About
My name Alan comes from Sir Alan Turing — a figure I regard not merely as the father of artificial intelligence, but as one of the first true bioinformaticians. Beyond the Turing machine and the foundations of computation, Turing was fascinated by the biochemical logic of life itself: his 1952 paper on morphogenesis proposed reaction-diffusion equations that explain how organisms generate spatial patterns — stripes, spots, the architecture of embryos. He was, in the deepest sense, someone who asked how mathematics could speak the language of biology.
I want to build machines that have genuinely learned — and can carry forward — biomedical knowledge. Not tools that retrieve facts, but systems that reason about life the way a trained scientist does: integrating evidence across scales, generating hypotheses, and knowing what they do not know.
Technology has transformed the surface of human life — how we communicate, move, consume, work. But biology is different. It reaches into the interior of each person: the aging of their cells, the unfolding of disease, the molecular basis of who they are. Across the species, it raises questions no prior technology has been equipped to answer. I think about how biological science should advance in the digital age — not just as an engineering problem, but as a scientific, anthropological, and philosophical one.
The Bio+AI revolution, I believe, is not merely a technological transition. It is an ontological one. It has the potential to offer genuine answers — or at least sharper questions — to the problems that existentialism, humanism, and phenomenology have long circled without resolution: What does it mean to exist? What is the boundary of the self? How does consciousness emerge from matter? These are no longer only philosophical questions. They are becoming empirical ones.
Research Interests
Quantitative measurement and mechanistic interpretation of genome-wide regulatory changes underlying human aging, using integrative multi-omic approaches. Particular focus on transcriptomic aging clocks, epigenetic dynamics under infectious stress, and population-scale genomics — with a growing interest in how large-scale biological models can accelerate drug discovery through perturbation-based virtual cell experiments.
Experience
Drug discovery through perturbation via virtual cell experiments. Building AI systems that integrate multi-omic biological knowledge to simulate cellular responses and identify therapeutic targets.
Developed transcriptomic aging clocks; studied epigenetic dynamics during COVID-19. Contributed to Korea4K and Korea10K population genomics datasets. Supervised by Jong Bhak, Ph.D.
Selected Publications
An K, Kwon Y, Bhak J, et al.
Ryu H, An K, Kwon Y, et al.
Jeon Y, Kwon Y, Kim YJ, Jeon S, Ryu H, An K, et al.
Jeon S, Choi H, Jeon Y, …, An K, et al.
An K, Jeon S, Kwon Y, et al.
Awards
First in Course: BIOSCI 759, MEDSCI 732, MATHS 102, BIOSCI 203, MEDSCI 203 · University of Auckland
PGDip Scholarship · University of Auckland (2020)
Distinction in Statistics 101 · University of Auckland (2019)
Ideas & Writing
— postsShare an Idea
Upload a .md or .txt file — or write directly📁 How to publish to GitHub
blog/posts/YYYY-MM-DD-your-title.html
blog/index.html and add a new entry to the posts array in the JavaScript block.git add . git commit -m "New post: Your Title" git push origin main