About Us

The SCOPE (Smart and resilient Computing for Physical Environment) Lab is managed by Prof. Abhishek Dubey at Vanderbilt University. We work on research problems related to performance management, online failure detection, isolation and recovery in smart and connected cyber-physical systems, with a focus on transportation networks and smart grid. As such, we conduct research across distributed middleware, AI methods, component-based design methods, anomaly detection and system level assurance for these systems. Model integrated computing methods are crucial for our work as they allow us to describe the architecture of the system formally and then use it to generate a variety of artifacts: analytical models to conduct timing, reliability, security, performance, etc. analysis from a single source. You can read more about this methodology in the paper on DREMS-ML . Recently, we have been looking into system-level assurance when the control components in classical cyber-physical systems are replaced by data-driven components like neural networks.

Key contributions of this research group include the development and deployment of resilient decision support systems for Metropolitan Transit Authority in Nashville, a robust incident prediction and dispatch system developed for Nashville Fire Department and a privacy-preserving decentralized system for peer-to-peer energy exchange. Other contributions include middleware for online fault-detection and recovery in software intensive distributed systems and a robust software model for building cyber-physical applications, along with spatial and temporal separation among different system components, which guarantees fault isolation. This work has been adapted for fault detection and isolation in breaker assemblies in power transmission lines. The lab is funded in part by grants from NSF, NASA, DOE, ARPA-E. AFRL, DARPA, Siemens, Cisco and IBM.

 Recent News

  • Matthew Burruss Successfully defended his MS Thesis on Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions in March 2020. Congratulations. He has written about his work in a Medium Article.

  • JP and Mike’s paper about a middleware for decentralized routing was accepted at ICFC 2020. The papers title is On Decentralized Route Planning Using the Road Side Units as Computing Resources. Congratulations.

  • Geoff’s paper on emergency response titled: On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities was accepted at AAMAS 2020. Acceptance rate was 23%. Congratulations.

  • Shreyas Paper titled A Methodology for Automating Assurance Case Generation was accepted at TMCE 2020 to be hosted in Ireland. Congratulations.

  • Sanchita presented her paper titled Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks at Big Data 2019 special session on Data Mining.

 Selected Projects

Smart Emergency Response

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders. The problem of dispatching emergency responders to service accidents, fire, distress calls and crimes plagues urban areas across the globe. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. Further, most of these approaches are offline and fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Consider the classical problem of emergency response. The goal of responders is to minimize the variance in the operational delay between the time incidents are reported and when responders arrive on the scene.
Solving this problem requires not just sending the nearest emergency responder, but sometimes being proactive placing emergency vehicles in regions with higher incident likelihood. Sending the nearest available responder by euclidean distance ignores road networks and their congestion, as well as where the resources are stationed. Greedily assigning resources to incidents can lead to resources being pulled away from their stations, increasing response times if an incident occurs in the future in the area where responder should be positioned. Now, consider solving this problem when there is a high uncertainty in the veracity of the request due to either communication failures or due to the nature of the communication medium – in extreme disruptions the most common communication mechanism used is social media, however, the social media requests have a lot of uncertainty in terms of duplication, spatial location etc. Ultimately, the methods developed in this work can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge. We collaborate with Prof. Yevgeniy Vorobeychik, WUSTL, Prof. Hemant Purohit, GMU and Prof. Saideep Nannapaneni, Wichita State. This project is funded in part by the National Science Foundation. Learn More.

High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

The goal of the project is to enable the development and evaluation of tools to promote energy efficiency within mobility as a service system currently operational in Chattanooga. For this purpose, we are developing real-time data sets containing information about engine telemetry, including engine speed, GPS position, fuel usage and state of charge (electrical vehicles) from all vehicles in addition to traffic congestion, current events in the city and the braking and acceleration patterns. These high-dimensional dataset allow us to train accurate data-driven predictors using deep neural networks, for energy consumption given various routes and schedules. CARTA is planning to use these predictors for the energy optimization of its fleet of vehicles. We are planning to evaluate our framework by comparing the energy consumption, comfort, etc. of the routes and schedules found using our data-driven framework to existing routes and schedules. We believe that such predictors will revolutionize the transportation sector in a way that is similar to the capabilities provided by high-definition maps used in autonomous driving. This project complements the DOE national labs effort on vehicle energy consumption model by exploiting new data to investigate impacts of road/driver factors on vehicle energy consumption. We collaborate actively with Prof. Aron Lazka, University of Houston and Philip Pugliese, Chattanooga Regional Transit Authority and Prof. Yuche Chen from University of South Carolina in this project. This project is funded by the Department of Energy. Learn More.

