Department of Software Engineering
Rochester Institute of Technology
I am an Associate Professor specializing in Data Science, housed in the Department of Software Engineering in the B. Thomas Golisano College of Computing and Information Sciences (GCCIS). My research focuses on the application of machine learning to large-scale, real world data sets using high performance and distributed computing, with an emphasis on developing systems for practical scientific use. I'm interested in the intersection of evolutionary algorithms and neural networks, or "neuro-evolution", where evolutionary algorithms are used to automate and optimize the design of neural network architectures. I am actively developing the Evolutionary eXploration of Augmenting Convolutional Toplogies (EXACT) and Evolutionary eXploration of Augmenting Memory Models (EXAMM, formerly known as EXALT) algorithms, which are hosted on GitHub.
I am also active in the area of volunteer computing and citizen science, where I did the initial development of MilkyWay@Home, and more recently the Citizen Science Grid and NSF funded Wildlife@Home which has volunteer citizen scientists annotate hundreds of thousands of hours of video and millions of images to help in the development of computer vision algorithms. Recent work on Wildlife@Home has focused on the development of convolutional neural networks to detect various wildlife species in imagery collected from unmanned aerial systems.
My currently funded research projects include the National General Aviation Flight Information Database (NGAFID), used by general aviation institutions across the country to monitor and predict potential flight safety issues. We are actively developing an interface and methods to detect potential flight issues, trends and mine this massive database of over 800,000 hours of flight data. I am also working on Department of Energy Award #FE0031547, Improving Coal Fired Plant Performance through Integrated Predictive and Condition-Based Monitoring Tools, where we are developing neuro-evolution algorithms to evolve recurrent neural networks to predict coal fired power plant data.
I have also been a main contributor in the development of both the compiler and runtime of SALSA and SALSA Lite, a programming language based on the actor model of computation. SALSA enables easy development of concurrent and transparently distributed applications by following actor semantics.