University of Colorado Denver, Denver, CO, U.S.
I'm an assistant professor at University of Colorado Denver. My research focuses on cyber-physical systems, IoTs, and mobile computing. Before joining UCD, I worked as a research fellow at The University of Michigan at Ann Arbor, MI, USA. I'm a senior member of IEEE and a member of ACM.
I'm looking for students/postdocs to explore the cyber-physical world together. Contact me if you are interested!
The best way to reach is through my email at: email@example.com, or just stop by my office at Lawrence St. Center, LW816.
University of Colorado Denver, Denver, CO, U.S.
University of Michigan, Ann Arbor, MI, U.S.
Singapore University of Technology and Design, Singapore
University of Victoria, Victoria, BC, Canada
Ph.D. in Computer Science
Nankai University, Tianjin, China
B.Eng. in Computer Science
Tianjin University, Tianjin, China
My research focues on cyber-physical systems and Internet-of-Things, such as energy/power systems and ground/space vehicles. We exploit methodologies such as physical modeling and data analytics to diagnose/predict/optimize system performance. In the past, I made research contributions to mobile computing as well as wireless communications and networks. My research is in close collaboration with commercial companies such as General Motors, Microsoft, and NEC.
Please click on each project to find more details.
We aim to improve the reliability and safety of complex systems by designing and implementing novel cyber-physical solutions for on-board diagnostics. Our exploreations in this direction cover typical applications of vehicles, drones, e-scooters, industrial robots, mobile devices, etc. This project is in close collaboration with General Motors, Microsoft, and Glion.
Publications: UbiComp'21, SenSys'20, UbiComp'20, MobiSys'20, RAID'20, ICCPS'20, MobiCom'19.
Supported by: CRC Fellowship (CUDenver); Comcast Fellowship (CUDenver).
The prevalence of battery-powered systems such as electric vehicles, smartphones, and IoT devices has made batteries crucial to everyone’s daily life and business. Battery health, however, degrades over time, not only decreasing system reliability such as unexpected system shutoffs, but also causing overheating/gassing which, in turn, increases safety risks such as thermal runaway or even battery fire/explosion. To address these problems, we must monitor, prognose, and optimize battery health throughout the physical system life. Specifically, we develop R-AWARE, a recovery period-assisted battery health management that schedules system operation while considering both system/user requirements and battery health. R-AWARE will improve battery health via relaxation-aware battery scheduling of battery charging/discharging, and recovery-based thermal control.
Publications: ICCPS'19, e-Energy'19, MobiSys'17, ICCPS'17, ICCPS'16.
Supported by: NSF CNS-1739577.
Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. Current state-of-the-art still needs significant improvements in the architecture and algorithms of battery management before achieving the desired levels of efficiency and performance. To meet this need, we aim to (i) design a dynamically reconfigurable energy storage system to withstand harsh internal and external stresses; (ii) develop cell-level thermal management algorithms; (iii) develop efficient, dependable charge and discharge scheduling algorithms; (iv) develop comprehensive, diagnostic/prognostic algorithms with system parameters adjusted for making optimal decisions; and (v) build a testbed, implement and evaluate the proposed architecture and algorithms on the testbed.
Publications: RTSS'16, e-Energy'16, ICCPS'15, ICCPS'14, RTSS'13.
Supported by: NSF CNS-1446117.
Below is a selected list of my publications.
I work closely with students of cross-discipline backgrounds at CUDenver.
We have been organizing a seminar series on Cyber-Physical Systems with speakers from both academia and industry.
We are sorry to annouce that the seminar by Mr. James Yao from SAP has to be cancelled due to COVID-19. We expect to resume the seminars as soon as things are back to normal.
Abstract: As the transportation ecosystem, e.g., vehicles, road infrastructures, becomes increasingly smarter, systems that can enhance the interaction between humans and transportation systems are of paramount importance to road safety and efficiency. Meanwhile, ubiquitous computing devices, such as smartphones and wearables, are empowered by ever-increasing sensing and communication capabilities. Although many existing applications have attempted to harvest the ubiquitous sensory data for facilitating transportation applications, e.g., navigation apps, they are often risk-prone, coarse-grained, and for special purposes. This talk will cover two representative studies that are designed for improving the safety and efficiency of the transportation ecosystem in a scalable and efficient manner. First, I will introduce VSense, a real-time data analytics pipeline for detecting a vehicle’s steering maneuver (i.e., left/right turn and lane change) only with commodity smartphone sensors. Second, I will present TurnsMap, a crowdsensing framework for analysis of risks at intersections. TurnsMap demonstrates a ubiquitous sensing + machine learning framework that is adaptive to numerous smart transportation applications/.
Abstract: Modern connected and autonomous vehicles (CAVs) are equipped with an increasing number of Electronic Control Units (ECUs) that produce large amounts of data. The data is exchanged between ECUs via an in-vehicle network. Furthermore, CAVs do not only have physical interfaces, but also increased data connectivity to the Internet via their Telematic Control Units (TCUs) which make them accessible remotely just like mobile phones. As a result, an increasing number of attack vectors make vehicles an attractive target for hackers. Automotive cyber-security research is a relatively novel field which tries to respond to constantly rising threats with countermeasures. In this talk, we will give a primer on cyber-security in the automotive domain as well as discuss privacy concerns of the big data generated by cars.
