Title of the talk: "Can we develop it?", "Yes". "Does it scale?", "Not sure ...": On the modeling, simulation and development of scalable services for social goods
Abstract: Current information and communication technologies, and the novel Internet of Things (IoT) in particular, promote the development of a plethora of novel services for social good. Examples of applications can be found in the domains of intelligent transportation systems, smart cities, up to the deployment of sustainable services for decentralized territories. The current focus is mainly on the development of these services and on the related enabling technologies, while the analysis, modeling and simulation steps are quite often ignored. This was also due to the limits of certain modeling and simulation tools. As a matter of fact, the combination of complex network theory and hybrid, multi-level simulation allows composing heterogeneous modeling and simulation scenarios. These can be proficiently exploited to devise large scale IoT services for social goods.
Stefano Ferretti is an Associate Professor at the Department of Computer Science and Engineering of the University of Bologna. He received the Laurea degree (summa cum laude) and the Ph.D. in Computer Science from the University of Bologna respectively in 2001 and in 2005. His current research interests include distributed systems, computer networks, complex networks, mobile communications, multimedia, hybrid and distributed simulation. He is in the editorial board of the Simulation Modelling Practice and Theory (SIMPAT) journal, published by Elsevier, and of the Encyclopedia of Computer Graphics and Games, published by Springer.
CTO of BioBeats, Artificial Intelligence for Human Wellbeing
Wearable physiological monitoring, exploiting devices with unobtrusive (often wrist-worn) packages, offers substantial promise for improving the care of patients in clinical settings, and for enabling individuals to better manage their own health. With recent advances in consumer markets, including devices such as fitness trackers and “smart watches”, the use of wearable monitors is becoming increasingly commonplace. However, very few of these devices penetrate into use at scale within either clinical settings. Medical literature relies on limited amount of reliable data collected in a controlled environment, and wrist worn-devices represent a new challenge and opportunity to use a vast amount of unreliable data collected in a non controlled environment. A new field of research is needed.
At BioBeats, we're working on projects that help people be well, fight stress, and be more productive. In most of these projects, deep learning approaches are taken to train models that can classify, predict and illuminate behaviour from the person's body and actions. Most of our classifiers learn from smartphone sensors, but increasingly our algorithms ingest from wrist-worn sensors. Our approach to building machine-learning-driven applications learns from evidence-based psychosocial intervention practices in mental health, but embodies continuous cardiovascular, skin, and movement-based sensor data in order to arrive at profound but granular insight for the individual, and their care or employer circle.
Researcher and entrepreneur, Davide leads BioBeats’ engineering team as CTO. He is a specialist in the intersection between Artificial Intelligence and music, previously ran a distributed software consultancy company in Italy for ten years. His PhD in Computer Science focuses on models that discover latent variables in performance profiles.