Danilo P. Mandic is a Professor of Machine Intelligence with Imperial College London, UK, and has been working in the areas of machine intelligence, machine learning, statistical signal processing, big data, machine intelligence on graphs, generative Artificial Intelligence, neural networks, adaptive learning systems, bioengineering, and financial modelling. Applications of his 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.

Dr Mandic has published two research monographs on neural networks, entitled “Recurrent Neural Networks for Prediction”, Wiley 2001, and “Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural models”, Wiley 2009 (both first books in their respective areas), and has co-edited books on Data Fusion (Springer 2008) and Neuro- and Bio-Informatics (Springer 2012). He has also co-authored a two-volume research monograph on tensor networks for Big Data, entitled “Tensor Networks for Dimensionality Reduction and Large Scale Optimization” (Now Publishers, 2016 and 2017), and more recently a research monograph on Data Analytics on Graphs (Now Publishers, 2021).

He has given more than 80 Keynote and Tutorial lectures in international conferences and was appointed by the World University Service (WUS), as a Visiting Lecturer within the Brain Gain Program (BGP), in 2015. Danilo is currently serving as a Distinguished Lecturer for the IEEE Computational Society and a Distinguished Lecturer for the IEEE Signal Processing Society. Dr Mandic is a 2014 recipient of President Award for Excellence in Postgraduate Supervision at Imperial College and holds six patents.

His current research interest in wearables/Hearables include:
- Machine Intelligence for eHealth, including data from wearable physiological sensors
- Embedded systems for Hearables, in-ear sensing of physiological signals like ear-EEG, ear-ECG, ear-PPG
- Artificial Intelligence for Hearables, including in-ear blood glucose monitoring, in-ear blood oxygen levels, in-ear epilepsy monitoring
- Machine intelligence for artefact removal from wearable sensors
- Machine Intelligence on graphs
- Fully interpretable Convolutional Neural Networks and Graph CNNs
- Tensors and Big data for the compression of Large Language Models (LLM)
- Correncoder networks for making sense from real-world data
- Fully interpretable Generative AI

Current research interests in Machine Intelligence for Finance include:
- Large Language Models for financial sentiment analysis
- Reinforcement Learning for Finance
- Advanced methods for algorithmic trading
- Graph learning and graph networks for portfolio management
- Algorithmic investment research
- Tensors and Big Data for Financial Analysis, including Limit Order Book
- Machine Learning for financial markets
- Generative AI for financial engineering
- Complexity science for quantitative finance