Dr Ismail Adeniran

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Dr Ismail Adeniran

Senior Lecturer

My profile

Biography

I have an undergraduate degree in Chemical Engineering and an MSc in Computation from UMIST (University of Manchester). My PhD is in Biological Physics from the School of Physics and Astronomy at the University of Manchester and involved the application of Physics to the understanding of the fundamental mechanisms underlying irregular heart rhythms particularly in the Short QT Syndrome.

Interests and expertise

My research is focused on the application of the combination of Physics, computational Science and Scientific (Physics-informed) Machine Learning to:

  • understanding the fundamental mechanisms underlying neurological, musculo-skeletal and cardiovascular diseases and disorders
  • discovering and developing therapeutic approaches and rehabilitation for these diseases and disorders.

Get in touch

0161 247 1779

Research outputs

Research expertise

The application of scientific machine learning (computational science and machine learning) to solve complex, real-world problems at all levels of scale.

  • Computational Physiology: The use of novel and innovative physiologically based scientific machine learning to:
    • understand the functional consequences of sarcomeric protein mutations in Hypertrophic Cardiomyopathy and Dilated Cardiomyopathy, linking genotype to phenotype and possible novel therapeutic interventions.
    • understand the pathophysiological mechanisms underlying co-existent Atrial Fibrillation and Heart Failure and possible novel therapeutic interventions.
    • understand the pathophysiological mechanisms underlying Heart Failure with Preserved Ejection Fraction and possible novel therapeutic interventions.
    • understand the pathophysiological mechanisms underlying neuromusculo-skeletal diseases and possible novel therapeutic interventions.
    • understand the common pathways between neuromusculo-skeletal diseases and cardiomyopathies.
  • Scientific software development
  • Optimal Control, Deep Reinforcement Learning, PDE-Constrained Optimisation, Operations Research, Mathematical Optimisation/Mathematical Programming
  • Quantum Computing
  • Grassmann Algebra and its derivatives including Clifford/Geometric Algebra.
  • Cryptoassets: Specifically, the development of novel classical and quantum algorithms for fundamental analysis of the Cryptoasset class. The primary objective is to adress the central problems of volatility, instability and uncertainty, which presently plague cryptoassets by providing a valuation and regulatory framework that imbues cryptoassets with intrinsic value.