About us

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ScopeLab is a research group directed by Prof. Abhishek Dubey at Institute for Software Integrated Systems, Vanderbilt University working on design and operation of Cyber-physical Systems (CPS) with Artificial Intelligence (AI) components (AI-CPS). Our application domains include public transit systems, emergency response systems and power grid. For these systems we investigate principled design, operation and optimization methods that not only consider the system operations, but also consider resilience, performance and assurance. Our work is part of the broad research into smart and connected communities (SCC) and Cyber-Physical Systems (CPS). These activities have been enabled by the rapid and transformational changes driven by innovations in smart sensors, such as cameras and air quality monitors, which are now embedded in almost every physical device and system we use, from watches and smartphones to automobiles, homes, roads, and workplaces.

Recent Updates

Research Verticals

Smart Incident Response
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Planning and preparation in anticipation of urban emergency incidents are critical because of the alarming extent of the damage such incidents cause as well as the sheer frequency of their occurrence. Incident response is typically optimized as part of designing ERS pipelines but can have a high variance depending upon the location of the first responders, other incidents in the call chain, the severity of the call, traffic conditions, and weather conditions. Our research on algorithmic approaches to ERS spanning the past six years has developed proactive stationing and principled dispatch strategies to reduce the overall response times. The system depends upon incident data, temporal data (weather and traffic), and static roadway data and has to contend with changes in incident distributions and communication failures.

Smart Mobility
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Transit Agencies struggle to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints. We have been working for the last several years to design AI-based scheduling systems to solve the problem of allocating vehicles and drivers to transit services, scheduling vehicle maintenance, and electric-vehicle charging, proactive stationing, and dispatch of vehicles for fixed-line service to mitigate unscheduled maintenance and unmet transit demand, aggregating on-demand transit requests, and dispatching and routing on-demand vehicles. Similar to the incident response, the decision support systems face key challenges - environments are non-stationary and difficult to predict due to human factors and complex processes affecting transit demand and traffic as well as unscheduled maintenance and accidents; simulations are expensive and complex as city-scale simulations need to consider millions of individuals and vehicles.

Smart Power
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Resilience, scalability and safety of operations is critical for power grid. Ensuring that the system operates resiliently while handling the challenges of both component failures, environmental uncertainty and adversarial attacks is not easy. The challenge in this domain include data corruption, failure and misoperation of protection equipment, and the possibility of misclassification of a fault by the AI system. System integration and operation of the grid remains open challenges. Further, the emerging trend in this domain is networked microgrids that can island or be connected together to respond to adversities. However, 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.

Research Horizontals

AI for Cyber-Physical Systems

The motivation for this research area is the use of Artificial Intelligence (AI) in cyber-physical systems (CPS). The advantage of AI methods is their ability to handle high-dimensional state-space and learn decision procedures/control algorithms from data rather than models. This is important as real-world state spaces and environments are often complex, dynamic, and hard to model. Despite their impressive capability, using them in CPS is challenging because of two reasons, (1) it is likely that the data and operating assumptions made during the design of the system are not complete, and the system may have failures at runtime; and (2) safety and assurance case development of systems using these components is complicated because traditional design methods focus only on training, testing and deploying individual components of the system and do not focus on the integrated system level assurance. We co-design system architecture as well as novel state estimators, predictors, and decision procedures for the different research verticals we investigate - proactive emergency response systems, transit management systems, and electric power grids. As we develop these systems and methods, we consider the societal context in which they are being used and investigate principles that allow us to reason about resilience, assurance, and fault diagnostics for AI-CPS.

Resilient Design and Operation of Complex Cyber-Physical Systems

Cyber-Physical Systems encompass all modern engineered systems, including smart transit, smart emergency response, smart grid. The big issue in these systems is the construction and operation of the system in a safe and efficient manner. There have been many different approaches taken by the research community over the years. The approach of this lab has been to focus on component-based software engineering (CBSE) efforts for these systems. The guiding principles of CBSE are interfaces with well defined execution models, compositional semantics and analysis. However, there are a number of challenges that have to be resolved (a) performance management, (b) modularization and adaptation of the design as the requirements and environment changes, (c) safe and secure design of the system itself and ensuring that new design and component additions can be compositionally analyzed and operated during the life cycle of the system, (d) fault diagnostics and failure isolation to detect and triage problems onlines and (e) reconfiguration and recovery to dynamically adapt to failures and environmental changes to ensure the safe completion of mission tasks.

