Bells Recognized as Regeneron STS Scholars

Posted: January 16, 2017

Seniors Kevin Chang, Pravin Ravishanker and Arvind Sridhar are among 300 students nationwide to be named Regeneron Science Talent Search (STS) 2017 Scholars. The competition, a program of Society for Science & the Public, is considered the nation’s most prestigious pre-college science competition.

Faculty member Dr. Debjani Roy, who has taught each of the boys and moderated their club activities, knows them well. "I am so proud of Arvind, Kevin and Pravin for this tremendous achievement. They are fantastic young scientists in the making. Their projects are particularly special because they are contributing to the next generation research frontiers – advancing computer science and biomedical science.” she said. "Kevin is a mature and extremely talented student. There are numerous interesting applications of this project, which is related to Ramsey theory, to various fields of mathematics and theoretical computer science. His project highlights his exceptionally strong mathematical background and passion for complex problem solving. Pravin is a talented, creative and devoted student with an inclination for critical analysis. His computational neuroscience research project on predicting the onset of Alzheimer’s disease is very elegant and highlights his statistical analysis skill. And Arvin's project in the field of cardiac tissue engineering to design hydrogels highlights his in-depth knowledge in biomedical science. His intellectual curiosity and creativity impresses me."

I asked each of the students to describe their projects, in layman's terms, and give me a little background into how long they have been working on their projects and/or why the topic interests them. Following are their responses.

Kevin Chang

Project title: Upper Bounds for Ordered Ramsey Numbers of Small 1-Orderings

Kevin's synopsis: In math, there is a subfield of combinatorics called Ramsey theory, that studies the conditions under which a structure must satisfy some property. My project studies what are known as Ramsey numbers, which specify how large a graph has to be such that it must always satisfy a certain property.

Background: I've been working on this project since January 2016. I became interested in Ramsey theory because I thought that finding unavoidable order out of chaos, which is basically what Ramsey theory is about, was a really cool idea.

Pravin Ravishanker

Project title: ALZCan: Predicting Future Onset of Alzheimer’s Disease Using Polygenic Risk Scores, Cognitive Tests, Cerebrospinal Fluid Biomarkers, and Multiple Neuroimaging Modalities (Resting-State fMRI, FDG-PET, Florbetapir PET)

Pravin's synopsis: Alzheimer’s disease is an irreversible neurodegenerative disorder that affects 44 million people worldwide but currently has no preventive cures or extremely accurate diagnostic tests. Predicting the future onset of Alzheimer’s several years beforehand is the first essential step in order to slow, stop and prevent the disease from causing further irreversible brain damage and cognitive decline. In this computational neuroscience research, I developed an accurate statistical methodology for Alzheimer’s and Mild Cognitive Impairment’s early detection using patient demographic information, standard cognitive test scores, 600,000+ genetic variations, protein levels in the brain’s surrounding fluid, and 3 different types of brain scans measuring brain functional activity, neuronal metabolism, and plaque buildup. With the predictive power of polygenic risk scores and machine learning to discover patterns amongst huge genomic and neuroimaging datasets, we can pinpoint the future occurrence of Alzheimer’s and prevent irreversible brain damage.

Background: This 4-year computational neuroscience research on Alzheimer’s was conducted from home using clinical, genomic and neuroimaging data from the Alzheimer’s Disease (AD) Neuroimaging Initiative, a global collaborative effort tracking AD biomarkers. I reached out to statisticians, neuroscientists and geneticists to seek help on challenging technical topics via email and through several local and international science conferences.

I’ve always been interested in neuroscience from a very early age. My 9th grade project was about statistically comparing the relative importance of gender and genetics risk factors for Alzheimer’s. My 10th grade project kickstarted my interest in Alzheimer’s early detection using machine learning, graph theory and signal processing for analyzing neuroimaging data. My 11th grade project, a massively computational genome-wide association study, made me interested in the millions of genetic variations that somehow together contribute to Alzheimer’s disease. I combined my 10th and 11th grade projects to build a more accurate system for Alzheimer’s early detection. This 12th grade project was submitted to the Regeneron Science Talent Search.

Arvind Sridhar

Project title: Engineering Injectable and Conductive Hydrogels Doped with Graphene and Graphene Oxide Nanoparticles for Post-MI Cardiac Tissue Engineering and Robust Drug Discovery: A Computationally-Aided Investigation for Enhancing Therapeutic Efficacy

Arvind's synopsis: I am fascinated by the field of cardiac tissue engineering, using stem cells and biomaterials to regenerate the damaged hearts of heart disease patients. Recent advances in biomedical research have opened avenues for patient-specific, stem cell-derived cardiac tissue constructs to be engineered in the lab, transplanted into patients, and investigated as drug testing platforms. In this study, I sought to engineer novel water-based hydrogels to support the formation of these tissues and their non-invasive delivery into patients’ hearts. I was able to create hydrogels that were injectable, conductive and excellent drug-loading repositories. They were also highly biocompatible and supported stem cell proliferation. In addition, I successfully designed robust computational algorithms to evaluate cells and tissues treated under different conditions, accelerating laboratory analysis procedures. Ultimately, I sought to devise low-cost, clinically-viable approaches to further the goals of precision cardiovascular medicine.

Background: I first became interested in this field back in 2014, when I attended a summer program at the University of Pennsylvania, and was able to explore the possibility of using hydrogels for cardiac repair. This past summer, I was fortunate to have the opportunity to attend The Stanford Institutes of Medicine Summer Research Program (SIMR), where I was able to pursue my longtime research goals working in the Joseph Wu Lab under my mentors, Dr. Oscar Abilez and Dr. Huaxiao Yang.

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