Opportunities to carry out postgraduate research towards an MPhil/PhD exist and further information may be obtained from the NTU Graduate School.

Past PhD Students:

  • Dr Mahmud Shertil - 'On the Induction of Temporal Structure by Recurrent Neural Networks'. Jointly supervised with Dr Heather Powell
  • Dr Tom McQueen – 'STORM: an Unsupervised Connectionist model for Language Acquisition'. Jointly supervised with Professor Adrian Hopgood and Dr Tony Allen
  • Dr Dianabasi Nkantah - 'Shallow Lexical Representations for Deep Corpus-based Connectionist Parsing'. Jointly supervised with Dr Heather Powell and Professor Nasser Sherkat.

Current PhD Students:

  • Alaa Bafail - ' A Computational Intelligence Model for Constructively Aligning Instructional Designs '. Jointly supervised with Ann Liggett
  • Jennifer Evans – 'What is the Impact of Technology Based Interventions on Student Learning and Staff Practice in Undergraduate Chemistry-based Laboratories?' Jointly supervised with Dr Karen Moss and Professor Steve Allin

External activity

  • Fellow member of the Higher Education Academy (FHEA)
  • External Examiner for Nicholas Moss, Open University, PhD entitled 'The Role of Non-Conceptual Content in the Navigation Skills of an Artificial Agent'
  • External Examiner for Yulei Fan, Leeds Metropolitan University, PhD entitled 'Histological Edge Detection using Statistical Tests and Neural Networks'
  • Co-organiser (with HE Academy) of the first two UK Workshops in Constructive Alignment
  • Reviewer for Journal of Natural Language Engineering
  • Reviewer for Special Issue of Advances in Economics: Applications of Artificial Intelligence in Finance and Economics 19
  • Member of the International Programme Committee for The 2003 International Conference on Artificial Intelligence
  • Reviewer for Expert Systems journal

Sponsors and collaborators

Current research projects relating to non-linear modelling of economic time-series data are being conducted with the collaboration of:

  • Professor Jane Binner (University of Birmingham)
  • Dr Logan Kelly (University of Wisconsin – River Falls)
  • Professor Michael Belongia (University of Mississippi)
  • Professor Peter Tino (University of Birmingham)
  • Professor Marcelle Chauvet (University of California – Riverside)

Publications

Module assessment: assessment, content, standards alignment and grade integrity. Tomas C, Thomas G and Tepper J, SIG1 Conference, Madrid, 27-29 August 2014

Assessment for learning systems analysis and design using constructivist techniques. Tepper J, HEA STEM Annual Learning and Teaching Conference 2014: Enhancing the STEM Student Journey, University of Edinburgh on 30 April – 1 May 2014

Does money matter in inflation forecasting? Binner J, Tino P, Tepper J, Anderson R, Jones B and Kendall G, Physica A - Statistical Mechanics and its Applications, 2010, 389 (21), 4793-4808

Predictable non-linearities in U.S. inflation. Binner J, Elgar T, Nilsson B and Tepper J, Economics Letters, 2006, 93 (3), 323-328

Measuring constructive alignment: an alignment metric to guide good practice. Tepper JA in 1st UK Workshop on Constructive Alignment, Higher Education Academy Information and Computer Sciences (ICS), Subject Centre and Nottingham Trent University, 23 February 2006

Extracting finite structure from infinite language. McQueen T, Hopgood AA, Allen TJ and Tepper JA, Knowledge-Based Systems, 2005, 18 (4-5), 135-141

Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks. Binner JM, Elger T, Nilsson B and Tepper JA, Advances in Economics: Applications of Artificial Intelligence in Finance and Economics, 2004, (19), 71-91

A corpus-based connectionist architecture for large-scale natural language parsing. Tepper JA, Powell HM and Palmer-Brown D, Connection Science, 2002, 14 (2), 93-114

Connectionist natural language parsing. Palmer-Brown D, Tepper JA and Powell HM, Trends in Cognitive Sciences, 2002, 6 (10), 437-442

See all of Jonathan Tepper's publications...

Course(s) I teach on