Blockchains for Smart Communities

We are focusing on creating smart and connected community solutions, which provide participants the capability to not only exchange data and services in a decentralized and perhaps anonymous manner, but also provide them with the capability to preserve an immutable and auditable record of all transactions in the system. Blockchains form a key component of these platforms because they enable participants to reach a consensus on any state variable in the system, without relying on a trusted third party or trusting each other. Distributed consensus not only solves the trust issue, but also provides fault-tolerance since consensus is always reached on the correct state as long as the number of faulty nodes is below a threshold. However, it also introduces new assurance challenges such as privacy and correctness that must be addressed before protocols and implementations can live up to their potential. For instance, smart contracts deployed in practice are riddled with bugs and security vulnerabilities. Our group has been working on a number of projects in this interesting area, including work on transactive energy systems. Our research focuses on both the reusable middleware aspect as well as the foundational technologies required to ensure the rigor and correctness of the platform. We collaborate actively with Prof. Aron Lazka, University of Houston in this project. The work in this area has been supported by grants from Siemens, CT and National Science Foundation. Learn More.

All Projects

 Selected Videos


Deep NN Car with SafetyManager


Transit Analytics Dashboard Demonstration


T-Hub An Application for Public Transit


Transax-Blockchain and Transactive Energy


Mobility Application Built by Students in the Smart Cities Class


Incident Analytics and Response Management Dashboard


RIAPS Demo at 1st LF Energy Summit in Edinburgh, UK given by Prof. Gabor Karsai.


Modular Computing Platform for Public Transit

  Selected Publications

[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]
  1. G. Pettet, A. Mukhopadhyay, M. Kochenderfer, Y. Vorobeychik, and A. Dubey, On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities, in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2020, Auckland, New Zealand, 2020.
  2. F. Sun, A. Dubey, J. White, and A. Gokhale, Transit-hub: a smart public transportation decision support system with multi-timescale analytical services, Cluster Computing, vol. 22, no. Suppl 1, pp. 2239–2254, Jan. 2019.
  3. C. Hartsell et al., CPS Design with Learning-Enabled Components: A Case Study, in Proceedings of the 30th International Workshop on Rapid System Prototyping, RSP 2019, New York, NY, USA, October 17-18, 2019, 2019, pp. 57–63.
  4. S. Basak, A. Aman, A. Laszka, A. Dubey, and B. Leao, Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks, in Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM), 2019.
  5. Garcı́a-Valls Marisol, A. Dubey, and V. J. Botti, Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges, Journal of Systems Architecture - Embedded Systems Design, vol. 91, pp. 83–102, 2018.
  6. S. Pradhan et al., CHARIOT: Goal-Driven Orchestration Middleware for Resilient IoT Systems, TCPS, vol. 2, no. 3, pp. 16:1–16:37, 2018.
  7. A. Laszka, S. Eisele, A. Dubey, G. Karsai, and K. Kvaternik, TRANSAX: A Blockchain-Based Decentralized Forward-Trading Energy Exchanged for Transactive Microgrids, in 24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018, Singapore, December 11-13, 2018, 2018, pp. 918–927.
  8. D. Balasubramanian et al., DREMS ML: A wide spectrum architecture design language for distributed computing platforms, Sci. Comput. Program., vol. 106, pp. 3–29, 2015.
  9. T. Levendovszky et al., Distributed Real-Time Managed Systems: A Model-Driven Distributed Secure Information Architecture Platform for Managed Embedded Systems, IEEE Software, vol. 31, no. 2, pp. 62–69, 2014.
  10. N. Roy, A. Dubey, and A. S. Gokhale, Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting, in IEEE International Conference on Cloud Computing, CLOUD 2011, Washington, DC, USA, 4-9 July, 2011, 2011, pp. 500–507.
  11. N. Mahadevan, A. Dubey, and G. Karsai, Application of software health management techniques, in 2011 ICSE Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2011, Waikiki, Honolulu , HI, USA, May 23-24, 2011, 2011, pp. 1–10.
All Publications