Decentralized Operations for Cyber-Physical Systems

One of the fundamental focus of research in our lab is the design and development of algorithms and framework to enable decentralized operations in large scale cyber-physical systems. We note that a number of these systems require multiple entities and stakeholders to participate and as such the systems are vulnerable to data and operational disruptions, which are common in centralized architectures. These challenges have led to increasing focus on SCC platforms that 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. Such transactive platforms are actively being suggested for use in Healthcare, Smart Energy Systems, and Smart Transportation Systems. These platforms can provide support for privacy-preserving and anonymizing techniques, such as differential privacy, fully homomorphic encryption, and mixing. Further, the immutable nature of records and event chronology in these platforms provides high rigor and auditability. Lastly, the decentralized nature of these platforms ensures that any adversary needs to compromise a large number of node to take control of the system.

Active Projects

Optimizing Fixed Line and On-demand Services for Public Transportation

We are developing algorithms to perform system-wide optimization, (the microtransit, fixed line and paratransit) focusing on three objectives - minimizing energy per passenger per mile, minimizing total energy consumed, and maximizing the percentage of daily trips served by public transit. While it is possible to optimize these decisions separately as prior work has done, integrated optimization can lead to significantly better service (e.g., synchronizing flexible courtesy stops with microtransit dispatch for easy transfer). However, this is hard due to uncertainty of future demand, traffic conditions etc. We address these challenges using state-of-the-art artificial intelligence, machine learning, and data-driven optimization techniques. Deep reinforcement learning (DRL) and Monte-Carlo tree search form the core of our operational optimization, which is supported by data-driven optimization for offline planning and by machine learning techniques for predicting demand, maintenance requirements, and traffic conditions. A key aspect of this research area is the development of techniques to preserve privacy across multimodal datasets, while also providing sufficient information for analysis and scheduling. The outcome of the project will be a deployment-ready software system that can be used to design and operate a micro-transit service effectively. Collaborators Include - Nashville Wego, Chattanooga Area Regional Transportation Authority and a multi-university team that includes Penn State, Cornell, niversity of Tennessee and Chattanooga and University of Washington.

Robust Online Decision Procedures for Societal Scale CPS

This career grant studies novel methods for designing sequential, non-myopic, online decision procedures for societal-scale cyber-physical systems such as public transit, emergency response systems, and power grid, forming the critical infrastructure of our communities. Online Optimization of these systems entails taking actions that consider the tightly integrated spatial, temporal, and human dimensions while accounting for uncertainty caused due to changes in the system and the environment. For example, emergency response management systems (ERM) operators must optimally dispatch ambulances and help trucks to respond to incidents while accounting for traffic pattern changes and road closures. Similarly, public transportation agencies operating electric vehicles must manage and schedule the vehicles considering the expected travel demand while deciding on charging schedules considering the overall grid load. The project’s proposed approach focuses on designing a modular and reusable online decision-making pipeline that combines the advantages of online planning methods, such as Monte-Carlo Tree Search, with offline policy learning methods, such as reinforcement learning, promising to provide faster convergence and robustness to changes in the environment. The research activities of the proposed project are complemented by educational activities focusing on designing cloud-based teaching environments that can help students and operators with prerequisite domain and statistical knowledge to design, manage, and experiment with decision procedures.

Optimizing Vehicle Charging and Discharging for Reducing Building Grid Dependency.

Our team is working with Nissan on designing online decision policies to optimize the charging and discharging rates of electric vehicles connected to buildings with the goal of reducing the overall price of electricity purchased from the main power grid. The system once complete will respond to dynamic changes in utility pricing signals and will ensure that all vehicles that are part of the deployment experiments will meet their requirements of expected charge level needed for completing their trips.

Mechanisms for Outsourcing Computation to Edge Resources

Developing an edge cloud is one of the big concerns for cyber-physical systems because latency to the cloud is a big issue. Under DOE and ARPA-E funding we have been developing a middleware that can support edge cloud called RIAPS. It is built upon a lot of our prior work in the area of componentized software frameworks for real-time systems and provides solutions for security, fault isolation, fault recovery, device abstractions, time synchronization and correct-by construction design. Recently, we have been extending this work to create a computational outsourcing market called MODICUM for edge systems. It is a decentralized system for outsourcing computation. MODICUM deters participants from misbehaving, which is a key problem in decentralized systems, by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODICUM minimizes computational overhead since it does not require global trust in mediation results.

Generative Anomaly Detection and Prognostics

Anomaly detection, prognostication and automated mitigation are very critical for data center management. Most of these approaches can be divided into two categories - model-based and data-driven. While model-based techniques rely on physics guided models that can explain and predict the expected progression of parameters such as temperature and voltage in electronics, the data-driven approach is suitable for complex scenarios where a suitable physics based model is unavailable. The data-driven approaches can be further divided into supervised and unsupervised methods. While supervised methods rely on prior labels, unsupervised methods are more suited for cases where the prior failure data might be unavailable or where the failure trends change over time. This is precisely the problem that exists in modern data-centers where the network devices and models change over time and the prior labels are absent. The precise problem therefore is to develop unique anomaly scoring functions that can identify whether the various components in a complex device are failing or not. Note that unlike the prior art it is crucial to not only identify anomalies, but identify the sub-components at the silicon level which are failing in the devices.

Microgrid Control-Coordination Co-Design (MicroC3)

Microgrids (MG) deliver highly resilient power supply to local loads in the event of a power outage, while improving distribution system reliability by reducing the load on the system under stress conditions. Today’s microgrids are typically one-off configurations that are rarely optimized for the specific microgrid architecture, equipment, physical or economic environment, and they are often proprietary, closed systems, leading to vendor-lock-in, and brittle, unmodifiable implementations. The goal of this project is to develop and demonstrate a Microgrid Control/Coordination Co-design (MicroC3) toolsuite that systematically designs an optimized microgrid, given a set of design objectives and performance constraints. The MicroC3 toolsuite will consist of two design-time tools - co-design optimization and validation; and a run-time tool. The co-design optimization tool identifies a selection of low-cost, right-sized equipment (i.e. plant) and optimized local and system-level controls, to meet design constraints and objectives. The validation tool verifies the design tool outputs in high-fidelity simulations and generates the implementation, including code and configurations for control, communications, and coordination. The run time tool is a library of microgrid control algorithms running on ARPA-E funded open-source platform that is automatically customized by the design and validation tools and deployed on low cost but robust and cyber-secure hardware devices. The impact of the project is (1) a framework for identifying lowest-cost, non-trivial MG design variations that deliver predictable and superior performance; and (2) drastically simplified deployment of microgrids at significantly lower cost through automated implementation of validated control and communication architecture into the operational environment.Investigators Prof. Gabor Karsai (Co-PI), Prof. Abhishek Dubey (Co-PI) and Prof. Srdjan Lukic (PI).

AI-Engine for Adaptive Sensor Fusion For Traffic Monitoring Systems

This project will enable a new suite of learning-based AI-engine that can be integrated and deployed as an edge computing solution to interface with both integrated and non-intrusive sensors (cameras and lidars) deployed in a region. The AI engine will dynamically calibrate the sensor fusion and incident detection algorithms to provide a continuous stream of traffic analytics - vehicle classification, vehicle count, vehicle density estimation, early incident detection, and incident clearance times. Overall, the goals of this proposed project are three-fold. First, we want to develop a technique that can provide sensor fusion at scale and identify outliers. Second, we want to show that the system can dynamically adapt and reconfigure to changing situations across space and time on the highways (information about potential sensitiveness of traffic operations in the regions can be derived through prior work done by this team on incident predictive CRASH analytics). Third, we want to design a low-cost hardware design that can be used to deploy these sensor algorithms at scale through the region and can even integrate the upcoming low-cost lidars and low-resolution cameras within a box that integrates single computing boards, power supplies, weather-proof enclosures and graphical processing units.

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).

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. 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.

DataScience and STEM

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. 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.

Past 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. We are designing 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.


The CHARIOT (Cyber-pHysical Application aRchItecture with Objective-based reconfiguraTion) project, aims to address the challenges stemming from the need to resolve various challenges within extensible CPS found in smart Cities. CHARIOT is an application architecture that enables design, analysis, deployment, and maintenance of extensible CPS by using a novel design-time modeling tool and run-time computation infrastructure. In addition to physical properties, timing properties and resource requirements, CHARIOT also considers heterogeneity and resilience of these systems. The CHARIOT design environment follows a modular objective decomposition approach for developing and managing the system. Each objective is mapped to one or more data workflows implemented by different software components. This function to component association enables us to assess the impact of individual failures on the system objectives. The runtime architecture of CHARIOT provides a universal cyber-physical component model that allows distributed CPS applications to be constructed using software components and hardware devices without being tied down to any specific platform or middleware. It extends the principles of health management, software fault tolerance and goal based design.

Energy Efficiency of Transit Operations

We are developing models to analyze and optimize the cost of transit operations by focusing on the energy impact of the vehicles. 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 datasets allow us to train accurate data-driven predictors using deep neural networks, for energy consumption given various routes and schedules. Having these predictors combined with traffic congestion information obtained from external sources will enable the agencies to identify and mitigate energy efficiency bottlenecks within each specific mode of operation such as electric bus and electric car. To make this possible, the project also developed new distributed computing and machine learning algorithms that can handle data at such a rate and scale.


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.

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.

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.

Statistical Optimization and Analytics for Community Emergency Management

The goal of this project is to improve emergency response systems using proactive resource management that minimizes time and maximizes the effectiveness of the response. With road accidents accounting for 1.25 million deaths globally and 240 million emergency medical services (EMS) calls in the U.S. each year, there is a critical need for a proactive and effective response to these emergencies. Furthermore, a timely response to these incidents is crucial and life-saving for severe incidents. The process of managing emergencies requires full integration of planning and response data and models and their implementation in a dynamic and uncertain environment to support real-time decisions of dispatching emergency response resources. However, the current state-of-the-art research has mainly focused on advances that target individual aspects of emergency response (e.g., prediction, optimization) when different components of an Emergency Response Management (ERM) system are highly interconnected. Additionally, the current practice of ERM workflow in the U.S. is reactive, resulting in a large variance in response times.

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. 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.

Selected Videos, Talks and Posters

Fair Design of Public Transportation Lines

Predicting Public Transportation Load

Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests

The booking problem

Integrated Fixed Line and On Demand Problem

Solvers behind our Microtransit Algorithms

A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling

Recent paper on the impact of COVID-19 on transit

CHARIOT Demonstration

Project presentation given at the DoE Annual Merit Review

Incident Analytics and Response Management Dashboard

Deep NN Car with SafetyManager

Providing Information to Commuters

Data-driven Route Level Energy Prediction for Mixed Fleet

Energy Consumption Dashboard

Modular Mobility Application

Transax-Blockchain and Transactive Energy

Multi-Modal Mobility Workshops organized in 2018

Occupancy Analysis Dashboard for CARTA

Resilient Information Architecture Platform Demonstration

Poster at TDEC workshop in Knoxville in 2019

Mobility Application Built by Students in the Smart Cities Class

Modular Computing Platform for Public Transit

Occupancy Analysis Dashboard for WeGo Nashville

The Team

Dr. Abhishek Dubey


Dr. Abhishek Dubey is an Assistant Professor of Electrical Engineering and Computer Science at Vanderbilt University and a Senior Research Scientist at the Institute for Software-Integrated Systems. Abhishek directs the SCOPE lab (Smart and resilient COmputing for Physical Environment) 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 lies in the resilient system design of cyber physical systems. He is specially interested in performance management, online failure detection, isolation and recovery in smart and connected cyber-physical systems, with a focus on transportation networks and smart grid. His key contributions include the development and deployment of resilience 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. His 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. Recently, this work has been adapted for fault detection and isolation in breaker assemblies in power transmission lines. His work has been funded by the National Science Foundation, NASA, DOE, ARPA-E. AFRL, DARPA, Siemens, Cisco and IBM. Abhishek completed his PhD in Electrical Engineering from Vanderbilt University in 2009 in the area of fault detection and isolation for large computing clusters. 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.

Ammar Zulqarnain

Graduate Research Assistant

Ammar Bin Zulqarnain is a graduate student in the Department of Computer Science at Vanderbilt University and works as a research assistant at the Institute for Software Integrated Systems. He received his Bachelor’s degree in Engineering Science and Economics from Vanderbilt University in May 2022.

