Students

Student Profile:

As an aspiring physician-scientist, I am deeply committed to bridging the gap between basic science and clinical medicine and bringing scientific innovations closer to patients. Through my experiences in undergraduate and graduate research, I have been fortunate to gain experience in a variety of scientific methodologies, including organic synthesis, protein and nucleic acid analysis techniques, and cell culture experiments. However, while these techniques are important for understanding fundamental biological processes, I have come to appreciate the immense potential of data-driven approaches to revolutionize scientific research and medicine. My passion for incorporating bioinformatics and data science into medical research stems from my belief that these tools can enable us to identify previously unrecognized patterns and relationships crucial for understanding disease pathophysiology and developing new treatments.
With the latest advances in machine-learning and bioinformatics, I aim to unravel intricate molecular mechanisms underlying complex disorders. I am conducting my PhD dissertation research in Dr. Chunyu Liu's lab, where I am investigating the epigenetic regulation of brain functions in the context of Alzheimer's Disease. My goal is to uncover novel mechanisms that could serve as therapeutic targets for neurodegenerative and psychiatric pathologies. I deeply value the importance of interdisciplinary collaboration in scientific research, especially in the field of bioinformatics. I am constantly seeking opportunities to work with other research groups and learn from their expertise in applying novel methodological approaches for multiomics data analysis in the context of disease. Our team actively maintains collaborations with other bioinformatics groups to expand our knowledge and stay up-to-date with the latest techniques and tools. By leveraging computational tools and machine learning algorithms, we can uncover novel insights from complex datasets that are not easily discernible through traditional analytical methods.

Publications

https://orcid.org/0000-0003-4169-1322

Fellowship/Awards

NIH Ruth Kirschstein Pre-Doctoral Fellowship (F30)