Ioannis (Yannis) Ch. Paschalidis
PIPP Phase I: Predicting and Preventing Epidemic to Pandemic Transitions
The COVID-19 pandemic and its effects, both in terms of the millions of lives lost and the trillions in estimated costs, are a recent example of the devastation pandemics can cause. Any discernible progress in the prediction, early detection, and rapid response would have significant impacts on human welfare. The overarching goal of this project […]
High-Fidelity Self-Learning Tool for Residential Load and Load Flexibility Forecasting
The project will research, develop, and demonstrate technology that enables the modulation of controllable household loads, to provide multiple grid services, including peak capacity management, ramp support, and frequency regulation. We will show how the fusion of data from multiple sources, including communicating thermostats, smart appliances, weather forecasts, utility bills, solar production data, and interval […]
Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems
Although pandemics have threatened human civilization since ancient times, how to predict and prevent them remains one of the most pressing challenges, calling out for innovative insights and practices. Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and even individual behavioral […]
A New Risk Assessment and Management Paradigm (NewRAMP) in Electricity Markets
The grid relies on conventional bulk power plants to provide the flexibility to operate power systems reliably. These assets can guarantee available capacity except in rare events. The existing risk management strategy protects against those rare events and aligns well with conventional technologies. New risk management strategies are needed due to the shift in grid […]
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting […]
Optimizing and Learning Strategies for Protein Docking
Protein docking is defined as predicting the three-dimensional structure of the docked complex based on knowledge of the structure of the components. Experimental techniques for this purpose are often expensive, time-consuming, and in some cases, not feasible; hence the need for computational docking methods. The problem of finding the docked conformation is generally formulated as […]
Neuro-Autonomy: Neuroscience-inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots
State-of-the-art Autonomous Vehicles (AVs) are trained for specific, well-structured environments and, in general, would fail to operate in unstructured or novel settings. This project aims at developing next-generation AVs, capable of learning and on-the-fly adaptation to environmental novelty. These systems need to be orders of magnitude more energy efficient than current systems and able to pursue complex goals in […]
QuBBD: From Personalized Predictions to Better Control of Chronic Health Conditions
The United States spends twice as much annually on health care than the next-highest spender but significantly under-performs in quality of care metrics, such as life expectancy and infant mortality. Hospital care accounts for about a third of U.S. health care spending. It has been estimated that nearly $30 billion in hospital care costs each […]
CPS: Breakthrough: A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments
Connected Automated Vehicles (CAVs), often referred to as “self-driving cars”, will have a profound impact not only on transportation systems, but also in terms of associated economic, environmental, and social effects. As with any such major transformative undertaking, quantifying the magnitude of its expected impact is essential. The first part of this project aims at […]
Smart and Connected Health (SCH) PI Workshop, 2017
This proposal supports the NSF Smart and Connected Health (SCH) Principal Investigators Meeting and Visioning Workshop in Boston, MA on March 2-22, 2017. The meeting brings together principal investigators (PI) of projects awarded by the Smart and Connected Health program from 2011 to 2016. The SCH program was set up in order to bridge gaps […]