Keynotes
Five keynote presentations were delivered by: Christopher Bishop, Francesca Dominici, Deirdre Mulligan, Claudia Perlich, and Alessandro Vespignani.
Christopher Bishop on "Model-Based Machine Learning"
In this talk we introduce a new perspective called ‘model-based machine learning’, which recognises the fundamental role played by prior knowledge in all machine learning applications. It provides a compass to guide both newcomers and experts through the labyrinth of machine learning techniques, enabling the formulation and tuning of the appropriate algorithm for each application. We also show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning.
Francesca Dominici on "Data Science and our environment"
We have developed an artificial neural network model that uses on-the-ground air-monitoring data and satellite-based measurements to estimate daily pollution levels across the continental U.S., breaking the country up into 1-square-kilometer zones. We have paired that information with health data contained in Medicare claims records from the last 12 years, and for 97% of the population ages 65 or older. We have developed statistical methods and computational efficient algorithms for the analysis over 460 million health records.
Deirdre Mulligan on "Saving Governance-By-Design"
This talk proposes a detailed framework for saving governance-by-design. It examines recent battles to embed policy in technology design to identify recurring dysfunctions of governance-by-design efforts in existing policy making processes and institutions. It closes by offering a framework to guide "governance-by-design" that surfaces and resolves value disputes in technological design, while preserving rather than subverting public governance and public values.
Claudia Perlich on "Predictability and other Predicaments in Machine Learning Applications"
In the context of building predictive models, predictability is usually considered a blessing. After all - that is the goal: build the model that has the highest predictive performance. The rise of 'big data' has in fact vastly improved our ability to predict human behavior thanks to the introduction of much more informative features. However, in practice things are more differentiated than that. For many applications, the relevant outcome is observed for very different reasons: One customer might churn because of the cost of the service, the other because he is moving out of coverage.
Alessandro Vespignani on "Data Science and Epidemiology: more than forecast"
The data science revolution is finally enabling the development of infectious disease models offering predictive tools in the area of health threats and emergencies. Analogous to meteorology, large-scale data-driven models of infectious diseases provide real- or near-real-time forecasts of the size of epidemics, their risk of spreading, and the dangers associated with uncontained disease outbreaks. I will review and discuss recent results and challenges in the area, ranging from applied analysis for public health practice to foundational computational and theoretical challenges.