NCSI: Real-Time Adaptive Control of Artificial Tissue Growth using Machine Learning

Status	Completed
Mentor Name	Roman Voronov
Mentor's XSEDE Affiliation	Campus Champion
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Real-Time Adaptive Control of Artificial Tissue Growth using Machine Learning
Summary	Current approaches to tissue engineering fail because there is no central nervous system to orchestrate cell behavior in artificial cultures. We have created a microfluidics device capable of cell and fluid manipulations within living tissues. This project will develop a Model Predictive Controller (MPC) that will use a Recurrent Neural Network (RNN) in order to learn from empirical data and modulate the cell action based on real-time non-destructive feedback from 3D Lattice Light Sheet Microscopy (a Nobel Laureate technology available in the PI's lab) and chemical assaying. The ultimate goal is to achieve the ability to culture reproducible tissue patterns, specificed by the user prior to the at the beginning of the experiment. This technology will benefit patients in need of life-saving organ and tissue transplants.
Job Description	- Develop custom Matlab codes that implement HDF5 in order to parallelize 3D rendering of microscopy images by integrating the code's output with Visit and Paraview packages using the XDMF standard. -Train a Recurrent Neural Network (RNN) algorithm on the microscopy images of cell behavior and on the chemical assaying data, for implementation in the Model Predictive Controller (MPC)
Computational Resources	The project will use Texas Advanced Computing Center resourses via a New Jersey Institute of Technology Campus Champion allocation as follows: -Long term tape storage for large data from Lattice Light Sheet Microscopy -Image processing of the 3D Lattice Light Sheet Microscopy data, and Visualization using Visit and Paraview packages -Training of the Recurrent Neural Network (RNN) on experimental microscopy and chemical assaying data for implementation in the Model Predictive Controller (MPC).
Contribution to Community
Position Type	Apprentice
Training Plan	The student has worked in the PI's lab as a research assistant for 2.5 years. Given an interdisciplinary background (Mechanical Engineering/Computer Science double major), the student has performed various electrical engineering and image processing programming tasks in the PI's lab. Consequently, the student is a co-author on the following preprint: https://doi.org/10.1101/688424, and is capable of spending the vast majority of their time doing tasked but not necessarily completely independent work (expected of an Apprentice). However, the student needs additional HPC training to carry out the tasks of the project. The training will be administered as follows: 1) The student will attend the NJIT High-Performance Numerical Computing Fall Workshop 2) The student will receive personal training from the PI in the areas of Image Processing, Machine Learning and Controls Theory 3) The student will further improve by interacting with an active research group of 2 PhDs and 3 MS students, all of whom are active in image processing and HPC. 4) The student will help the PI to develop computational educational materials as a part of the Campus Champion and Computer Aids for Chemical Engineering Education (CACHE) initiatives (the PI is an active member of the CACHE community, see http://cache50th.org/?page=youngfaculty )
Student Prerequisites/Conditions/Qualifications	The student must be an undergraduate at the NJIT campus, with experience in Matlab and Lattice Light Sheet Microscopy. Additionally, interest in the following is highly desired: - Image Processing - Machine learning - Controls Theory
Duration	Semester
Start Date	09/01/2019
End Date	12/01/2019