Active research, applied to live systems.
Perceptronix sits at the intersection of academic research and industrial AI delivery. Recent work spans macroeconomic forecasting with NIESR, neural ensemble methods for financial time-series, and applied GenAI architectures. ORCID 0000-0001-7339-0132 for the full publication record.
Three current research strands.
Multi-recurrent neural networks (MRNs)
A long-running research programme on deep recurrent architectures whose recurrent state is structured to capture multi-scale temporal dependencies. Applied across NLP, finance, and macroeconomics.
AI for macroeconomic forecasting
Published with NIESR and the University of Birmingham. UK CPI inflation forecasts to within ±0.2% across multi-month horizons; US inflation turning-point detection ahead of the SPF.
Cognitive science & neural computation
Work published in Trends in Cognitive Science and AI in Medicine on the computational foundations of recurrent learning systems.
Peer-reviewed work — research-active in industry.
A representative slice of recent peer-reviewed outputs across macroeconomic forecasting, multi-recurrent neural networks, deep mining of biomedical data, and applied NLP. Each entry links to the full paper PDF.
- 2025
UK CPI inflation outlook — Winter 2025
Tepper, J. A. et al. · NIESR Economic Outlook (Winter 2025)
MASCET inflation forecasts and turning-point analysis.
- 2025
Multi-recurrent neural ensembles for financial time-series forecasting
Tepper, J. A. et al. · Journal of Risk and Financial Management (JRFM)
- 2024
UK CPI inflation outlook — Spring 2024
Tepper, J. A. et al. · NIESR Economic Outlook (Spring 2024)
Forecasts within ±0.2% across multi-month horizons.
- 2024
Do monetary aggregates improve inflation forecasting in Switzerland?
Kelly, L. J., Binner, J. M., and Tepper, J. A. · Journal of Management Policy and Practice (forthcoming)
- 2023
Neural ensembles for cross-cutting economic trend detection
Tepper, J. A. et al. · Procedia Computer Science
- 2022
Deep mining from omics data
Alzubaidi, A. and Tepper, J. · Methods in Molecular Biology, vol. 2449 (Humana, New York)
Book chapter — Data Mining Techniques for the Life Sciences (eds Carugo & Eisenhaber).
- 2020
Time sensitivity and self-organisation in multi-recurrent neural networks
Orojo, O., Tepper, J. A., McGinnity, T. M., and Mahmud, M. · IEEE International Joint Conference on Neural Networks (IJCNN)
- 2020
A novel deep mining model for effective knowledge discovery from omics data
Alzubaidi, A., Tepper, J., and Lotfi, A. · Artificial Intelligence in Medicine, 104: 101821
- 2019
A multi-recurrent network for crude oil price prediction
Orojo, O., Tepper, J. A., McGinnity, T. M., and Mahmud, M. · IEEE Symposium Series on Computational Intelligence (SSCI 2019)
doi:10.1109/SSCI44817.2019.9002841
- 2018
Detecting hate speech on Twitter using a convolution-GRU based deep neural network
Zhang, Z., Robinson, D., and Tepper, J. · 15th European Semantic Web Conference (ESWC 2018), Heraklion, Crete
- 2016
On the importance of sluggish state memory for learning long-term dependency
Tepper, J., Shertil, M., and Powell, H. · Knowledge-Based Systems, vol. 96
doi:10.1016/j.knosys.2015.12.024
Full publication record on ORCID and Google Scholar.
One expert. Two complete credentials.
Whether you need a seasoned data scientist who ships production AI systems, or a published researcher with a peer-reviewed track record in macroeconomic forecasting and neural computation — both are Jonathan. Open either CV to see the full depth of experience relevant to your engagement, or download a copy to share with your team.
Senior Data Scientist CV
Industry · production AI · 15+ years
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Senior Data Scientist CV
Industry · production AI · 15+ years
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Academic CV
Publications · grants · PhD supervision · teaching
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Academic CV
Publications · grants · PhD supervision · teaching
PDF · A4 · 2-page preview
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