I am an Assistant Professor in the Department of Bio and Brain Engineering and Graduate School of Engineering Biology at KAIST. Previously, I had the opportunity to work very closely with molecular biologists in V. Narry Kim’s lab at Seoul National University. In 2016, I received my Ph.D. in Computer Science at Princeton University working with Olga G. Troyanskaya. As an undergraduate, I majored in both Computer Science and Mathematics at the University of Texas at Austin where I was first introduced to computational biology by Tandy Warnow.
Please read the Featured Spotlight for more about my journey as a computational biologist, my advice to undergraduates and graduate students, and why I stayed in academia.
If you are interested in working with me, please feel free to contact me with a brief overview of your background and research interests.
PhD in Computer Science, 2016
Princeton University
BSc in Computer Science, 2010
The University of Texas at Austin
BSc in Mathematics, 2010
The University of Texas at Austin
I never teach my pupils; I only attempt to provide the conditions in which they can learn.
– Albert Einstein
The advent of massive open online courses has changed the way we look at education and challenges traditional views on the role of instructors. Simple transfer of knowledge is no longer the rate limiting step for educating the next generation. Instead, knowledge is now accessible to anyone with a computer, tablet or mobile phone with a connection to the internet. I’ve also benefited tremendously from these initiatives, but at the same time forced me to reevaluate my pedagogical values. This led me to my three foundations of instruction and mentorship: construction, selection, and interaction. All of which are the basis of the following courses shared below.
The science of today is the technology of tomorrow.
– Barbara McClintock
Biology is not random, just largely unknown. There are almost an infinite amount of possible interactions, yet only a sparse handful constitutes a complex living system. To narrow down this vast search space, massive amounts of biological data are being generated to capture snapshots or snippets of the functional genome, multicellular heterogeneity, and complex human diseases. In this effort, bioinformatics algorithms play a key role in interpreting these large data collections and elucidating the underlying principles, both at the molecular and system levels.
The Young Laboratory at KAIST draws upon ideas from data science, applied statistics, and machine learning to tackle fundamental questions in quantitative biology. We incorporate problem-specific knowledge into the behavior of our algorithms to address the challenge of underspecification in modern machine learning methods. One of our primary objectives is to complete the human gene regulatory network by utilizing these problem-specific algorithms. Specifically, we aim to map the missing axes of functional RNAs in terms of RNA modification, RNA structure and Protein-RNA interaction.
Only 2% of the human genome consists of protein-coding genes. The remaining 98% is non-coding and thought to encode the regulatory information for gene expression. Our lab develops problem-specific computational tools to interpret this non-coding region of the human genome. In particular, we focus on elements of the genome that are transcribed to functional RNAs. Advances in biochemical and high-throughput techniques provide strong evidence that 74.7% of the human genome undergoes transcription, thus highlighting the importance of RNA research in functional genomics.
To tackle this, we take advantage of biological data generated from breakthroughs in chemical biology and bioengineering such as short/long-read sequencing, oligo synthesis, chemical probing, and click chemistry. The technology-specific computational tools built from our lab offer the means towards integrative genomics and functional interpretation at single-nucleotide resolution across transcription, processing, modification, translation, decay, and other stages of the RNA life cycle.
It’s an exciting time to work in modern biology and bioengineering. Innovations in artificial intelligence and high-throughput techniques provide new strategies to understand complex cellular processes and investigate the molecular mechanisms underlying human diseases. For example, single-cell sequencing and spatial transcriptomics have shed light into the cellular heterogeneity of human physiology and tissue complexity in organismal development, immunology, and cancer biology.
The algorithmic task here is to address inherent computational challenges in each high-throughput technology and incorporate biology-specific knowledge into the design of computational tools, statistical models, and neural architectures. We compare our tailored solutions with general-purpose machine learning methods, which also serve as case studies in computational biology of the “no free lunch” (NFL) theorem of David Wolpert and William Macready.
RNA therapeutics, genome editing, and organoids represent just a few examples of biomaterial applications that are changing the way we solve biology. However, these endeavors are often combinatorial optimization problems with near-infinite potential but intractable tasks with brute-force solutions. For example in RNA engineering, there are more than 1060 possible 100-nucleotide sequences with varying degrees of functionality. To put this into perspective, the estimated number of atoms on Earth is approximately 1050 atoms, indicating the limit of solely relying on high-throughput screening for RNA design and optimization.
Our approach involves first extracting meaningful insights and principles from molecular biology and functional genomics. We then leverage this knowledge and develop powerful search algorithms for the computational design of functional RNAs and other bioproducts. Ultimately, our goal is to establish a computational platform for translational bioengineering that drives progress across diverse biomaterial applications.
We may have all come on different ships, but we’re in the same boat now.
– Martin Luther King, Jr.
*equal contributions #corresponding author
The essence of strategy is choosing what not to do.
– Michael Porter
*equal contributions #corresponding author
I confess I do not know why, but looking at the stars always makes me dream.
– Vincent Van Gogh
Here is the list of places I’d like to get my specific cup of coffee.