About Us

The SCOPE (Smart and resilient Computing for Physical Environments) Lab is managed by Prof. Abhishek Dubey at Vanderbilt University. We work on research problems related to large scale and distributed decision making in smart and connected cyber-physical systems such as transit systems, emergency response systems, and electrical power grids. As such we tackle a number of problems across the full stack of these cyber-physical systems including learning algorithms for failure detection, isolation and prognosis, fault-recovery and resilient design, middleware for distributed data management, performance management and focus on system-level assurance. We rely on a number of classical engineering principles including component-based design and model integrated computing in our work. You can read more about these methodologies in the paper on DREMS-ML and the paper on CHARIOT .

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. For further details see active projects and selected publications. See below for open positions.

 Open Positions

We are looking for motivated students to work on research projects in the areas of

  • artificial intelligence, machine learning, and data science for internet of things, cyber physical systems and smart cities.
  • role of blockchains and consensus protocols in resilient cyber physical systems design and operation.
  • resilient middleware design.
  • fault diagnosis and protection.
Please refer to the list of active projects and contact Prof. Abhishek Dubey if you are interested. Note that further information about the transit related projects is available at https://smarttransit.ai . Information about the statistical emergency response is available at https://statresp.ai

 Recent News

  • Information about the 2021 Spring Big Data Course Can be found on github

  • Check out the preprint of our recent paper on the impact of COVID-19 on public transit . Arxiv link .

  • We received a new NSF Smart and Connected Community grant to study and design novel transit solutions for communities. The project abstract is available at NSF site

  • We have received a new NSF grant to investigate the impact of COVID-19 on public transit systems. This grant is in collaboration with prof. Aron Laszka, UH, CARTA, Chattanooga and WeGo Nashville. See the abstract

  • Scott’s paper on Mechanisms for Outsourcing Computation was accepted at DEBS 2020. Acceptance rate was 30%. Congratulations. Slides are now available in youtube

 Active Projects

Addressing Transit Accessibility and Public Health Challenges due to COVID-19

The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability. A number of transit agencies have also begun to help the local food banks deliver food to shelters, which further strains the available resources if not planned optimally. At the same time, the lack of situational information is creating a challenge for riders who need to understand what seating is available on the vehicles to ensure sufficient distancing. In partnership with the transit agencies of Chattanooga, TN, and Nashville, TN, and Prof. Aron Lazka, University of Houston we are rapidly developing integrated transit operational optimization algorithms, which will provide proactive scheduling and allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). A key component of the research is the design of privacy-preserving camera-based ridership detection methods that can help provide commuters with real-time information on available seats considering social-distancing constraints. This project is funded by National Science Foundation. Learn More.

Assuring Cyber-Physical Systems with Learning Enabled Components

In recent years, AI based components are being heavily used in CPS. Despite their impressive capability, using them in safety critical applications is challenging because they learn from training data, and subtle changes in the images during testing could cause these components to predict erroneously, In addition, testing and verifying these components is complex and sometimes not possible and as a result safety and assurance case development of systems using these components is complicated. The group in collaboration with Prof. Gabor Karsai, Prof. Taylor Johnson, Prof. Xenofon Koutsoukos, Prof. Ted Bapty and Prof. Janos Sztipanovits have been focusing on methods to identify anomalies and recover from failures as well as develop system level safety assurance arguments. Till now, the SCOPE-Lab research group have developed a methodology to use a class of variational autoencoder called Beta-VAE in combination with dissimilarity metrics like Kullback-Leibler divergence to perform anomaly detection on the input data streams. Once an anomaly is detected we use a weighted simplex strategy to transition to a safe controller. Instead of using only a single control output (as in Simplex Architecture), we designed a weighted ensemble of the two control outputs. The weights are computed dynamically to improve the balance of safety versus performance of the system. We are also working on a methodology to semi-automate the generation of assurance cases for CPS with AI components. We have also built a test-bed called Deep NN-Car for experimentation and validation of these approaches. This project is funded by DARPA. Learn More.

