Keynotes
Title: Data-Driven ROADS (Resilient Operation of Active Distribution Systems)
Abstract
In the absence of real-time visibility and adequate control, the increasing proliferation of distributed energy resources can play havoc with the distribution system, particularly, its voltage. This talk will describe how system-wide information obtained from a select few real-time sensors using machine learning can be used to optimize reactive power regulation for achieving coordinated, robust, and fast voltage control of active distribution systems. To ensure trust in the machine learning-based approach, formal guarantees of performance will also be established. The talk will conclude by demonstrating additional system-wide benefits that an integrated data-driven approach towards monitoring and control provides to power utilities responsible for operating large, complex distribution systems in a reliable and resilient manner.
Personalised health driven by digital health systems and multi-source health/environmental data, ML/AI/DL analytics and predictive models
Bio:
TBA
Abstract
The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.
Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.
In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.
