Inverse Problems and Mathematical/Statistical Modelling Conference (IPMM)
Research students and academics with an interest in inverse problems and mathematical/statistical modelling are invited to a conference at Manchester Met in June 2024.
The event is being organised by associate professor Taysir Dyhoum to mark their appointment as senior lecturer in artificial intelligence at the Department of Computing and Mathematics.
It aims to strengthen the research network for inverse problems and mathematical modelling, set a vision for growing mathematical modelling and explore funding opportunities.
The conference is open to mathematicians, computer scientists and researchers working in associated fields from Manchester Met and other institutions.
Get involved
The conference is free to attend. To register, email conference organiser Taysir Dyhoum with your name, school address, and email.
Research students/academics are invited to present their work in a 15-minute talk or poster. Please submit a title and abstract for your contribution by 21 May 2024 for review. We will notify successful applicants.
Conference programme
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Talk title |
The presenter |
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9:00-9:30 |
Registration (coffee and tea will be available) |
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9:30- 10:00 |
Inverse problems of damped wave equations with Robin boundary conditions: an application to blood perfusion |
Prof. Daniel Lesnic (University of Leeds, UK). |
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10:00- 10:30 |
Direct and inverse Solutions for Non-homogeneous Wave Equations with Unusual Boundary Conditions. |
Dr Taysir Dyhoum (Manchester Metropolitan University, UK). |
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10:30- 11:00 |
Using Machine Learning as a Surrogate Model for Agent-Based Simulations |
Professor Claudio Angione (Teesside University, UK). |
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Coffee/Snacks Break |
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11:30- 12:00 |
Modelling uncertainties in phase-space boundary integral models of ray propagation. |
Dr David Chappell (Nottingham Trent University, UK). |
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12:00- 12:30 |
Application of machine learning algorithms to the discretization problem in wearable electrical tomography imaging for bladder tracking |
Prof. Manuchehr Soleimani (Bath University, UK). |
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Lunch Break |
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13:30- 15:15 |
15-mins talks each |
Postdoc and postgraduates Researchers in the field |
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Coffee Break |
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15:45- 16:45 |
Poster discussions (Social event) |
Postdoc and postgraduates Researchers in the field |
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16:45- 17:00 |
Announcing the poster & best 10-mins presentation winners and closing the conference |
Speakers
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Daniel Lesnic is a professor of applied mathematics at the University of Leeds. His areas of expertise: inverse problems; boundary element method; method of fundamental solution; industrial mathematics; heat and mass transfer; porous media; the critical Stokes number.
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Claudio Angione is a professor of artificial intelligence at Teesside University and holds a PhD in Computer Science from the University of Cambridge. He has published more than 65 peer-reviewed papers. His research group works at the intersection of computer science, mathematics and biology. Research topics include machine/deep learning; biomedical modelling and optimization; systems biology; genome-scale cell models; statistical big data analytics.
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David Chappell is an associate professor of applied mathematics at the School of Science and Technology at Nottingham Trent University. He leads research in computational mathematical modelling with application areas including acoustics, fluid mechanics, neuroscience and structural vibrations. The underlying motivation for his research is often to develop improved virtual design, analysis, and optimisation tools for industry.
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Taysir Dyhoum is an associate professor of mathematics and computing at Manchester Met. Research experience includes: numerical analysis; mathematical, statistical and data analytics modelling; imaging inverse problems.
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Manuchehr Soleimani is a professor in the Department of Electronic and Electrical Engineering, and leads the Engineering Tomography Lab (ETL) group he founded in 2011. Research themes include: electrical and electromagnetic tomography-ray CT; ultrasound tomography; multi-modality tomography; inverse problems and machine learning, with applications in industrial tomography; robotic touch sensing; medical imaging.
Department of Computing and Mathematics