Augmenting and Advancing Cognitive Performance of Control Room Operators for Power Grid Resiliency

The goal of the project is to investigate the mechanisms required to integrate recent advances from cognitive neuroscience, artificial intelligence, machine learning, data science, cybersecurity, and power engineering to augment power grid operators for better performance. Two key parameters influencing human performance from the dynamic attentional control (DAC) framework are working memory (WM) capacity, the ability to maintain information in the focus of attention, and cognitive flexibility (CF), the ability to use feedback to redirect decision making given fast changing system scenarios. The project will achieve its goals through analyzing WM and CF and performance of power grid operators during extreme events; augmenting cognitive performance through advanced machine learning based decision support tools and adaptive human-machine system; and developing theory-driven training simulators for advancing cognitive performance of human operators for enhanced grid resilience. We are building a new set of algorithms for data-driven event detection, anomaly flag processing, root cause analysis and decision support using Tree Augmented naive Bayesian Net (TAN) structure, Minimum Weighted Spanning Tree (MWST) using the Mutual Information (MI) metric, and unsupervised learning improved for online learning and decision making. In addition we use a discrete event model that captures the causal and temporal relationships between failure modes (causes) and discrepancies (effects) in a system, thereby modeling the failure cascades while taking into account propagation constraints imposed by operating modes, protection elements, and timing delays. This formalism is called Temporal Causal Diagram (TCD) and can model the effects of faults and protection mechanisms as well as incorporate fine-grain, physics-based diagnostics into an integrated, system-level diagnostics scheme. This project is in collaboration with Prof. Gautam Biswas from ISIS and Prof. Anurag Srivastava from Washington State University. This project is funded by National Science Foundation. Learn More.

Blockchain Middleware for Multi-stakeholder Cyber physical systems

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.

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.

Integrated Microgrid Control Platform

Dynamic formation of networked microgrids for heterogeneous components is not a solved problem. System integrators must often put together a microgrid from available components that communicate different information, at different rates, using different protocols. Due to variations in the microgrid architectures and their generation and load mix, each microgrid solution is customized and site-specific. Building on the Resilient Information Architecture Platform for Smart Grid, the goal of this project is to demonstrate a technology for microgrid integration and control based on distributed computing techniques, advanced software engineering methods, and state-of-the-art control algorithms that provides a scalable and reusable solution yielding a highly configurable Integrated Microgrid Control Platform (IMCP) . Our solution addresses the heterogeneity problem by encapsulating the specific details of protocols into reusable device software components with common interfaces, and the dynamic grid management and reconfiguration problem with advanced distributed algorithms that form the foundation for a decentralized and expandable microgrid controller. Investigators Prof. Gabor Karsai (PI), Prof. Abhishek Dubey (Co-PI) and Prof. Srdjan Lukic (Co-PI). This project has been funded in part by the DOD ESCTP program Learn More.

Interdisciplinary Approach to Prepare Undergraduates for Data Science Using Real-World Data from High Frequency Monitoring Systems

With support from the National Science Foundation (NSF) Improving Undergraduate STEM Education Program and in collaboration with Prof. Gautam Biswas, this project aims to help the incorporation of data science concepts and skill development in undergraduate courses in biology, computer science, engineering, and environmental science. Through a collaboration between Virginia Tech, Vanderbilt University, and North Carolina Agricultural and Technical State University, we are developing interdisciplinary learning modules based on high frequency, real-time data from water and traffic monitoring systems. The learning module topics will include Interdisciplinary Learning, Data Analytics, and Industry Partnerships. These topics will facilitate incorporation of real-world data sets to enhance the student learning experience and they are broad enough that they can incorporate other data sets in the future. Such expertise will better prepare students to enter the STEM workforce, especially those STEM professions that focus on smart and connected computing. The project will investigate how and in what ways the modules support student learning of data science. The project is also investigating how implementation of the modules varies across the collaborating institutions. It is expected that the project will define key considerations for integrating data science concepts into STEM courses and will host workshops to introduce faculty to these considerations and strategies so they can incorporate the learning modules into the STEM courses that they teach. Collaborators : V. Lohani, R. Dymond, & K. Xia (Virginia Tech); G. Biswas, Erin Hotchkiss, C. Vanags (Vanderbilt); M.K. Jha, N. Aryal, & E.H. Park (North Carolina Agricultural & Technical State University). This project is funded by National Science Foundation. Learn More.

