Future Direction: A PhD to Bridge Discovery and Application
What drives all of these interests is a singular vision: to make molecular insights predictive, personalized, and accessible. I seek to pursue a PhD where I can collaborate across disciplines—bioinformatics, structural biology, oncology, and systems medicine—to build computational tools grounded in biology and optimized for clinical utility. Specifically, I am looking to join research groups that explore:
- miRNA-based regulatory networks in cancer and immunity
- Multi-omics modeling for disease stratification
- Machine learning applications in RNA therapeutics
A PhD will not only provide the research infrastructure and mentorship to pursue these goals but also enable me to contribute meaningfully to a field where non-coding RNAs, once considered "junk," are now seen as the key to future diagnostics and therapies.
Conclusion
I aim to integrate these features into deep learning-based architectures, such as convolutional neural networks (CNNs) and transformer models, to capture hierarchical relationships between structure and function. In this context, I am particularly interested in RNA structural elements that are conserved across species and enriched at key regulatory loci, such as UTRs, pseudoknots, and stem-loop motifs. Additionally, I hope to apply attention mechanisms within these networks to visualize and interpret what structural or thermodynamic signals drive binding prediction.
One specific area of application is in studying structured viral RNAs, such as the Rev Response Element (RRE) in HIV, where host miRNAs may regulate viral replication. However, my broader goal is to apply these methods to human cancer-related transcripts, particularly in oncogenic and tumor suppressor pathways, where miRNA deregulation has been extensively implicated but not fully mapped at the systems level.