Evan Farach, originally from Conroe, Texas, is a fourth-year undergraduate student at Baylor University. He is pursuing a major in Neuroscience with a pre-medical focus, complemented by a minor in Business Administration. 

At Baylor, Evan is affiliated with the Neuroscience of Addiction Lab, a new research lab led by Dr. Jacques Nguyen. In this lab, Evan investigates the effects of commonly abused opioids, such as oxycodone and fentanyl, on the expression of perineuronal nets in key brain structures of rodent models. Recently, he has collaborated with graduate students to develop the lab’s intravenous drug self-administration protocol. Outside of the lab, Evan works for Baylor’s Department of Psychology and Neuroscience as one of six chosen student ambassadors. Additionally, he holds a position as a nocturnal medical transcriptionist in the emergency department at Baylor Scott & White – Hillcrest. On campus, Evan dedicates his time to mentoring fellow students as a Prehealth Mentor and serves as the President of Alpha Epsilon Delta, Baylor’s only nationally recognized prehealth honor society. In his free time, Evan enjoys photography, working out, and working on his car.

I’m Hanliang Xu, a sophomore majoring in Computer Science and Math. A fun fact about me is that since I came to the US two years ago, I’ve traveled to 12 states!

I became interested in applying my quantitative skills for medical imaging since my summer research project with Dr. Bennett Landman. Last summer, I attempted to harmonize connectivity matrices of diffusion MRI, removing site differences among datasets caused by variations in scanning protocol or scanner build. I lost track of time running experiments to explore whether and why our current statistical or deep learning methods are applicable to the harmonization task I tried to address.

Currently examining mummy data under the guidance of Dr. Katherine Van Schaik, I’m intrigued by the health and anthropology insights sealed in the mummy dataset which hasn’t been studied systematically since 2013. Together with other two undergraduate research assistants, I have written code to convert the DICOM series to NIFTI formats and extract header information on a large scale. I’m working on segmentation of the regions of interest for subsequent analysis.

In my spare time, you can find me fencing with friends in the Vanderbilt Fencing Club, watching classic movies at the Belcourt Theatre, and catching up with new deep learning papers on arXiv at the Stevenson Library.

Eddie Shangguan 25' is a Computer Science and Mathematics double major from Beijing, China & Vancouver, Canada. He is interested in how archaeology, radiology, and computational science intersect by studying ancient populations. He aims to use image processing and AI techniques to assess diseases in mummies, thereby enhancing the understanding of the disease's progression over time and identifying patterns and trends that provide context for today's health issues. Eddie is concurrently involved in projects that deal with brain MRI images and tissue segmentation, leveraging deep learning algorithms to train models to recognize and classify tissue types with high accuracy, which could ultimately lead to improved diagnostic and treatment planning for patients with AGS and other neurological conditions. In his spare time, Eddie enjoys traveling, reading, hiking, and hanging out with friends. He is an active member of Vanderbilt Alternative Spring Break (ASB), VandyHacks, and Vanderbilt Data Science Club on campus.

I am a PhD student in Electrical and Computer Engineering at Vanderbilt University, affiliated with the Medical-image Analysis and Statistical Interpretation (MASI) Lab and the Vanderbilt Institute for Surgery and Engineering (VISE).

I earned my BA in Computer Science and Art History with Honors and a minor in Medieval and Renaissance Studies from Washington and Lee University in 2022. My senior thesis, titled Florence + The Machine: A Computational Approach to Florentine Liturgical Manuscript Illuminations from the Late Trecento, applied deep learning to analyze a dataset of religious images produced in the same area and period. This research evaluated the neural network's performance on a small dataset and explored methodological and ethical considerations at a pivotal moment in the convergence of technology and the humanities. I continued my work on this project in Florence through the Fulbright U.S. Student Program from 2022 to 2023.

At Vanderbilt, I engage in research on brain connectivity while also contributing to the analysis of images of mummified remains.

Dr. Shunxing Bao is a Research Assistant Professor of Electrical and Computer Engineering at Vanderbilt University, where he completed both his master's and Ph.D. in Computer Science. He received his bachelor's degree in Software Engineering from Huazhong University of Science and Technology in China. Dr. Bao's research deftly bridges big data informatics with medical image analysis, employing distributed computing to enhance the handling and processing of medical imagery. At the helm of developing an integrative cross-disciplinary platform, he is merging diverse medical data, from clinical imaging to genomic analytics. His work is instrumental in driving forward diagnostic methods and improving consistency in clinical data management. His use of machine learning is reshaping computational approaches within digital pathology, focusing on image synthesis, segmentation, extraction of features, classification, and pattern recognition. His efforts are not only technically innovative but also target practical applications, significantly impacting the evolution of medical informatics.