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[INFO] System initialized
[INFO] Neural networks online
[INFO] Biological simulations running
[INFO] Quantum interface stable
[INFO] Synthetic biology module loaded
[WARN] Consciousness emergence detected
[INFO] Monitoring life patterns
[SYSTEM] Ready for input...
Research Interests
Manifolds in Gene Expression & Spatial Biology
Exploring the mathematical structure of gene expression spaces, spatial transcriptomics, and how optimal transport and latent spaces reveal biological insights.
Single-Cell & Multi-Omic Data Integration
Developing computational methods to integrate multi-modal single-cell data, enhancing our ability to understand cellular states and transitions.
Graph Neural Networks for Biological Systems
Advancing graph-based machine learning models to capture complex relationships in biological systems, improving our ability to infer cellular interactions, correct for sampling artifacts, and uncover emergent properties in high-dimensional biological data.
LLMs for Bioinformatics Code Generation
Harnessing large language models to accelerate bioinformatics workflows, enabling more efficient data analysis and computational tool development.
Building a Biologically Driven Society
Leveraging synthetic biology, ecological design, and agricultural innovation to create sustainable systems that integrate seamlessly with natural ecosystems, fostering a future where human progress and biology evolve together.
Computational Platforms for Translational Research
Building scalable, open-source tools that bridge computational biology with experimental research, driving innovation in medicine and biotechnology.
Featured Projects
Impeller: A path-based heterogeneous graph learning method for spatial transcriptomic data imputation, enhancing resolution and data integrity in spatial omics.
run impeller-impute.shCharacterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes, uncovering signaling alterations in neurodegeneration.
run alzheimers-cellcomm.shLeprendo: An agricultural logistics platform revolutionizing local food distribution, empowering farmers and markets through technology-driven efficiency.
run leprendo-service.shOpen Tech Bio: Advancing open-source bioinformatics and computational biology, creating accessible tools for researchers and innovators in synthetic and systems biology.
run open-tech-bio.sh#-----------------------
I am driven by a fascination with life both natural and artificial and how intelligence emerges across biological and computational systems. My work explores the mathematical structures underlying biological processes, from the manifolds of gene expression and spatial biology to the development of AI-driven bioinformatics tools that accelerate discovery in medicine and synthetic biology.
My background spans computational biology, machine learning, and mathematics, allowing me to bridge theoretical frameworks with real-world applications. I am particularly interested in how geometry, topology, and optimal transport shape biological organization and how we can leverage these principles to build more insightful models of cellular systems.
Beyond research, I enjoy designing computational platforms that empower scientists, fostering open-source collaboration in bioinformatics, and exploring ways to integrate synthetic biology and ecological principles into a more biologically connected society. I also have a deep appreciation for the aesthetic beauty of mathematical structures in nature and often explore these ideas through geometric visualizations.
I'm always open to discussions and collaborations at the intersection of computation, biology, and technology. Feel free to reach out through any of the social platforms linked below.