Zeyu (Alban) Li
Ph.D. Candidate in Biological and Environmental Engineering
Cornell University
DNA Material Lab
About
I bridge DNA biotechnology and materials science to create manufacturable solutions that scale from lab prototype to production. (Available for full-time roles in Materials or Biotech R&D, starting May 2026.)
Academic & Research Background
Ph.D. Candidate in Biological and Environmental Engineering at Cornell University DNA Materials Lab with Prof. Dan Luo, specializing in polymer materials and DNA biotechnology. I build DNA nanoparticle tracers for field-scale hydrology and DNA-composite hydrogels that repair themselves and respond to stimuli.
I also hold Cornell's M.S. and M.Eng. degrees, and a B.S. in Chemistry and Computer Science from Hong Kong Baptist University, where I trained in the Micro-fabrication & Surface Materials Lab with Prof. Kangning Ren.
Key Achievements
- ✓ DNA Tracers: 11 km² field-scale deployment; qPCR detection to 7 km with 1 mg DNA; hydrodynamic model calibration
- ✓ DNA Hydrogels: thermally reversible; 3D-printable; stimuli-responsive
- ✓ DNA Production from Biomass: cost reduced ~91%
- ✓ Publications & Patents: 3 publications; 2 patents
Value & Approach
I translate across materials, bioengineering, chemistry, environmental science, and computation to turn concepts into manufacturable, scalable solutions.
For Materials Industry
I bring cross-field training and disciplined characterization to make process-aware decisions that scale from lab to production. My toolkit spans materials characterization, polymer and hydrogel processing, nano-fabrication, and 3D printing.
For Biotech Industry
I treat DNA and RNA as materials, applying a chemist's perspective to design solutions built for robustness and manufacturability. I work across DNA/RNA design-and-build workflows, including sequence design, formulation, synthesis, purification, enzymatic reactions, and quantification and characterization.
Emerging Technologies & Expertise
AI & Automation in R&D: I integrate AI into day-to-day R&D through workflow automation, data pipelines, and lightweight agents for analysis and visualization. These practices standardize analysis and documentation and shorten the path from prototype to production. My tracer study's data analysis and figures were built with AI-assisted Python workflows. I also hold an ML-with-Python certification and a CS minor, and I regularly guest lecture on practical AI for research at Cornell.