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Haowen He

Haowen He

PhD student of Bioinformatics
School of Biological Sciences

Decoding neural circuit dynamics across development, behavior, and evolution via mathematics and computation.

My name is Haowen (he/him), and I am a third-year graduate student in the Bioinformatics PhD program at Georgia Tech. For my thesis research, I work jointly in the Streelman Lab, studying brain evolution and complex social behaviors through single-cell neurogenomics, and in the Qiu Lab, developing computational tools for functional genomics and single-cell assays.

Methods

  • Single-cell and spatial omics
  • Graph learning and statistical modeling
  • Deep learning of enhancer codes

Biology

  • Complex social behaviors
  • Forebrain evolution
  • Allele-specific expression

Latest News

Apr. 26

Our paper on cellular dynamics underlying accelerated tooth regeneration is out in eLife!

Apr. 26

Haowen hosted Dr. Sally Temple as part of the School of Biological Sciences Spring 2026 Seminar Series.

Research Themes

Social behaviors are diverse in nature, but it remains unclear how conserved genes, brain regions, and cell populations generate this diversity. I study how this diversity arises from evolutionarily conserved gene regulatory features and cellular states of the vertebrate brain, examining how gene expression, chromatin accessibility, and spatial organization of cell types collectively shape neural circuits and behavior through single-cell neurogenomics. I develop statistical modeling and machine learning approaches to turn rich, high-throughput, high-content biological measurements into quantitative, testable theories and useful computational tools.

Brain, Behavior, and Evolution

Context-dependent processes like embryonic development, the regeneration of organs and complex behavior are fascinating because they reveal new rules of biological systems that are not necessarily operational during homeostasis. For instance, recent results suggest that stem-like cells in the brain may tune the evolution of male social behavior. Using single-cell multi-omic profiling of the vertebrate forebrain, I investigate how brain circuits are rewired, how the composition of specific cell populations is rebalanced, and how epigenomic information and cis-regulatory enhancer–gene mapping are dynamically reconfigured across behavioral states.

neural circuit plasticity development & regeneration cis-regulation

Single-Cell & Spatial Omics

Single-cell and spatial omics provide unprecedented resolution to reconstruct cell states, lineage relationships, and tissue architecture in situ. I develop and apply multi-omic analytic frameworks to map cell-type diversity, resolve how behavioral state reshapes cellular programs over time, and link cis-regulatory elements to gene expression within spatial context. Through these efforts, I aim to uncover how spatial organization and regulatory landscapes coordinate cellular function across development, regeneration, and behavior.

gene regulatory networks transcriptional synchrony spatiotemporal modeling cell-type-resolved genetic mapping

Statistical Learning & Modeling

While I rarely build new tools for their own sake, single-cell data from non-model species is heterogeneous, high-dimensional, and structured in ways that off-the-shelf methods struggle to handle. To extract interpretable understanding from the data, I develop approaches that combine graph-based statistical modeling with modern machine learning, spanning latent representation learning, deep learning characterization of enhancer code evolution, and probabilistic methods for cell-type trajectory inference, causal GRN modeling, cell phylogeny reconstruction, and allelic imbalance detection.

graph learning probabilistic modeling trajectory inference

Selected Publications

Figure from accelerated whole tooth regeneraation

Cellular basis of accelerated whole-tooth regeneration

T Mubeen, H He, GW Gruenhagen, A Satoskar, JT Streelman
eLife 2026 PDF
Figure from scGENUS vertebrate forebrain evolution

Evolutionarily informed gene sets reveal conserved and lineage-modified transcriptional programs during vertebrate forebrain evolution

H He, JT Streelman, P Qiu
Submitted 2026 bioRxiv
Figure from Bayesian BiLO with Low-Rank Adaptation

Bayesian BiLO: Bilevel Local Operator Learning for Efficient Uncertainty Quantification of Bayesian PDE Inverse Problems with Low-Rank Adaptation

RZ Zhang, CE Miles, X Xie, JS Lowengrub
Submitted 2025 arXiv
Google Scholar

The Team

Jacy Zanussi

Jacy Zanussi

PhD Student UCI MCSB

stochastic gene expression + simulation-based inference

fav veggie: all peppers 🌶️ (approved as honorary vegetable)

Austin Marcus

Austin Marcus

PhD Student UCI MCSB

nuclear condensates, cilia (co-advised w/ Jun Allard)

fav veggie: onion 🧅

Rebecca Gu

Rebecca (Rujie) Gu

PhD Student UCI Math

physics-informed neural networks + point processes

fav veggie: spinach 🍃

Matt Lastner

Matt Lastner

PhD Student Utah Math Bio

meiosis biophysics, polymer models (co-advised w/ Ofer Rog)

fav veggie: sweet potato 🍠

🥦

You?

Prospective students can apply to the Utah math graduate program and/or reach out to Chris. Be prepared to defend your favorite vegetable.

Apply to Utah Math

About Chris

Miscellany