We are experts in the field of data science with a strong emphasis on:
i) the use of systematic and reproducible approaches to high-quality predictive analytics projects, such as the CRoss-Industry Standard Process for Data Mining (CRISP-DM).
ii) the exploitation of the latest discoveries in machine learning technologies, such as artificial neural networks (deep recursive autoencoder nets, convolutional nets, residual nets, long short-term memory nets, self-organising maps), decision tree learning models (xgboost, catboost, and random forests), Bayesian methods and support vector machines. Our emphasis is primarily on first establishing the organisations goals and objectives and their current business processes, technical infrastructure and employee talent for achieving those objectives. We then move through the CRISP-DM methodology, in manner suited to the business context and project requirements, to work collaboratively with the workforce to understand, visualise, transform and model their data until the resulting solution meets the desired objectives.
A process of agile systems development and deployment then takes place requiring the development and testing of necessary software, staff training and the refinement of appropriate business policies and procedures to enable effective deployment.
You can review the types of data science and machine learning projects our team have engaged in by visiting our Research page.
To achieve its mission, Perceptronix Ltd has a network of technology and consulting partners in the United Kingdom, North America, Thailand and India. We work with clients, predominantly engaged in the financial markets, to enable them to exploit the latest advances in machine learning to garner a competitive edge and thus to realise their objectives.
OUR MISSION
Gain traction with your predictive analytics project today!
Dr Jonathan Tepper
As the Quality Manager for the School of Science and Technology at NTU, he was responsible for the quality management and assurance of approximately 120 unique active courses and 440 active modules in the School of Science and Technology, spanning eight cognate disciplines of Biosciences, Chemistry, Forensics, Computing and Technology, Engineering, Physics, Mathematics and Sport Science. His academic leadership impacted all levels of the Higher Education qualification framework and associated quality assurance processes i.e. BSc, MSc and PhD (from design, validation, delivery and evaluation), from design to approval. Jon led numerous initiatives surrounding strategic action planning and associated task group implementation to enhance the student experience and enable academics and course leaders to meet the strategic priorities of the School and University.
He has notably presented his innovative neural network research in macro-economic modelling at the OECD, Bank of England, and MIT’s Sloan School of Management. In addition, Jon has published over 30 peer-reviewed papers and supervised six PhD students to successful completion in subjects relating to machine learning and data analytics.
Jon holds a BSc (H) Computer Systems degree and a PhD in Corpus-based Connectionist Parsing (neural network-based solutions to text analytics) from NTU. He also holds a PG Cert. in Higher Education (Dist) and is a Fellow of the Higher Education Academy and continues to perform research and sessional teaching in computer science.