Collaborative Research: CPS: Medium: Real-Time Crowd-Sourced Geospatial Digital Twin for Cyber-Physical Systems
Lead PI:
Bin Li
Abstract
This research project focuses on enhancing the way vital information is delivered to smart mobile devices?such as smartphones and tablets. With the advancement of technology, there is a growing necessity for these devices to receive various types of information (like images, videos, and texts) instantly and effectively. One promising approach to achieving this is through the use of Geospatial Digital Twins (GDT), which are digital models of physical environments.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2331105
Collaborative Research: CPS: Medium: Real-Time Crowd-Sourced Geospatial Digital Twin for Cyber-Physical Systems
Randall Berry
Lead PI:
Randall Berry
Abstract
This research project focuses on enhancing the way vital information is delivered to smart mobile devices?such as smartphones and tablets. With the advancement of technology, there is a growing necessity for these devices to receive various types of information (like images, videos, and texts) instantly and effectively. One promising approach to achieving this is through the use of Geospatial Digital Twins (GDT), which are digital models of physical environments.
Randall Berry
Randall Berry joined Northwestern University in 2000, where he is currently the Chair and John A. Dever Professor in the Department of Electrical and Computer Engineering. His research interests span topics in wireless communications, computer networking, network economics, and information theory. Dr. Berry received the M.S. and PhD degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 1996 and 2000, respectively, where he was part of the Laboratory for Information and Decision Systems. His undergraduate education was at the University of Missouri-Rolla, where he received the B.S. degree in Electrical Engineering in 1993. In 1998 he was on the technical staff at MIT Lincoln Laboratory in the Advanced Networks Group. Dr. Berry is the recipient of a 2003 CAREER award from the National Science Foundation and is an IEEE Fellow.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2331106
CPS: Medium: Compositional Learning and Control of Networked Cyber-Physical Systems
Lead PI:
David Fridovich-Keil
Abstract
This project aims to develop theoretical frameworks and design practical algorithms for learning data-driven models and control strategies in networked cyber-physical systems. In particular, the project is grounded in power distribution systems, whose modular structure and hierarchical positioning of subsystems in subnetworks make them ideal candidates for compositional learning and control design, in which dynamical properties and performance guarantees propagate among hierarchical subsystems.
Performance Period: 06/01/2024 - 05/31/2027
Award Number: 2409535
CPS: Medium: Self-organizing battery-free and networked origami microfliers
Lead PI:
Vikram Iyer
Abstract
This project develops a critical technological capability that does not currently exist ? the ability to accurately deploy a large number of sensors to otherwise inaccessible locations from aerial platforms. Specifically, it creates fully functional, battery-free microfliers that communicate with each other to self-organize and coordinate their descent, enabling precise deployment of wireless sensor networks over large spatial scales.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2401177
Collaborative Research: CPS: Small: Neuro-Symbolic Bridge: From Perception to Estimation & Control
Lead PI:
Radoslav Ivanov
Abstract
Modern cyber-physical systems (CPS) are increasingly neuro-symbolic. A typical CPS control pipeline consists of 1) neural networks (NNs), used to process raw high-dimensional data, such as camera images, and 2) downstream symbolic components, such as state estimation and control, that take the NNs' output in order to close the loop. However, there is a fundamental mismatch between the uncertainty on the NN outputs and the assumptions of the downstream components.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2403615
CPS: Medium: Chips for Efficient and Robust Navigation
Lead PI:
Sertac Karaman
Abstract
Energy-constrained Cyber-Physical Systems (CPS), ranging from smartphones and lightweight augmented reality (AR)/ virtual reality (VR) headsets to insect-size flying robots and pill-sized medical micro-robots, could transform a diverse set of applications in consumer electronics, targeted medication delivery, search and rescue missions, and space exploration. All these applications place severe constraints on the size, weight and power of on-board computers and sensors.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2400541
NSF-DST: CPS: TTP Option: RE-GAIN: An Adaptive, Medical CPS Platform Integrating E-Textile Wearables, Virtual Reality, and AI for Stroke Rehabilitation
Kunal Mankodiya
Lead PI:
Kunal Mankodiya
Abstract
Stroke is the second leading cause of death and disability globally and poses a significant challenge to the affected individuals as well as society at-large due to its extensive socioeconomic burden. In response, this project (RE-GAIN) is developed as an international collaboration between the United States and India to transform the landscape of stroke rehabilitation for young adult survivors.
Kunal Mankodiya

