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sYNTHETIC neUROBIOLOGY gROUp + neUROSCIENCE statistics Research Lab (jANUARY 2019 - Present)

I work under Dr. Ed Boyden (MIT Media Lab, Brain and Cognitive Sciences) and Dr. Emery Brown MD, PhD (MIT Brain and Cognitive Sciences, Harvard Medical School, Massachusetts General Hospital) on using deep learning, statistical methods, and non-invasive brain stimulation to better understand anesthesia. 

Supported by the NSF Graduate Research Fellowship (2019-Present), GEM Fellowship (2018-19), Medical Engineering and Medical Physics Fellowship (2018-19)

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I worked under Dr. Ramesh Raskar in the MIT Media Lab to leverage encryption for neural networks for secure analysis of HIPAA-protected patient data. 

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Cornell University - Bonassar and Estroff Groups (2015-2018)

Over three years, I developed a process to induce mineral gradients in bone to improve the mechanical properties of our tissue-engineered meniscus constructs. Much of this work involved image segmentation analysis on microCT images of the induced gradients to develop a model for mineral gradient profile. My results showed that the procedure created consistently reproducible gradients, and that those gradients improved collagen infiltration into the bone, resulting in improved tensile properties of the overall meniscus construct.

Alexander J. Boys, Hao Zhou, Jordan B. Harrod, Mary Clare McCorry, Lara Estroff, Lawrence Bonassar. Top-Down Fabrication of Spatially Controlled Mineral-Gradient Scaffolds for Interfacial Tissue Engineering. (2019) ACS Biomaterials Science and Engineering. DOI: 10.1021/acsbiomaterials.9b00176

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Stanford University - Xing Lab (2017)

Through this project, I discovered which aspects of a magnetic resonance image contribute to artifact formation, and successfully developed a network that could identify changes in the raw data that might form an artifact during reconstruction. I continued to work on this project remotely throughout the fall semester of my senior year, developing an algorithm that can identify intra-scan artifacts in real-time MRI, which was published at Neural Information Processing Systems 2017. 

Jordan B. Harrod, Morteza Mardani, John Pauly, Shreyas Vasanawala, Lei Xing. Deep Predictive Coding for Super Time-Resolved MR Imaging. Neural Information Processing Systems 2017 - Medical Imaging Workshop.



In this role, I learned to develop electrospun polymer nanofiber scaffolds for periodontal ligament regeneration and drug delivery, and used tensile testing to compare the strength of the scaffold to that of native tissue. My tensile testing results showed that the usage of varied ratios of polymeric acids substantially affected the Young’s modulus and ultimate tensile strength of the scaffolds, which did not yet compare to the strength of the periodontal ligament.