David is a researcher in applied artificial intelligence.
David holds a PhD in mathematics and a Master's degree in engineering physics from the Royal Institute of Technology. Davids research experience spans applied algebra, geometry and machine learning. He also has industry experience in the field of formal verification and railway signalling. Lately, David has worked in AI related projects in process chemistry, cybersecurity and business analytics.
Further areas of expertise:
- Hybrid modelling: machine learning is combined with physical models. This may be necessary when the mechanisms governing the system under modelling is only partially known, which is common for example in process chemisty. A hybrid model can be trained on less data than a purely statistical model since known properties such as physical conservation laws are built into the model.
- Federated machine learning: to preserve data privacy, machine learning models can be trained locally without the sharing of private data. The local models are combined through distributed machine learning to a global model. This means that individuals or organisations can share knowledge without sharing sensitive data. Such techniques are useful for example in cybersecurity where different partners may have varying experience of security threats but wish to keep their data private. To ensure that private information cannot be extracted from the local models transmitted over the network, these models may be encrypted homomorphically before transfer.
- The numerical algebraic geometry of bottlenecks
- FL4IoT : IoT Device Fingerprinting and Identification Using Federated Learning
- SparSFA : Towards robust and communication-efficient peer-to-peer federated lea…
- Privacy-preserving Federated Learning System for Fatigue Detection
- Computing Geometric Feature Sizes for Algebraic Manifolds
- Excess Intersections and Numerical Irreducible Decompositions
- Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for …