Call for PhD Studies in “Data Analytics and Decision Sciences” @Politecnico di Milano in cooperation with the Centre for Analysis Decisions and Society @Human Technopole.
PhD in Data Analytics and Decision Sciences at the Politecnico di Milano
The PhD program in Data Analytics and Decision Sciences aims at breeding the next generation of data scientists who will tackle the challenges and the opportunities created by the increasing availability of massive amount of data. These data scientists will be able to capture the relevant aspects of phenomena at play, develop adequate models, supervise the development of analytic pipelines, critically analyze the results, and support the technological transfer.
The PhD program will begin October 2018. There are four scholarships available. Three of them are on the specific research topics listed below and are supported by the Center for Analysis, Decisions and Society of the Human Technopole. The deadline to apply is May 21 at 2pm (CET UCT+1).
Topic-Specific Scholarships for the PhD in Data Analytics and Decision Sciences
Topic 1 – Data Integration and Analysis in Heterogeneous Environments
Over the past 20 years the view on data-related problems has changed dramatically, moving to a Web- dominated world in which many different sources of however structured information must interoperate, ideally in a way that gives users a fully integrated view of them. This research focuses on the problems that derive from this scenario, aiming to study and design the means to access and analyze data coming from several sources of information and to deal with heterogeneity, uncertainty, possible low-quality data with a view over them that is as unified as possible. In doing so, the research will also take into account basic ethical principles like fairness, non- discrimination, transparency, data protection, diversity, endeavoring to inject them into each step of the data analysis lifecycle (source selection, data integration, and knowledge extraction), so as to incorporate ethics throughout the design of the technology.
The project aims at providing a middleware that supports the combination of data coming from different sources by leveraging existing middleware previously oriented to support the Internet of Things, tailoring ethically-aware integration views on the basis of a view adaptation factor that takes into account the ethical context.
Topic 2 – Healthcare Research in a Real-World Evidence Setting
Healthcare research is concerned with the study of the huge amount of data and information produced by the clinical and administrative datawarehouses arising from healthcare context. In this huge amount of data lies an immense potential for statistical learning which aims to support and inform policy making. If properly exploited, managed and integrated, such data may lead to a great innovation in epidemiology, pharmacoeconomics, clinical and healthcare research in general.
The research will develop novel statistical methods for integrated and massive healthcare data as well as new econometric setups to (i) provide new insightful methods for outcome research; (ii) measure effectiveness of procedure and protocols of healthcare paths; (iii) perform HTA on Real Word based datawarehouses; (iv) assess healthcare providers performances and quantify the related costs in different scenario analysis; (v) make pharmaco-epidemiological and pharmaco-economical analyses. Expertise in programming languages for statistics (R, SAS or similar) is recommended.
Topic 3 – Data Analytics for Impact Measurement and Economic Interdependencies in Complex Ecosystems
The research focuses on the study of scalable statistical and economic models to support the evaluation of the socio-economic impacts generated by different economic agents, and to analyze the interdependencies that characterize current complex ecosystems. The research includes the development of advanced data analytics methods that integrate both accounting (e.g., IO and EEIO data) and socio-environmental data to study i) the economic and environmental impacts arising from external shocks (e.g. strategic investments, environmental disasters), ii) the evolution of the ecosystems (in terms of technological and environmental conditions), iii) the comparison of different ecosystems.
The project will develop novel statistical methods for complex data (e.g., matrices, networks and object data) and new econometric setups to (i) provide an innovative perspective on the use of IO and EEIO matrices, and (ii) to gauge the uncertainty in the estimation of economic and socio-environmental multiplier effects and in forecasting future configurations.