Patrick McSharry is a Professor at the Department of Electrical and Computer Engineering, Carnegie Mellon University, Research Fellow at the Kigali Collaborative Research Centre (KCRC) and Strategic Advisor to the World Bank funded African Centre of Excellence in Data Science (ACE-DS). Having served 22 years at Oxford University, he remains affiliated with the Oxford Man Institute of Quantitative Finance and the Oxford Internet Institute. He is a Fellow of the Royal Statistical Society, Senior Member of the IEEE and Senior Academic Member of the Willis Research Network and advises on working groups for insurance, open data and big data. Patrick was a Royal Academy of Engineering Research Fellow and held two Marie Curie Fellowships (UK and Spain).
He advises national and international government agencies, foundations, NGOs and private sector firms on developing strategies for data analytics within the finance, insurance, agriculture, energy, telecoms, environment, education and healthcare sectors. He takes a multidisciplinary approach to developing quantitative techniques for automating decision-making and improving risk management. His research focuses on artificial intelligence, machine learning, data science, big data, forecasting, predictive analytics and the analysis of human behaviour. He has published over 100 peer-reviewed papers and three books including the "Big Data Revolution". Patrick received a first class honours BA in Theoretical Physics and an MSc in Engineering from Trinity College Dublin and a DPhil in Mathematics from Oxford University.
You can contact Patrick at firstname.lastname@example.org
Popular posts from this blog
Tea plantation in the western province of Rwanda. The global tea market is estimated to be worth about 38.2 billion U.S. dollars in 2016 and is projected to continue growing at 2.8% annually until 2020 . After coffee, tea is Rwanda's second most valuable export where agriculture accounts for one third of economic output . Tea productivity depends on a various factors such as weather, fertiliser and management practices. Hot dry conditions are generally detrimental for tea productivity and therefore daily rainfall and temperatures are important variables. A recent drought in Rwanda, claimed to be the worst in 60 years, has generated losses of up to 40% for some tea producers. Climate variability is already a concern for the agricultural sector. Uncertainty about the impact of future climate change and the frequency and severity of weather extremes such as droughts poses substantial challenges for policymakers. Information about the current climate relies on 30-year
Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels
Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotel