Research

Current Research:

Secure and Dynamic Service Provisioning for IoT Applications.

Problem: In different IoT based applications like remote healthcare (doctors or nurses remotely monitoring patients) or disaster management (think the forest fires in California or Australia and fire firefighters wanting to remotely monitor the situation and allocate emergency resources) different users may have different service requirements. These requirements can be along the lines of performing a tradeoff between the network's Quality of Service parameters like latency or bandwidth or network security parameters like the strength of encryption standards or authenticating data packets.

Goal: The objective is then to design a system that can perform these tradeoff efficiently addressing the needs of different users, designing network security as a dynamic parameter rather than how it is traditionally designed in a universally static way, and then use machine learning to predict the requests of future users and recommend them to service providers such the energy-constrained IoT devices can be more efficiently managed.

Key-Ideas/Research Domains: Cybersecurity, Machine Learning, service provisioning and recommendations, optimization algorithms, Internet of Things.


Artificial Intelligence (AI) systems to address mental well-being.

Problem: Mental Disorders like Depression is a major issue in the youth of the USA, and also one of the leading causes of suicides. The current situation of the global pandemic has only aggravated this situation. Many individuals are unable to avail proper care due to a variety of reasons some attributed to the inability to monitor or diagnose the disorder at early stages, therapy and care not being easily accessible and/or affordable, and/or the disorder being treated as a taboo/stigma in their social circles.

Goal: Design and Develop an Artificial Intelligence system that will aid in the monitoring and therapy of major depressive disorder with the goal to make the treatment more accessible and acceptable by individuals.

Key-Ideas/Research Domains: Artificial Intelligence/Machine Learning, psychology, data science (understanding patterns of depressive behavior), delivery of an AI system that will be trusted and accepted by humans (should you develop a chatbot? Should you integrate it with devices like Amazon Alexa, should you develop an AR/VR technology that is more immersive, etc. what works more effectively?)


Risk Assessment Frameworks Using Quantum Probabilities.

Problem: Connected and Autonomous vehicles are the future of automotive transportation. However, with the increase in interconnectivity between autonomous vehicles and vehicles with the internet, the potential for cybersecurity attacks also increases. As such, an effective tool to evaluate the likelihood and impact of cyber-attacks on autonomous vehicles is a pre-requisite to their design and deployment.

Goal: Design and develop a cybersecurity risk assessment framework to evaluate the likelihood and impact of cybersecurity attacks on connected and autonomous vehicles. In doing so, the goal will be to utilize the concepts of Quantum Probabilities (probabilities of complex numbers), which will address some of the traditional limitations of current cybersecurity risk assessment frameworks like Attack Graphs.

Key-Ideas/Research Domains: Cybersecurity, risk modeling, probabilities, graph theory, quantum computing applications.

Current Students

Ph.D. Students:

  • Damilola Alao

  • Oluwafeyisayo Oyeniyi

  • Meena Nagabhushana

  • Victorine Clotilde Wakam Younang

MS Students:

  • -

Current and Past Undergraduate Students:

  • Alec Breslow (P)

  • Paul Albrecht (P)

  • Giuseppe DiMaio (P)

  • Rio Capollari (P)