I am currently looking for students with interests in the following projects. All of my projects involve aspects of Computational Intelligence (Neural Networks, Evolutionary Computation, and Swarm Computation), High Performance/Parallel Computing, and Data Science. In addition, I am interested in working with multiple datasets including the Forest Fire Dataset, Scientific Registry of Transplant Recipients (SRTR), ADFA-LD and IDS, and BackBlaze.
- Optimal Exchange and Prediction for Kidney Exchanges. Every year, thousands of individuals receive kidney transplants that are life saving. Yet, the underlying algorithms for matching chains of donors and predicting various outcomes of kidney transplants are two wide open research fields. This project is focused analyzing real world data to predict various patient outcomes while optimizing kidney transplants. Work will include the use data science techniques, machine learning, and high performance computing.
- Cloud/Edge Computing System Modeling & Reliability Evaluation. When the cloud goes down (think of failures in AWS, Azure, Facebook, Twitter, Gitlab, Github, etc.), tremendous amounts of money are lost. This project is focused on 1) Developing system models for cloud computing systems, 2) Applying data-driven algorithms for evaluating the reliability and availability of these systems under various scenarios and loads, 3) Evaluating failure data using computationally intelligent techniques (swarm algorithms, neural networks, support vector machines), 4) Investigating economic and performance costs of various models of oversubscription and offloading, and 5) Performing data analysis on related datasets (like BackBlaze data).
- Computational Aspects of (Smart) Power System Reliability Evaluation. If the power goes off, everyone notices, thousands of dollars are lost, and people die. The goal of this system is improving the computational aspects of power system reliability evaluation through the investigation and application of various algorithms, with a particular focus on swarm algorithms and high performance and parallel computing. This relates to my previous work in Intelligent State Space Pruning.