I am an Assistant Professor in the Department of Bio and Brain Engineering 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.


  • Bioinformatics
  • Functional genomics
  • Molecular biology
  • Probabilistic modeling
  • Single-nucleotide analysis


  • 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


The advent of massive open online courses (MOOCs) has changed the way we look at education and further questioned the fundamental role of instructors. Simple transfer of knowledge is no longer the rate limiting step for educating the next generation, but knowledge is democratized to anyone with a computer, tablet or mobile phone with a connection to the internet. I personally have benefited tremendously from such efforts, yet MOOCs have forced me to reassess my pedagogical ideals. This led me to the three foundations of instruction and mentorship: construction, selection, and interaction. All of which are the basis of the following courses shared below.


Biology is not random, just largely unknown. There are almost an infinite amount of possible interactions, but only a sparse handful constitutes a complex living system. To narrow the search space, massive amounts of biological data are being generated to capture snapshots or snippets of such living systems. In this effort, bioinformatics algorithms play a key role in interpreting these large datasets and enable the reconstruction of underlying biological principles both at the molecular and system level.

The Young Laboratory at KAIST draws upon ideas from data science, applied statistics, and machine learning to tackle fundamental questions in quantitative biology and biomedical engineering. Our algorithms are optimized according to a specific data generation process which means we often ask for copies of experimental protocols and lab notes from our collaborators. In particular, we are interested in (1) decoding the human genome by developing probabilistic models at single-nucleotide resolution and (2) encoding those molecular insights within the context of large biological networks.


Probabilistic modeling at single-nucleotide resolution

  • Genetic variants of the non-coding genome
  • Nucleotide modifications
  • Rational design of biomolecules

Reconstructing the gene regulatory network

  • Integrative genomics
  • Complex networks
  • RNA regulation

Bioinformatics algorithms

  • Single-cell analysis
  • Long-read sequencing
  • Mass spectrometry


Graduate students


  • 하성현 (Summer 2023) Life Science @ Korea University
  • Aleksandra J. Wisniewska (Summer 2023) Bio and Brain Engineering @ KAIST
  • 김민정 (Winter 2022) Genetic Engineering @ Kyung Hee University
  • Shubhangi Kumar (Winter 2022) Computer Science @ KAIST
  • 안규찬 (Winter 2022) Bio and Brain Engineering @ KAIST
  • Daniil Melnichenko (Summer 2022) Chemistry @ KAIST
  • 김동근 (Summer 2022) Computer Science @ KAIST
  • 김대원 (Summer 2022) Computer Science @ KAIST
  • 김근희 (Spring 2022) Biological Sciences @ KAIST
  • 김민주 (Spring 2022) Bio and Brain Engineering @ KAIST
  • 박해준 (Winter 2022) Computer Science @ KAIST
  • Azamat Armanuly (Fall 2021) Biological Sciences @ KAIST
  • Benedict Fabia (Fall 2021) Undeclared @ KAIST
  • 정다현 (Summer 2021) Biological Sciences @ KAIST
  • 이지현 (Summer 2021) Computer Science @ KAIST


(*equal contribution)

Mathematical models of deadenylation

In press

Near-optimal variant calling by pseudo-database construction

Under review

Deadenylation kinetics at single-nucleotide resolution

In press


(*equal contribution)

(2023). Short poly (A) tails are protected from deadenylation by the LARP1–PABP complex. Nature Structural & Molecular Biology.

DOI pubmed

(2022). Functional and molecular dissection of HCMV long non-coding RNAs. Scientific Reports.

DOI pubmed

(2022). The m6A(m)-independent role of FTO in regulating WNT signaling pathways. Life Science Alliance.

DOI pubmed

(2021). Crosstalk between Fat Mass and Obesity-related (FTO) and multiple WNT signaling pathways. biorxiv.

DOI biorxiv

(2021). The SARS-CoV-2 RNA interactome. Molecular Cell.

DOI pubmed


Here is the list of places I’d like to get my specific cup of coffee.

  • Daejeon, South Korea (work in progress)
  • Seoul National University
    • Gabean Coffee Roasters : Pour-Over (+refill)
    • A Twosome Place : Long black + extra shot
    • Hollys Coffee : Cold brew
  • Princeton, NJ


  • youngl@kaist.ac.kr
  • +82-42-350-7924
  • 1113 CMS(E16), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea
  • Enter Building E16 and take the elevator to Office 1113 on Floor 11