My research is centred around the design of algorithms for Statistical Learning and Machine Intelligence, including Neural Networks, learning on graphs, Big Data, and Statistical Signal Processing. Applications of my research include machine intelligence for wearables / Hearables physiological sensing, and machine intelligence for financial applications.
Particular emphasis is on "no data is bad data" with the aim to make sense from both real-world data and manifold sources of noise and artefacts, together with the interpretability and explainability of the otherwise black box deep learning algorithms throughout the data processing chain.

I am honoured to currently serve as:
- President of the International Neural Network Society (INNS)
- Distinguished Lecturer of the IEEE Computational Intelligence Society (IEEE CIS)
- Distinguished Lecturer of the IEEE Signal Processing Society (IEE SPS)

Biography: Dr. Mandic received the Ph.D. degree in nonlinear adaptive signal processing in 1999 from Imperial College, London, London, U.K. where he is now a Professor. He specialises in Statistical Learning Theory, Machine Intelligence, and Statistical Signal Processing, and their applications especially in Biomedicine and Finance. He is a pioneer of Hearables (in-ear sensing of neural function and vital signs), an unobtrusive, discreet and long-term wearable solution for long-term physiological monitoring based on miniaturised sensors embedded on an earplug, an area where he holds several patents. He also specialises in Machine Intelligence for Finance, and is a Director of the Financial Signal Processing and Machine Learning Lab a Imperial.

He has written over 600 journal and conference articles, and research monographs on Recurrent Neural Networks (with Wiley, 2001), Complex-valued Adaptive Filters and Neural Networks (Wiley 2009), Tensor Networks for Dimensionality Reduction and Large Scale Optimisation (Now Publishers, 2017) and Data Analytics on Graphs (Now Publishers, 2021).

Prof Mandic is a Fellow of the IEEE, the 2019 recipient of the Dennis Gabor Award for "Outstanding Achievements in Neural Engineering", given by the International Neural Networks Society (INNS). He is also a 2018 winner of the Best Paper Award in IEEE Signal Processing Magazine, for his article on Tensor Decompositions for Signal Processing Application, and the 2021 winner of the Outstanding Paper Award in the IEEE ICASSP conference. He is also a winner of The Prize in the 2023 IEEE Engineering in Medicine and Biology Prize Paper Awards, and has coauthored 6 more award winning articles. He is a Core Member of the Machine Learning Initiative at Imperial.

Danilo is the President of the International Neural Networks Society, and a past Technical Chair of ICASSP 2019, held in Brighton UK. He also received President's Award for Excellence in Research Supevervision at Imperial College in 2014. Danilo is passionate about cross-disciplinary aspects of his work and about bringing research into the curriculum. His current research interests areas are Adaptive Learning Theory, Big Data, Machine Learning on Graphs, Neural Networks, and Complexity Science, and their applications in Biomedicine and Financial Engineering.