Dr. Abhishek Dubey is an Assistant Professor of Electrical Engineering and Computer Science at Vanderbilt University. He is also a Senior Research Scientist at the Institute for Software-Integrated Systems and co-lead for the Vanderbilt Initiative for Smart Cities Operations and Research (VISOR). His research interest broadly lies in the area of performance management and secure and resilient operation of smart cyber-physical systems and smart cities with an emphasis on transportation and power networks. He has extensive experience in middleware, data analytics and toolchains for cyber-physical systems (NASA Aviation Safety Program, DARPA F6, AFOSR Resilient Software Systems and NSF Smart and Connected Community Programs). He is a senior member of IEEE and he has published several peer-reviewed articles. Abhishek completed his PhD in Electrical Engineering from Vanderbilt University in 2009. He received his M.S. in Electrical Engineering from Vanderbilt University in August 2005 and completed his undergraduate studies in electrical engineering from the Indian Institute of Technology, Banaras Hindu University, India in May 2001.

Geoffrey Pettet is a graduate student in the Department of Computer Science and Computer Engineering at Vanderbilt University, and works as a research assistant at the Institute for Software Integrated Systems. He completed his undergraduate studies in computer science at Vanderbilt University in May 2016.

Scott Eisele is a graduate student in Electrical Engineering at Vanderbilt University. He is a research assistant at the Institute for Software Integrated Systems. His research interests are in cyber-physical systems, and distributed computing. He completed an undergraduate degree in Mechanical Engineering at Brigham Young University in 2013.

Sanchita Basak is a graduate student in Department of Electrical Engineering and Computer Science at Vanderbilt University and a research assistant at the Institute for Software Integrated Systems. She received her M.Tech degree in Electrical and Electronics Communication Engineering from Indian Institute of Technology, Kharagpur in India in May 2015.

Shreyas Ramakrishna is a graduate student in the Department of Electrical Engineering at Vanderbilt University, and works as a research assistant at the Institute for Software Integrated Systems. He received his M.S. degree in Electrical Engineering from Technical University Kaiserslautern (Germany) in June 2015 and completed his undergraduate studies in Electrical engineering from Visvesvaraya Technological University, India in 2012.

Michael Wilbur is a graduate student in the Department of Electrical Engineering and Computer Science at Vanderbilt University and works as a research assistant at the Institute for Software Integrated Systems. He received his M.S. degree in Structural Engineering from Northwestern University in December 2018 and his Bachelor’s degree in Civil Engineering from The University of Notre Dame in 2012.

Nithin is a PhD student at Scope Lab. He has over 16 years professional experience, working in various companies like Cisco ,Honeywell and GE transportation and have extensive knowledge on embedded software and iOS development across industry verticals like Mobile Communication, Avionics, Railroad and Computer Networking. Previously before joining to Vanderbilt University, he was working as a Software Engineer 4 at Cisco Systems Ltd, where he was responsible for debugging support and developing new features for five switch fabric modules of Cisco’s NCS6K core router and application of machine learning on Cisco routers for hardware failure prediction.


Subhav graduated with PhD from our research group in 2017. His dissertation was titled Algorithms and techniques for managing extensibility in cyber-physical systems. He is working with Uber now.

Fangzhou Sun graduated with Phd in computer science at Vanderbilt University in 2018. His dissertation was titled Algorithms for Context-Sensitive Prediction, Optimization and Anomaly Detection in Urban Mobility. He received his M.S. degree in computer science from Vanderbilt University in 2015 and completed his undergraduate studies in computer science from Nanjing University, China in 2013. His main research topics include: (1) developing and managing applications, analytics tool boxes and platforms for smart city; (2) creating and integrating cyber-attack detection systems for heterogeneous web-based applications. He works with Facebook now.

Chinmaya Samal completed his MS in Computer Science from Vanderbilt in 2020. He finished his undergraduate studies in Information Technology from Veer Surendra Sai University of Technology, India in May 2016.

Sagar Shah completed his MS in Electrical Engineering at Vanderbilt University in 2020. Before this he received his Bachelor’s degree in Electrical Engineering from Rajiv Gandhi Technical University, Indore (India) in June 2017.