R&D
As an academic, consultant and entrepreneur, I have worked in a number of areas, ranging from purely theoretical to fully applied. A selected list of topics may be found below, with pointers to further reading for anyone interested.
The advent of neuroimaging modalities, notably functional MRI, has marked the dawn of a new era in computational neuroscience allowing for non-invasive observation of brain activity in real-time, enabling us to understand connections between disparate brain regions as a function of activity. My work has focused on representing brain activity as a real-time network of connected micro-regions, and tracking that activity in real-time using data analysis on fMRI signals. This was joint work with researchers from Imperial College and Kings College London. The real-time aspect of this work offers the unique advantage of enabling neurofeedback: the ability to train the brain by voluntary activity or via electrical stimulation in response to observed activity. This real-time control loop has never before been possible and holds promise for future medical interventions.
|
Cybersecurity may be simplistically defined as the defence of a set of information assets that lie within a perimeter from malicious agents outside it. A common example is the protection of enterprise data from exfiltration. Although dominated by rules-based systems, recently the cybersecurity community has recognised that ubiquitous digital data collection renders it a dreamland for statistical machine learning. Nevertheless, obtaining labelled datasets in cybersecurity is exceptionally difficult, as it requires manual labelling by security experts, whose time is scarce and invaluable. The problem is aggravated by a dramatic imbalance in the relative frequency of legitimate and illegitimate traffic: although estimates vary depending on the enterprise in question, attacks can be as rare as one a month, or less, pitted against terabytes of legitimate daily traffic. Consequently, techniques that rely on semi-supervised, weakly supervised or fully unsupervised (e.g., anomaly detection) technology are of great interest, and the focus of my work in this area.
|
Scorecards are in widespread use in the retail banking industry, primarily for determining the creditworthiness of individuals or companies, but also, in modified forms, for determining the likelihood that a certain transaction was part of a money laundering effort. They are similar to probabilistic classifiers, but are subject to additional constraints because of regulatory issues. As a result, building a scorecard (which is the term used for the process of feature extraction, model selection and parameter estimation) is a costly exercise that involves human supervision, and retail banks are reluctant to repeat that process often. This results in scorecards whose parameters can often become obsolete because of population drift, i.e., because of demographic or other changes in the population of interest. Especially in anti-money laundering, the arms race between regulators and launderers results in frequent behavioural changes of both parties, which would render any scorecard built on historical data possibly irrelevant to current practice. My group at Imperial College spent considerable energy evangelising the use of continual scorecard updating instead of periodic rebuilding, and the use of principled exponential forgetting techniques to gradually downweight the importance of historical data and enable the scorecard to remain up-to-date.
|
A general trend in predictive analytics is to replace modelling assumptions, largely driven by mathematical descriptions of a given phenomenon, with data-driven techniques, driven by data crunching and trial and error. This same trend is about to revolutionise industrial manufacturing, as the introduction of numerous high-frequency sensors on industrial components enables the use of data-hungry machine learning algorithms, that can anticipate the behaviour of the IoT device with comparable accuracy to physical models of the device involving complex mathematics and possibly years of study. By the same token, AI can detect when the behaviour of a component is outside its normal bounds, and raise an early warning for a potential catastrophic fault. The ease of use and general applicability of AI means that such capabilities will soon be rolled out across all IoT industrial devices -a massive market, with huge potential gains. For more, please read the blog post cited below which describes our work with Mentat in predicting faults of pneumatic valves.
|