Abstract: For the first time ever, we have more people living in urban areas than in rural areas. Based on this inevitable urbanization, the research in my group is aimed at addressing sustainability challenges related to urban mobility (e.g., energy consumption and traffic congestion) by data-driven modeling and applications with a Cyber-Physical-Systems (CPS) approach in the vision of Smart Cities. In this talk, I will focus on mobility modeling and resultant applications based on large-scale cross-domain CPS, e.g., cellular networks, payment systems, social networks, and transportation systems (including electric vehicles, taxis, buses, subway, private vehicles, Ubers). I will first show how cross-domain CPS systems can be collaboratively utilized to capture real-time urban mobility by a set of model integration techniques. Then I will show how the captured mobility can be used to design various urban mobile services to close the “loop”, from urban-scale ridesharing to for-hire vehicle dispatching, electric toll collection management, electric-vehicle charging recommendation, and emergency response under mobility anomaly. Finally, I will present some research challenges related to future cross- domain CPS in the context of the smart cities research.
Abstract: Autonomous multi-UAV networks will revolutionize the design of systems solutions for civil, entertainment, public services, security and other critical application areas. At NEC Labs, we are particularly interested in the application of such multi-UAV networks to public safety networks. Our goal is simple: how do we design, implement and deploy a reliable and flexible multi-UAV network that can meet the demands of first-responder networks? In this talk, I will introduce SkyLiTE, our multi-UAV network that is designed for public safety applications. SkyLiTE is one of the first efforts at a fully autonomous, untethered multi-UAV network that can be deployed on demand in challenging scenarios to achieve public safety mission objectives. Our SkyLiTE prototype consists of three important components: SkyHAUL, which is a high bandwidth, millimeter wave backhaul network that achieves 1Gbps of bandwidth across the entire multi-UAV network; SkyCORE, a UAV-optimized, lightweight LTE Evolved Packet Core network; and SkyRAN, a full-fledged LTE radio access network. SkyLiTE is deployed either to complement and augment existing terrestrial cellular networks in public safety scenarios, or as a standalone LTE network in areas not covered by fixed infrastructure.
Abstract: Mobile navigation services are used by billions of users around globe today. While GPS spoofing is a known threat, it is not yet clear if spoofing attacks can truly manipulate road navigation systems. In this work, we explore the feasibility of a stealthy manipulation attack against road navigation systems. The goal is to trigger the fake turn-by-turn navigation to guide the victim to a wrong destination without being noticed. Our key idea is to slightly shift the GPS location so that the fake navigation route matches the shape of the actual roads and trigger physically possible instructions. To demonstrate the feasibility, we perform controlled measurements by implementing a portable GPS spoofer and testing on real cars. The complete attack is validated by extensive trace-driven simulation and real-world driving tests. Deceptive user studies using a driving simulator also show that 95% of the participants follow the navigation to the wrong destination without recognizing the attack.
Abstract: Vision processing is a key component in automated driving systems. Most vision processing algorithms today, however, are not designed for safety-critical real-time applications. Considering these algorithms are typically both data- and computation-intensive and require advanced hardware platform, applying them to automated driving brings new architecture challenges. In this talk, I will discuss some architecture challenges and potential solutions to support the vision processing for real-time vehicle controls.
Abstract: Graphic processing units (GPUs) have seen wide-spread use in several computing domains as they have the power to enable orders of magnitude faster and more energy-efficient execution of many applications. Unfortunately, it is not straightforward to reliably adopt GPUs in many safety-critical cyber-physical systems that require predictable timing correctness, one of the most important tenets in certification required for such systems. A key example is the advanced automotive system where timeliness of computations is an essential requirement of correctness due to the interaction with the physical world. In this talk, I will describe several system-level and algorithmic challenges and our developed solutions on ensuring predictable timing correctness in GPU-accelerated systems.
Abstract: During the past decade, we are moving swiftly towards a mobile-centered world. This thriving mobile ecosystem builds upon the interplay of three important parties: the mobile user, OS, and app. These parties interact via designated interfaces many of which are newly invented for or introduced to the mobile platform. Nevertheless, as these new ways of interactions arise in the mobile ecosystem, what is enabled by these communication interfaces often violates the expectations of the communicating parties. This shakes the foundation of the mobile ecosystem and results in significant security and privacy hazards. In this talk, we describe our attempts to fill this gap by: 1.) securing the conversations between trusted parties, 2.) regulating the interactions between partially trusted parties, and 3.) defending the communications between untrusted parties. First, we deal with the case of two opposing parties, mobile OS and app, and analyze the Inter-Process Communication protocol (Binder) between them. We found that the OS is frequently making unrealistic assumptions on the validity (sanity) of transactions from apps, thus creating significant security hazards. We analyzed the root cause of this emerging attack surface and secured this interface by developing effective precautionary testing framework and runtime diagnostic tool. Then, we study the deficiency of how existing mobile user interact with app, a party he can only partially trust. We found that in the current mobile ecosystem, information about the same user in different apps can be easily shared and aggregated, which clearly violates the conditional trust mobile user has on each app. We address this issue by providing an OS-level extension that allows the user to track and control, during runtime, the potential flow of his information across apps. Last, we elaborate on how to secure the voice interaction channel between two trusted parties, mobile user and OS. The open nature of the voice channel makes applications that depend on voice interactions, such as voice assistants, difficult to secure and exposed to various attacks. We solve this problem by proposing the first system that provides continuous and usable authentication for voice commands. It takes advantage of the neck-surface acceleration to filter only those commands that originate from the voice of the owner.