Abstract- Future Health requires to leverage the fact that each individual is unique due to his/her/their omics (genomic, transcriptomic, metabolomics, etc.), lifestyle, environment, and socioeconomic factors. Progress in sensors, mobile and ubiquitous computing, medical, pharmaceutical and nursing sciences, informatics, genomics, and Artificial Intelligence allows gathering information about individuals to build their own personal models for predictive and preventive guidance. Cybernetics (i.e., continuous closed-loop feedback control) principles can provide perpetual guidance to individuals toward a healthy life in real time. Achieving the goal of preventive health systems in the cybernetic model occurs through the flow of several components. From personalized models, we can predict health status using perpetual sensing and data streams. Given these predictions, we give precise recommendations to best suit the prediction for that individual. To enact these recommendations, we use persuasive technology to deliver and execute targeted interventions. In this talk, I describe how AI and wearable technology are enabling big health data collection, analytics, and smart recommendation. I present examples of how IoT-based remote monitoring and intervention systems are being used to address real-life health and wellbeing issues, and how sense-making is performed on such fine-grained big data.

Short Biography- Amir M. Rahmani is the founder of Health SciTech Group (healthscitech.org) at the University of California, Irvine (UCI) and the Associate Director of the Institute for Future Health (futurehealth.ics.uci.edu). He is an Assistant Professor of Nursing, Computer Science, and EECS at UCI and is also a life-time adjunct professor (Docent) in embedded parallel and distributed computing at the Department of Information Technology of University of Turku (UTU), Turku, Finland. His research is in Internet-of-Things (IoT), e-health, ubiquitous computing, bio-signal processing, health informatics, and big health data analytics. He is especially excited about novel sensing, computation/analytics, communication, and networking paradigms, applied to healthcare/medical and wellbeing applications. He has been leading several NSF, NIH, Academy of Finland, and European Commission funded projects on Smart Pain Assessment, Preventing Preterm-Birth, Family-centered Maternity Care, Stress Management in Adolescents, and Remote Elderly and Family Caregivers Monitoring. He has received numerous research excellence awards (e.g., 2x from Nokia Foundation) and best paper awards (e.g., from MobiHealth, ANT, HealthyIoT, and DFT). He is the co-author of more than 200 peer-reviewed publications and the associate editor-in-chief of ACM Transactions on Computing for Healthcare.

Ensembles of Deep Neural Networks  and Evolutionary Algorithms for Big Data Analytics

Abstract- Big Data gained importance, as many organizations have been collecting massive amounts of domain-specific information, i.e., in cybersecurity, medicine, forecasting, etc. Analyzing and extracting the high-level complex information contained in the data belonging to these particular domains need more sophisticated algorithms. The new emerging DNN techniques and the evolutionary computing-based approaches, can work also with little knowledge of the domain and also handle massive amounts of data in an incremental way. In addition, the ensemble paradigm permits handling the problem of unbalanced classes, captures better non-linear correlation in the data, works well with changing data streams and are robust to noise and missing data. In this talk, we explore how ensembles of Deep Learning and evolutionary algorithms can be exploited  for addressing some important problems in Big Data Analytics, varying from the detection of attacks in modern intrusion detection systems to the real-time rainfall estimation for the prevention of disasters.

Short Biography- Gianluigi Folino holds a Ph.D. in Physics, Mathematics and Computer Science. Since 2001, he works as a senior researcher at ICAR-CNR (the Institute of High Performance Computing and Networking of the Italian National Research Council). He is also a lecturer at the University of Calabria. His research interests focus on applications of distributed computing in the area of data mining, bio-inspired algorithms, big data, bioinformatics and cybersecurity. He was visiting researcher at University of Nottingham (United Kingdom), at Radbound University, Nijmegen (Netherlands) and at University of California (UCLA).   He is in the Editorial Board of Applied Soft Computing, Elsevier and published more than 100 papers in international conferences and journals among which IEEE Transactions on Evolutionary Computation, IEEE Transactions on Knowledge and Data Engineering, Parallel Computing, Information Sciences and Bioinformatics.