Milad Ghiasi Rad, Jacob Kimball, and Mohammad S.E. Sendi have received the Blended Online Learning Design (BOLD) Graduate Fellowship. They are all Ph.D. students in the Georgia Tech School of Electrical and Computer Engineering (ECE). 

The Blended and Online Learning Design (BOLD) Graduate Fellowship Program is an open education initiative supported and funded by the Provost Funds for Excellence in Graduate Studies. The program aims at enabling and empowering graduate students to become knowledge producers through designing, developing, and contributing open educational resources (OER) for blended and online learning.

Ghiasi Rad’s proposed BOLD project is to develop an interactive web application to help students understand the basics of data science and machine learning. He is doing his Ph.D. research in “Machine Learning Applications in Bioinformatics and Sepsis Detection.” 

Ghiasi Rad’s passion in data analytics and machine learning has led him to direct his studies towards data science. His research is focused on bringing new intelligent solutions to medical data analytics by implementing new computational models that can perform high accuracy infection prediction using genomics and demographic information. His advisors are Rishikesan Kamaleswaran, an assistant professor in the Department of Biomedical Informatics at Emory University, and Georgia Tech ECE Associate Professor Omer T. Inan.

Kimball’s proposed BOLD project is to develop a series of online programming environments to train users how to analyze biomedical signals and properly apply machine learning techniques to make predictions. He is doing his Ph.D. research in “Estimation of Blood Volume Status with a Noninvasive Wearable Multi-modal Sensing System.” 

Kimball’s research combines wearable cardiovascular monitoring with machine learning to estimate a person’s blood volume status in a noninvasive way. Blood volume changes can occur in many situations, such as hemorrhage from traumatic injuries, dehydration, or heat stress. His goal is to someday have a commercial system that can be used by the military, EMS, and others to minimize preventable injuries due to changes in blood volume and maximize recovery when unavoidable situations do occur. Inan also serves as Kimball’s advisor in the Georgia Tech School of ECE.

Sendi’s proposed BOLD project aims to design a set of simulation and experimental procedures that help undergraduate students learn how analog electronics circuits work step-by-step. In his Ph.D. research, he developed an interpretable machine learning approach to elucidate the underlying mechanism of deep brain stimulation (DBS) therapy in different regions of the brain. 

Sendi is also developing an active learning-based framework for the optimal design of closed-loop DBS control systems. He has already made significant contributions to uncovering mesoscale dynamic correlations of serious mental illnesses such as schizophrenia, Alzheimer's disease, and major depressive disorder from fMRI data. Sendi is advised by Vince Calhoun, of the Center for Translational Research in Neuroimaging and Data Science, a joint effort among Georgia State University, Emory University, and Georgia Tech (where Calhoun is an adjunct professor in the School of ECE); Babak Mahmoudi, of the Neuroinformatics and Intelligent Systems Lab at Emory University; and Robert E. Gross, of the Translational Neuroengineering Lab at Emory University.