NCSI: Large Scale Genetic Programming for Scientific Image Understanding (Learner Position)

Status	Completed
Mentor Name	Dirk Colbry
Mentor's XSEDE Affiliation	Campus Campion
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Large Scale Genetic Programming for Scientific Image Understanding (Learner Position)
Summary	We are looking for students interested joining our research team to help develop software to scale a prototype Simple Evolutionary Exploration (SEE) library to utilize large scale computing systems. The search space involved in this research is extremely large and requires massive computing resources. This research will look into leveraging High Performance Computing Resources (XSEDE), HTCondor and Cloud Resources. The long term goal of the project is to build image annotation system that works in "real time" with the researchers to explore the algorithm space for solutions to scientific image understanding problems.
Job Description	Students will work with a research team continue to build and scale a prototype software library to run on very large systems. The current prototypes are written in Python and searches the "algorithm space" of image segmentation algorithms using single sample learning. The nature of the search space is very large and highly non-differentiable so standard optimization techniques do not apply. However, this software utilizes Genetic Programming which is pleasantly parallel and therefore this algorithm can easily leverage large scale systems.
Computational Resources	The current prototype has been run on a local Tear-III XSEDE resource and multiple cloud resources (Azure, Google Cloud and AWS). We hope to further test the system on these resources and specifically build an interface that works with HTCondor and maybe XSEDE cloud resources such as Jetstream.
Contribution to Community	Community The tools we are developing should have a broad impact for scientific image understanding. our hope is to build web-based gateway which will allow researchers to annotate their images on the front end while the tool searchers for an automated solution on the back end.
Position Type	Learner
Training Plan	Although no prior work experience is required, some knowledge of computer programming (primarily Python) or scientific computing is expected. Ideally, applicants will also have experience in one or more of the following: scientific image understanding, using HPC systems, hacking or tinkering. The research is gaining a lot of momentum and there are lots of opportunities.
Student Prerequisites/Conditions/Qualifications	Students with a background in Python, Genetic Algorithms or Scientific Image Understanding is preferred but not required.
Duration	Semester
Start Date	01/10/2022
End Date	05/06/2022