Dr Hooman Oroojeni BSc, MSc, PhD

Module leader

• Artificial Intelligence Applications
• Data Warehousing and Business Intelligence

Module contribution

• Introduction to Artificial Intelligence
• Machine Learning

University of Greenwich Vice Chancellor Scholarship

• TinyML: Interested in TinyML, focusing on optimising machine learning models for low-power, small devices, blending computational efficiency with energy conservation.
• Tomography Reconstruction: Interested in Tomography Reconstruction, which involves reconstructing images from signals, with applications in medical imaging and material science.
• Swarm Intelligence: Explore Swarm Intelligence, drawing inspiration from nature to develop algorithms for complex problem-solving in optimisation.

Orlov, N.D., Muqtadir, S.A., Oroojeni, H., Averbeck, B., Rothwell, J. and Shergill, S.S., 2022. Stimulating learning: A functional MRI and behavioural investigation of the effects of transcranial direct current stimulation on stochastic learning in schizophrenia. Psychiatry Research, 317, p.114908.

al-Rifaie, M.M., Hooman, O.M. and Mihalis, N., 2020. Dispersive flies optimisation: modifications and application. In Swarm Intelligence Algorithms (pp. 145-161). CRC Press.

Hooman, O.M., Oldfield, J. and Nicolaou, M.A., 2019, September. Detecting early Parkinson’s disease from keystroke dynamics using the tensor-train decomposition. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE.

Hooman, O.M., Al-Rifaie, M.M. and Nicolaou, M.A., 2018, September. Deep neuroevolution: Training deep neural networks for false alarm detection in intensive care units. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 1157-1161). IEEE.