SLES: Whitebox Testing, Debugging, and Repairing for Multi-module Autonomous Vehicles in Near-Collision Traffic Scenarios
Tianyi Zhang
Lead PI:
Tianyi Zhang
Abstract
The advances in artificial intelligence and machine learning have empowered the development and adoption of autonomous vehicles, including self-driving cars and delivery drones. However, the increasing number of incidents involving autonomous vehicles has raised public concerns about their safety and reliability. Ensuring end-to-end safety of such systems is critical but challenging given the sophisticated multi-module systems operating in these vehicles and the enormous number of possible traffic scenarios, especially complex and previously unseen scenarios.
Tianyi Zhang
Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University. At Purdue, he leads the Human-Centered Software Systems Lab, where he and his students develop intelligent systems that synergize human expertise with machine intelligence, with a particular focus on improving programming productivity and the robustness and safety of modern software. Prior to that, he was a Postdoctoral Fellow at Harvard University and obtained his Ph.D. in CS from UCLA in 2019. His work has been recognized with an NSF Career Award, an Amazon Research Award, and Best Paper Honorable Mention Awards from SIGCHI and VAHC.
Performance Period: 10/01/2024 - 09/30/2027
Award Number: 2416835
Collaborative Research: SLES: Improving Safety by Synthesizing Interacting Model-based and Model-free Learning Approaches
Kyriakos G Vamvoudakis
Lead PI:
Kyriakos G Vamvoudakis
Abstract
Learning-enabled systems have been rapidly increasing in size, acquiring new capabilities. These systems are typically deployed in complex operating environments, so their safety is extremely important. Ensuring safety requires that systems are robust to extreme events while we can monitor them for anomalous and unsafe behavior. While traditional machine learning systems are evaluated pointwise with respect to a fixed test set, such static coverage provides only limited assurance when exposed to unprecedented conditions in complex operating environments.
Kyriakos G Vamvoudakis

Kyriakos G. Vamvoudakis was born in Athens, Greece. He earned his Diploma in Electronic and Computer Engineering (equivalent to a Master of Science) from the Technical University of Crete, Greece, in 2006, graduating with highest honors. After relocating to the United States, he pursued further studies at The University of Texas at Arlington under the guidance of Frank L. Lewis, obtaining his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011, respectively. From May 2011 to January 2012, he served as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and the Automation and Robotics Research Institute. Between 2012 and 2016, he was a project research scientist at the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. He then joined the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech as an assistant professor, a position he held until 2018.

He currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy. 

He has received numerous prestigious awards, including the 2019 ARO YIP Award, the 2018 NSF CAREER Award, the 2018 DoD Minerva Research Initiative Award, and the 2021 GT Chapter Sigma Xi Young Faculty Award. His work has also been recognized with several best paper nominations and international awards, such as the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. Dr. Vamvoudakis has served on various international program committees and has organized special sessions, workshops, and tutorials for several international conferences. He is the Editor-in-Chief of Aerospace Science and Technology and currently serves on the IEEE Control Systems Society Conference Editorial Board. Additionally, he is an Associate Editor for several journals, including Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Artificial Intelligence, Neural Networks, and the Journal of Optimization Theory and Applications. He is also a Senior Guest Editor for the IEEE Open Journal of Control Systems for the special issue on the intersection of machine learning with control. Previously, Dr. Vamvoudakis has served as a Guest Editor for various special issues, including those in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Intelligent Transportation Systems. He is a registered Professional Engineer (PE) in Electrical/Computer Engineering, a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.