Dr. Kunal Mankodiya is a Professor of Biomedical Engineering at the University of Rhode Island and Director of the Wearable Biosensing Lab (WBL). His research focuses on developing wearable and health technologies to monitor the brain, body, and behavior. WBL conducts transformative research with support from federal agencies like NSF and NIH, state funding from RI Commerce, and foundation grants. A notable example of his work is the development of a smart glove system for Parkinson’s disease assessment, funded by the prestigious NSF CAREER and PFI-TT Awards. He is currently expanding this research internationally, collaborating with partners in India to advance Smart Glove technologies. To date, he has mentored 18 graduate students, 3 postdocs, and published over 115 research papers. 
As a prior co-founder and now scientific advisor for WellAware, a Rhode Island-based digital health startup, Dr. Mankodiya is passionate about bridging clinical needs with innovation. Through programs like HealthHacks and the Symposium on Smart Health & Wearables, he inspires the next generation to pursue med-tech innovation and entrepreneurship, advancing personalized medicine and accessible healthcare globally. Born in India, Dr. Mankodiya brings a global perspective, with an MS and PhD from Uni-Luebeck, Germany, and postdoctoral research at Carnegie Mellon University, USA.

Performance Period: 06/01/2024 - 05/31/2027
Award Number: 2413838
CPS: MEDIUM: Certified Robust Learning for Multi-Agent Planning and Control
Lead PI:
Yiannis Kantaros
Abstract
Recent advances in artificial intelligence (AI), perception, and decision-making theory have facilitated the development of autonomous agents (e.g., ground, and aerial robots) capable of collaborating on complex tasks such as delivery, search-and-rescue, transportation, and manufacturing, in unknown environments. To handle environmental uncertainty effectively, decision-making in multi-agent systems often relies on AI techniques, particularly reinforcement learning (RL). RL translates perceptual feedback and shared information into individual control decisions.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2403758
CPS: Small: NSF-DST: Safety-Aware Behaviour-Driven Reinforcement Learning Based Autonomous Driving Solution for Urban Areas
Xiaopeng Li
Lead PI:
Xiaopeng Li
Abstract
This NSF Cyber-Physical Systems (CPS) project will support research that intends to enhance the operation of automated vehicles (AV) swarms in various traffic environments, including structured intersections (e.g., intersections in the US) and unstructured intersections (e.g., intersections in India). The project will examine the 'Tragedy of the Commons (ToC)'? a situation where AVs, while smart on their own, might cause significant traffic oscillation and disorder when they all use the same logic.
Xiaopeng Li

Dr. Xiaopeng (Shaw) Li is currently a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison (UW-Madison). He served as the director of National Institute for Congestion Reduction (NICR) before. He is a recipient of a National Science Foundation (NSF) CAREER award. He has served as the PI or a co-PI for a number of federal, state, and industry grants, with a total budget of around $30 million. He has published over 110 peer-reviewed journal papers. His major research interests include automated vehicle traffic control and connected & interdependent infrastructure systems. ). He received a B.S. degree (2006) in civil engineering from Tsinghua University, China, an M.S. degree (2007), and a Ph.D. (2011) degree in civil engineering along with an M.S. degree (2010) in applied mathematics from the University of Illinois at Urban-Champaign, USA.

Performance Period: 05/01/2024 - 04/30/2027
Award Number: 2343167
CPS: Medium: Federated Learning for Predicting Electricity Consumption with Mixed Global/Local Models
Lead PI:
Alex Olshevsky
Abstract
This proposal aims to integrate federated learning with power systems, leveraging distributed data from numerous devices to better predict electricity consumption and lower the cost of generation. Our goal is to take advantage of data sources which are becoming more common in the power domain, namely the proliferation of smart meters which record electricity consumption at 15- minute intervals. We will develop machine learning methods which predict electricity consumption at the day-ahead scale from this data.
Performance Period: 05/01/2024 - 04/30/2027
Award Number: 2317079
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