Keynote Lectures

Abstract
We are living in the most exciting time in the history of mankind. The last century has seen unprecedented improvements in the quality of the human condition and technology is at the heart of this progress. Now we are experiencing an even bigger leap as we move towards a new level of digitisation and automation. Ranging from self-driving cars to factories without workers to societal infrastructure, every sensor and actuator is becoming connected and new applications that enable new opportunities are appearing daily. The fuel of this emerging connected, software-driven reality is software and the key challenge is to continuously deliver value to customers. The future of software engineering in this context is centered around three main developments: Speed, Data and Ecosystems. The focus on speed is concerned with the constantly increasing rate of deploying new software in the field. This continuous integration and deployment is no longer only the purview of internet companies but is also increasingly deployed in embedded systems. Second, data is concerned with the vast amounts of information collected from systems deployed in the field and the behavior of the users of these systems. The software-intensive systems industry needs to significantly improve its ability to exploit the value present in that data. Finally, ecosystems are concerned with the transition in many companies from doing everything in-house to strategic use of innovation partners and commodity providing partners. The keynote addresses these three main developments, provides numerous examples from the Nordic and international industry and predicts the next steps that industry and academia need to engage in to remain competitive.

Abstract
It is expected that some 5 billion people representing ~60% of the world’s population will live in urban areas by 2030. The growth of cities is an evolving phenomenon that is often unplanned, leading to serious social problems such as traffic congestion, noise pollution, energy wastage, and high levels of carbon dioxide emissions. Given growing urban populations, it is clear we need to change our behaviour to better manage the sharing of increasingly constrained urban resources, such as the road network, energy, water, and so on. This is also an exciting time for ICT, with great advances in sensor technology and wireless communication giving some optimism that in this age, we may be capable of coping with the challenges ahead. This talk explores how automated communication and collaboration, using real-time decision-making, can play a part in assisting citizens in making better use of the resources available to them. The goal is not to take over citizens' lives, but to remove the onus on citizens to be constantly aware of potential opportunities for optimising resource sharing. In particular, the talk uses examples from autonomous vehicles and energy demand-side management.

Abstract
Genomic computing is a new science focused on understanding the functioning of the genome, as a premise to fundamental discoveries in biology and medicine. Next Generation Sequencing (NGS) allows the production of the entire human genome sequence at a cost of about 1000 US; many algorithms exist for the extraction of genome features, or "signals", including peaks (enriched regions), mutations, or gene expression (intensity of transcription activity). The missing gap is a system supporting data integration and exploration, giving a “biological meaning” to all the available information; such a system can be used, e.g., for better understanding cancer or how environment influences cancer development.

The GeCo Project (Data-Driven Genomic Computing, ERC Advanced Grant, 2016-2021) has the objective or revisiting genomic computing through the lens of basic data management, through models, languages, and instruments, focusing on genomic data integration. Starting from an abstract model, we developed a system that can be used to query processed data produced by several large Genomic Consortia, including Encode and TCGA; the system employs internally the Spark engine, and prototypes can already be accessed from Cineca or from PoliMi servers. During the five-years of the ERC project, the system will be enriched with data analysis tools and environments and will be made increasingly efficient. Among the objectives of the project, the creation of an “open source” repository of public data, available to biological and clinical research through queries, web services and search interfaces.

Abstract
Today the problem are heterogeneous, probabilistic, high-dimensional and complex data sets. The challenge is to learn from such data to extract and discover knowledge, and to help to make decisions under uncertainty. In automatic machine learning (aML) great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from "big data" with many training sets. However, sometimes we are confronted with a small amount and complex data sets, where aML suffers of insufficient training samples. The application of such aML approaches in complex application domains such as health informatics seems elusive in the near future, and a good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems which are trivial for human learners. In such situations, interactive Machine Learning (iML) can be beneficial where a human-in-the-loop helps in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where the knowledge and experience of human experts can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase. ML is a fast growing and very practical field with many business applications and much open research challenges, particularly in multi-task learning, transfer learning and hybrid multi-agent systems with humans-in-the-loop. Consequently, successful ML needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization and tackling complex challenges needs both disciplinary excellence and a cross-disciplinary skill-set and international joint work without any boundaries.