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Christine Y

- Research Program Mentor

MD candidate at Albert Einstein College of Medicine


Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism


Hi, my name is Christine and I'm so grateful for the opportunity to mentor students and give back to the community. I am a 2021 graduate of Duke University and M3 at Albert Einstein College of Medicine. My main academic interest is in the intersection of medicine and computer science, and some of the ways I've explored this is through my web development work with the health tech startup Cydoc, research in developing a neural network to predict COVID-19 in patients using gene expression, and coursework in computational protein design. In the future, I hope to pursue a surgical subspecialty or interventional radiology due to my passion for anatomy, dissections and hands-on work. In my free time, I enjoy playing sports (such as tennis, squash, volleyball) and running.

Project ideas

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

Binding site analysis of potential inhibitors of SARS-CoV-2 S2

Therapeutics are needed to control the spread of SARS-CoV-2 and the S2 region is an effective target due to its highly conserved nature. In this project, we will find examples of existing inhibitors targeting this region in similar viruses (i.e. SARS-CoV, MERS-CoV) and dock them to SARS-CoV-2 S2 to understand the binding site interactions. Then, we will re-design these inhibitors to best fit SARS-CoV-2 S2 and improve upon our binding interactions in the final project.

Application of cryoDRGN for cryo-EM image analysis

Cryo-EM is a revolutionary imaging technique to capture the molecule representation of proteins, though AI is required to denoise and reconstruct the 2D projections of these proteins. CryoDRGN is a recent paper demonstrating improved ability to reconstruct proteins through neural networks, so in this project we will validate the results of cryoDRGN by applying it to another dataset from the EMPIRE database as well as learn the details of neural networks and cryo-EM image analysis.

Gene network analysis in human disease

Gene networks are promising methods to predict connections between genes and human disease. In this project, we will use gene expression and biology pathways (from Enrichr) to develop a neural network to predict disease (i.e. COVID-19).

Literature review in medicine

The human body normally follows the same design, but occasionally we run into anatomical variations. For instance, having a short indicis propius is common in up to 10% of patients, which can lead to negative consequences if patients require tendon transfer for another issue, or need to have hand surgery. Other anatomic variants of consequence include those of the gallbladder ducts, which can be of consequence for gallbladder surgeries. These anatomic variants can make the difference between successful and failed surgeries, so it is important to review these variants and increase awareness. Thus, a possible project is having a literature review of observed variants and their possible surgical consequences. These are relatively simpler projects that are easily publishable.

Detecting trends in surgical and medical patients

Through chart review, we can collect data on patient outcomes based on their medical and surgical management and detect trends to improve the process of patient care. For example, can we use statistics and AI to determine the difference between stroke patient outcomes by hospital? These are highly publishable projects with promising outcomes.

Coding skills

Python, JavaScript, C, R

Languages I know


Teaching experience

I have been tutoring students since high school in various subjects, chemistry and math being those that I've had the most experience teaching. At Duke, I have tutored through the Peer Tutoring program, Organic Chemistry Learning Community and FEMMES+ volunteer program. Through Peer Tutoring, I've tutored students one-on-one in computer science (Python) and math (intro calculus and multivariable calculus) weekly for one semester each. Through the Organic Chemistry Learning Community, I've tutored a group of 15-20 students for 2 hours/week for a year, much of which was remote and over Zoom. This taught me how to manage large groups and still cater to students' needs in the tough subject. Finally, through FEMMES+ I've tutored computer programming (Python and JavaScript) to groups of middle school students in Durham, NC.


Work experience

Cydoc (2019 - Current)
Software Engineer
Duke University (2020 - Current)
Machine Learning research assistant
23andMe (2019 - 2019)
Software Engineering Intern
Icahn School of Medicine at Mount Sinai (2020 - 2020)
BD2K-LINCS DCIC Summer Fellow


Duke University
BS Bachelor of Science
Computer Science & Chemistry
Albert Einstein College of Medicine
MD Doctor of Medicine candidate

Completed Projects

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