Mobility for all - Harnessing Emerging Transit Solutions for Underserved Communities

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. A solution that holds great promise for improving public transit systems is the integration of fixed-route services with microtransit systems: multi-passenger transportation services that serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. However, most microtransit systems have failed in the past due to the lack of community engagement, inability to handle the uncertainty of operations when integrating the fixed transit, and inability to handle the system-level optimization challenges. The project takes a socio-relational approach to community engagement in collaboration with the Chattanooga Area Regional Transportation Authority (CARTA), design a community-centric micro-transit service that augments fixed-line public transit networks (improving transit accessibility), and demonstrate its effectiveness in the representative city of Chattanooga. The outcome of the project will be a deployment-ready software system that can be used by an agency to design and operate a micro-transit service effectively. The algorithmic toolchain will be complemented by mechanisms to optimally select the parameters and sustainably manage the data required by the algorithms. In addition, this project will provide a set of exemplar case studies and a validated social methodology to engage the community and learn their requirements, which will be fed into the algorithms. This will potentially impact a wide range of cities in the U.S. that do not have well-developed transit systems as the project will not only provide a reusable operations system but also demonstrate how integrated socio-technical research and strong community engagement can provide a pattern for sustainability and expansion. This project is funded by National Science Foundation. Learn More.

Model-based Intent-Driven Adaptive Software (MIDAS)

The goal of the project to develop a new approach to evolutionary software development and deployment that extends the results of model-based software engineering and provides an integrated, end-to-end framework for building software that is focused on growth and adaptation. The envisioned technology is based on the concept of a ‘Model Design Language’ (MDL) that supports the expression of the developer’s objectives (the ‘what’), intentions (the ‘how’), and constraints (the ‘limitations’) related to the software artifacts to be produced. The ‘models’ represented in this language are called the ‘design models’ for the software artifact(s) and they encompass more than what we express today in software models. We consider software development as a continuous process, as in the DevOps paradigm, where the software is undergoing continuous change, improvement, and extension; and our goal is to build the tools to support this. The main idea is that changes in the requirements will result in the designer/developer making changes in the ‘design model’ that will result in changes in the generated artifacts, or changes in the target system, at run-time, as needed. Such tool support is essential for developers as expensive, manual rework cannot be avoided without it. This project is in colaboration with Prof. Gabor Karsai, Prof. Daniel Balasubramanian at ISIS and Alessandro Coglio at Kestrel. This project is funded by the DARPA. Learn More.

Resilient Information Architecture Platform for the Smart Grid

The future of the Smart Grid for electrical power depends on computer software that has to be robust, reliable, effective, and secure. This software will continuously grow and evolve, while operating and controlling a complex physical system that modern life and economy depends on. The project aims at engineering and constructing the foundation for such software; a ‘platform’ that provides core services for building effective and powerful apps, not unlike apps on smartphones. The platform is designed by using and advancing state-of-the-art results from electrical, computer, and software engineering, will be documented as an open standard, and will be prototyped as an open source implementation. This project has been funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000666 and funded in part by a grant from Siemens, CT. Learn More.

Secure and Trustworthy Middleware for Integrated Energy and Mobility in Smart Connected Communities

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. The goal of this project is to develop a framework to ensure data privacy, data integrity, and trustworthiness in smart and connected communities. The innovativeness of the project lies in the collaborative effort between team of researchers from US and Japan together. As part of the project the research team is developing privacy-preserving algorithms and models for anomaly detection, trust and reputation scoring used by application providers for data integrity and information assurance. Towards that goal, we are also studing trade-offs between security, privacy, trust levels, resources, and performance using two exemplar applications in smart mobility and smart energy exchange in communities. This project is funded by National Science Foundation. Learn More.

Smart Emergency Response

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders. As such we are building StatResp – an open-source integrated tool-chain to aid first responders understand where and when incidents occur, and how to allocate responders in anticipation of incidents. This is important because first-responders are constrained by limited resources, and must attend to different types of incidents like traffic accidents, fires, and distress calls. 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. 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. As a consequence, statistical and algorithmic approaches to emergency response have received significant attention in the last few decades. Governments in urban areas are increasingly adopting methods that enable Smart Statistical Emergency Response, which are a combination of forecasting models and visualization tools to understand where and when incidents occur, and optimization approaches to allocate and dispatch responders. Please refer to a preprint of our survey paper for more information. 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.