Performance Period: 10/01/2024 - 09/30/2027
Award Number: 2415479
CPS: Medium: Collaborative Research: Real-time Subsurface Sensing with Cognitive Networked Robotic System
Lead PI:
Dalei Wu
Abstract
This Cyber-Physical Systems (CPS) project will investigate cognitive and cooperative sensing and imaging systems for rapid near real-time subsurface infrastructure monitoring and mapping. This research advances the frontier of subsurface sensing to a new paradigm enabling practical large-area surveys not possible by existing means.
Performance Period: 09/01/2024 - 08/31/2027
Award Number: 2345852
CPS: SMALL: NSF-MeitY: 5G Enabled Real-Time Digital Twins of Dynamic Construction Sites
Lead PI:
Pratik Chaudhari
Abstract
This Cyber-Physical Systems (CPS) grant, a collaboration between the US National Science Foundation and the Ministry of Electronics and Information Technology of the Government of India (NSF-MeitY), supports research to develop a digital twin of dynamic environments such as construction sites using multiple unmanned aerial and ground robots equipped with cameras and 5G radios. This research can (a) enhance worker safety by identifying potential hazards and (b) improve construction efficiency by monitoring and optimizing resources devoted to different tasks.
Performance Period: 09/01/2024 - 08/31/2027
Award Number: 2415249
Collaborative Research: CPS: Medium: Ensure Privacy and Truthfulness in Self-interested Multi-agent Cyber-physical Systems
Lead PI:
Zhaojian Li
Abstract
In many multi-agent Cyber-Physical Systems (CPS), the agents are self-interested in that their individual costs/rewards are not fully aligned with the network-level global objective function. A typical example is the large-scale coordinated charging of electric vehicles, where the network-level goal usually focuses on filling the overnight decrease in background power demand while individual electric vehicles only mind their own charging costs. Therefore, an opportunistic self-interested agent may be tempted to lie in information sharing to reduce its local cost.
Performance Period: 09/01/2024 - 08/31/2027
Award Number: 2422313
Collaborative Research: CPS: Medium: Ensure Privacy and Truthfulness in Self-interested Multi-agent Cyber-physical Systems
Lead PI:
Yongqiang Wang
Abstract
In many multi-agent Cyber-Physical Systems (CPS), the agents are self-interested in that their individual costs/rewards are not fully aligned with the network-level global objective function. A typical example is the large-scale coordinated charging of electric vehicles, where the network-level goal usually focuses on filling the overnight decrease in background power demand while individual electric vehicles only mind their own charging costs. Therefore, an opportunistic self-interested agent may be tempted to lie in information sharing to reduce its local cost.
Performance Period: 09/01/2024 - 08/31/2027
Award Number: 2422312
NSF-DST:CPS:Small: Equitable Energy Access via Energy Communities and Microgrids: A Cyber Physical System Approach
Lang Tong
Lead PI:
Lang Tong
Abstract
This project develops a theoretical foundation, computational approaches, and practical solutions for equity-regarding cyberphysical systems. As a specific application, the project focuses on equitable energy access in energy communities and microgrids where energy demand and resources are co-optimized to achieve an optimal tradeoff among economic efficiency, equitable access among consumers with income inequality, and operation resilience. The project is structured into three thrusts.
Performance Period: 07/01/2024 - 06/30/2027
Award Number: 2412776
CPS:MEDIUM: Radar-based Perception and Control for Small Autonomous Robots
Lead PI:
Deepak Vasisht
Abstract
This project aims to establish millimeter wave (mmWave) radar as a first-class perception and control tool for small-sized robots and drones. Small-sized robots and drones are key enablers for many emerging applications ? precision agriculture, inventory management in smart warehouses, drone-based delivery of goods, and search & rescue operations. Perception and control of such robots and drones is fundamentally challenged by their over-reliance on optical sensors, low power budgets, and limited computational capabilities.
Performance Period: 07/15/2024 - 06/30/2027
Award Number: 2414227
CPS: Medium: Artificial-intelligence-enabled Atomic Force Microscopy (AI-AFM)
Lead PI:
Juan Ren
Abstract
Mechanical forces have long been implicated in regulating basic cellular and molecular processes such as cell proliferation, differentiation and DNA-protein bonding. Understanding the basic working mechanism of these processes can lead to breakthrough improvements in biochemical and biomedical sciences and engineering. Atomic force microscopy (AFM), by far, is the most suitable platform for nanomechanical characterization of biological materials owing to its capability to exert precisely controlled force at desired locations and sense the sample response.
Performance Period: 07/01/2024 - 06/30/2027
Award Number: 2409359
Collaborative Research: CPS: Medium: Real-Time Crowd-Sourced Geospatial Digital Twin for Cyber-Physical Systems
Jia (Kevin) Liu
Lead PI:
Jia (Kevin) Liu
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.
Jia (Kevin) Liu
Jia (Kevin) Liu is an Associate Professor in the Dept. of Electrical and Computer Engineering at The Ohio State University (OSU) and an Amazon Visiting Academic (AVA) with Amazon.com. He received his Ph.D. degree from the Dept. of Electrical and Computer Engineering at Virginia Tech in 2010. From Aug. 2017 to Aug. 2020, he was an Assistant Professor in the Dept. of Computer Science at Iowa State University (ISU). He currently serves as the Managing Director of the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) at OSU. He is a Co-Principal Investigator of the NSF TRIPODS D4 (Dependable Data-Driven Discovery) Institute at ISU. He is also a faculty investigator affiliated with the NSF ARA Wireless Living Lab PAWR Platform between ISU and OSU, and the Institute of Cybersecurity and Digital Trust (ICDT) at OSU. Dr. Liu's research areas include theoretical machine learning, stochastic network optimization and control, and performance analysis for data analytics infrastructure and cyber-physical systems. Dr. Liu is a senior member of IEEE and a member of ACM. He has received numerous best paper awards at top venues, including IEEE INFOCOM'19 Best Paper Award, IEEE INFOCOM'16 Best Paper Award, IEEE INFOCOM'13 Best Paper Runner-up Award, IEEE INFOCOM'11 Best Paper Runner-up Award, and IEEE ICC'08 Best Paper Award. He has also received multiple honors of long/spotlight presentations at top machine learning conferences, including ICML, NeurIPS, and ICLR. His joint work with IBM Research was selected to receive the IBM Pat Goldberg Memorial Best Paper Award Distinction of Honorable Mention in 2024. Dr. Liu is an NSF CAREER Award recipient in 2020, a winner of the DARPA Young Faculty Award (YFA) in 2024, and a winner of the Google Faculty Research Award in 2020. He received the LAS Award for Early Achievement in Research at Iowa State University in 2020, and the Bell Labs President Gold Award. Dr. Liu is the Lead Editor of the Special Issue on AI and Networking of IEEE/ACM Transactions on Networking in 2015. He is an Associate Editor for IEEE Transactions on Cognitive Communications and Networking. He has served the TPC for numerous top conferences, including ICML, NeurIPS, ICLR, ACM SIGMETRICS, IEEE INFOCOM, and ACM MobiHoc. His research is supported by NSF, DARPA, AFOSR, AFRL, ONR, Google, Meta, and Cisco.
Performance Period: 06/15/2024 - 05/31/2027
Award Number: 2331104
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