About Me

I am a PhD Student in Bioengineering at UC Berkeley advised by Jay Keasling and Patrick Shih. My research focuses on developing molecular technologies and algorithms to model the evolutionary dynamics of gene regulatory systems. Previously, I studied Electrical Engineering & Computer Science, where I worked on multi-agent systems, and Molecular & Cell Biology, where I worked on biological feedback control.

I am grateful to have my research supported by the H2H8 and NSF Graduate Research Fellowships.

Research Interests

• Machine learning for biological design: active learning and model-guided design of proteins and gene-regulatory elements.
• Evolutionary modeling of gene regulation: learning the dynamics that shape transcriptional control across evolutionary space.
• High-throughput & self-driving labs: closing the loop between large-scale experiments and predictive models.

Teaching & Outreach

• AI4ALL (UCSF): designed and taught machine learning and statistics courses.
• Universidad de Guanajuato: created and taught (in Spanish) a course on ML-driven protein design.
• Graduate Student Instructor, UC Berkeley: “Introduction to Biomedicine for Engineers.”
• Undergraduate Student Instructor, UC Berkeley: “Biophysical Chemistry.”

Selected Publications

1. 

   bioRxiv NeurIPS Workshop First author PDF

   Active learning enables discovery of transcriptional activators across fungal evolutionary space

   L. M. Waldburger, H. Nisonoff, M. Zintel, et al.

   bioRxiv preprint, 2025. Machine LearningSynthetic Biology
2. 

   Nature Biotech Co-first author PDF

   Multiplexed profiling of transcriptional regulators in plant cells

   S. Alamos^*, L. M. Waldburger^*, A. Dee, L. A. Owens, R. Rattan, S. Ong, P. M. Shih

   Nature Biotechnology, 2025. Machine LearningSynthetic Biology
3. 

   ICLR Workshop Co-first author PDF

   Hierarchical Generative Agents for Simulating Sequential Human Behavior

   M. G. Mendoza^*, L. M. Waldburger^*, J. Lee, S. S. Sastry

   ICLR 2026 Workshop on Multi-Agent Learning & Generative AI (MALGAI). Multi-Agent Systems
4. 

   Curr Opin Biotech PDF

   Perspectives for self-driving labs in synthetic biology

   H. García Martín, T. Radivojević, J. Zucker, …, L. M. Waldburger, et al.

   Current Opinion in Biotechnology, 2023. Autonomous Labs
5. 

   mSystems First author PDF

   Transcriptome architecture of the three main lineages of agrobacteria

   L. M. Waldburger, M. G. Thompson, A. J. Weisberg, N. Lee, J. H. Chang, et al.

   mSystems, 2023. Computational BiologyEvolution
6. 

   Nature Biotech Second author PDF

   Binary vector copy number engineering improves Agrobacterium-mediated transformation

   M. J. Szarzanowicz, L. M. Waldburger, M. Busche, G. M. Geiselman, et al.

   Nature Biotechnology, 2025. Synthetic BiologyDirected Evolution
7. 

   Nature PDF

   Modular and tunable biological feedback control using a de novo protein switch

   A. H. Ng, T. H. Nguyen, M. Gómez-Schiavon, …, L. M. Waldburger, et al.

   Nature, 2019. Synthetic BiologyFeedback Control
8. 

   Nature Comm PDF

   Diversity-driven biochemical survey reveals dimeric structural origin of rubisco

   A. J. Kehl, L. Taylor-Kearney, A. L. Jaffe, J. H. Pereira, J. Lee, M. Hammel, …, L. M. Waldburger, et al.

   Nature Communications, 2026. Computational BiologyEvolution
9. 

   Nature Tabula Muris Consortium PDF

   Molecular hallmarks of heterochronic parabiosis at single-cell resolution

   R. Pálovics, A. Keller, N. Schaum, …, L. M. Waldburger, et al.

   Nature, 2022. Computational Biology
10. 

    Nature Tabula Muris Consortium PDF

    A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

    The Tabula Muris Consortium (incl. L. M. Waldburger)

    Nature, 2020. Computational Biology
11. 

    Nature Tabula Muris Consortium PDF

    Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

    The Tabula Muris Consortium (incl. L. M. Waldburger)

    Nature, 2018. Computational Biology