Transactive Energy Systems

The goal of the project to develop transactive energy systems. Transactive energy systems have emerged as a transformative solution for the problems faced by distribution system operators due to an increase in the use of distributed energy resources and rapid growth in renewable energy generation. They are tightly coupled cyber and physical systems, which require resilient and robust financial markets where transactions can be submitted and cleared, while ensuring that erroneous or malicious transactions cannot destabilize the grid. In the last five years, we have used this research vertical to drive our research in the area of resilient decentralized CPS and have developed a novel middleware platform called TRANSAX by enabling participants to trade in an energy futures market, which improves efficiency by finding feasible matches for energy trades, reducing the load on the distribution system operator. It provides privacy to participants by anonymizing their trading activity using a distributed mixing service, while also enforcing constraints that limit trading activity based on safety requirements, such as keeping power flow below line capacity. One of the key innovations in TRANSAX was the development of a novel hybrid solver concept, combining the trustworthiness of distributed ledgers with the efficiency of conventional computational platforms. This hybrid architecture ensures the integrity of data and computational results as long as majority of the ledger nodes are secure while allowing the complex computation to be performed by a set of redundant and efficient solvers. We collaborate actively with Prof. Aron Lazka, University of Houston in this project. This project is funded in part by National Science Foundation and in Part by Siemens, CT. 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], [12], [13], [14]
  1. S. Eisele, T. Eghtesad, N. Troutman, A. Laszka, and A. Dubey, Mechanisms for Outsourcing Computation via a Decentralized Market, in 14TH ACM International Conference on Distributed and Event Based Systems, 2020.
  2. S. Eisele, T. Eghtesad, K. Campanelli, P. Agrawal, A. Laszka, and A. Dubey, Safe and Private Forward-Trading Platform for Transactive Microgrids, Transactions on Cyber-Physical Systems, 2020.
  3. S. Ramakrishna, C. Hartsell, M. P. Burruss, G. Karsai, and A. Dubey, Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems, Special Issue on the 2019 IEEE Symposium on Real-time Computing ISORC, 2020.
  4. 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.
  5. S. Hasan, A. Dubey, G. Karsai, and X. Koutsoukos, A game-theoretic approach for power systems defense against dynamic cyber-attacks, International Journal of Electrical Power & Energy Systems, vol. 115, 2020.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. S. Pradhan et al., CHARIOT: Goal-Driven Orchestration Middleware for Resilient IoT Systems, TCPS, vol. 2, no. 3, pp. 16:1–16:37, 2018.
  11. 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.
  12. 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.
  13. 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.
  14. 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, Senior Research Scientist at the Institute for Software-Integrated Systems. Abhishek directs the SCOPE lab at the Institute for Software Integrated Systems and is the co-lead of Vanderbilt Initiative for Smart Cities Operation and Research (VISOR). His broad research interest is in the design and operation of decision procedures for smart and connected communities with a focus on transportation and energy networks. As part of this research, he investigates big data operations, data analysis, machine learning, anomaly detection, and fault source isolation. His work has been funded by the NSF, NASA, DOE, ARPA-E. AFRL, DARPA, Siemens, Cisco, and IBM. Abhishek has been involved in the organization and program committee of several conferences including Middleware, AAAI, ICBC, SmartComp, ISORC, RTAS, ICCPS, and RTSS. He has also led the organization of the SCOPE Smart city workshop at CPSWeek from 2016-2020. He is a senior member of IEEE and he has published several peer-reviewed articles. Abhishek completed his Ph.D. 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. His research statement is available here.

Dr. Ayan Mukhopadhyay is a Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University, USA. Prior to this, he was a Post-Doctoral Research Fellow at the Stanford Intelligent Systems Lab at Stanford University, USA. He was awarded the 2019 CARS post-doctoral fellowship by the Center of Automotive Research at Stanford (CARS). Before joining Stanford, he completed his Ph.D. at Vanderbilt University’s Computational Economics Research Lab, and his doctoral thesis was nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research interests include multi-agent systems, robust machine learning, and decision-making under uncertainty applied to the intersection of CPS and smart cities. His work has been published in several top-tier AI and CPS conferences like AAMAS, UAI, and ICCPS. His work on creating proactive emergency response pipelines has been covered in the government technology magazine, won the best paper award at ICLR’s AI for Social Good Workshop, and covered in multiple smart city symposiums.

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.

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.

Sayyed Mohsen Vazirizade is a Post Doctoral Research Fellow in the Department of Computer Science and Computer Engineering at Vanderbilt University. Sayyed earned his PhD from the University of Arizona in Dec. 2019.


Sanchita Basak graduated with a Masters in Electrical Engineering from Scope Lab in 2020. Earlier, she received her M.Tech degree in Electrical and Electronics Communication Engineering from Indian Institute of Technology, Kharagpur in India in May 2015.

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.

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.

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.