Dr. Ava Pettet

Visiting Scholar

Ava Pettet is currently a visiting scholar at the Institute for Software Integrated Systems. She received her Ph.D. in computer science at Vanderbilt University in 2022. She completed her undergraduate studies in computer science at Vanderbilt University in May 2016.

Dr. Ayan Mukhopadhyay

Research Scientist

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.

Baiting Luo

Graduate Research Assistant

Baiting Luo 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 Computer Engineering from Northwestern University in June 2021 and his Bachelor’s degree in Computer Engineering and Computer Science from Rensselaer Polytechnic Institute in 2019.

Jacob Buckelew

Graduate Research Assistant

Jacob Buckelew 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 Bachelor’s degree in Computer Science and Mathematics from Rollins College in May 2022.

Dr. Jose Paolo Talusan


Dr. Jose Paolo Talusan is a Post Doctoral Researcher in the Department of Computer Science and Computer Engineering at Vanderbilt University. He earned his PhD from the Nara Institute of Science and Technology, Japan in 2020. His research interests include middleware and distributed computing systems, with a focus on smart transportation networks.

Maxime Coursey

Research Software Engineer

Max Coursey works as a Research Software Engineer. He has Master’s of computer science from Vanderbilt University and Bachelor’s from the Georgia Institute of Technology. Prior to joining Vanderbilt, Max worked as a technical consultant for large enterprise clients throughout the United States. He currently works on designing/deploying end-to-end solutions for machine learning applications - including data pipelines and engineering, data analytics, as well as overall systems design and web application development.

Michael Wilbur

Graduate Research Assistant

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.

Rishav Sen

Graduate Research Assistant

Rishav Sen is a graduate student and a Russel G. Hamilton Scholar in the Department of Electrical Engineering and Computer Science at Vanderbilt University. He completed his Undergraduate in Electronics and Communication from the Heritage Institute of Technology in August, 2020 and had been working as a Software Engineer before starting his Graduate studies.

Dr. Sayyed Mohsen Vazirizade


Dr. 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.

Dr. Scott Eisele

Senior Engineer

Dr. Scott Eisele is a senior Engineer at 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 and received PhD in Electrical Engineering in 2020.

Dr. Shreyas Ramakrishna

Graduate Research Assistant

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.

Sophie Pavia

Graduate Research Assistant

Sophie Pavia 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. She completed her undergraduate studies in computer science at Florida State University in April 2022.

Yunuo Zhang

Graduate Research Assistant

Yunuo Zhang 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 Bachelor’s degree in Computer Science and Applied Mathematics from Vanderbilt University in December 2021.

Selected Publications

  1. S. Eisele, T. Eghtesad, K. Campanelli, P. Agrawal, A. Laszka, and A. Dubey, Safe and Private Forward-Trading Platform for Transactive Microgrids, ACM Trans. Cyber-Phys. Syst., vol. 5, no. 1, Jan. 2021.
  2. C. Hartsell, S. Ramakrishna, A. Dubey, D. Stojcsics, N. Mahadevan, and G. Karsai, ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems, in 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021, 2021.
  3. 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.
  4. 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.
  5. 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.
  6. S. Ramakrishna, C. Harstell, M. P. Burruss, G. Karsai, and A. Dubey, Dynamic-weighted simplex strategy for learning enabled cyber physical systems, Journal of Systems Architecture, vol. 111, p. 101760, 2020.
  7. 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.
  8. C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, 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.
  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, A. Dubey, S. Khare, S. Nannapaneni, A. S. Gokhale, S. Mahadevan, D. C. Schmidt, and M. Lehofer, CHARIOT: Goal-Driven Orchestration Middleware for Resilient IoT Systems, TCPS, vol. 2, no. 3, pp. 16:1–16:37, 2018.
  11. D. Balasubramanian, A. Dubey, W. Otte, T. Levendovszky, A. S. Gokhale, P. S. Kumar, W. Emfinger, and G. Karsai, DREMS ML: A wide spectrum architecture design language for distributed computing platforms, Sci. Comput. Program., vol. 106, pp. 3–29, 2015.
  12. T. Levendovszky, A. Dubey, W. Otte, D. Balasubramanian, A. Coglio, S. Nyako, W. Emfinger, P. S. Kumar, A. S. Gokhale, and G. Karsai, 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.
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