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- ECE PhD Prospectus Defense: Arman Tan9:00 am
- Workshop on Love, Hate, and Reactive Attitudes9:30 am
- Marsh Chapel's Global Stress Relief Day10:00 am
- ECE PhD Thesis Defense: Timur Zirtiloglu11:00 am
- NSF Broader Impacts in a Different Climate12:00 pm
- ECE PhD Thesis Defense: Yangruirui Zhou2:30 pm
- The Future of English Learners in an "Official English" America, Conversations with the Dean3:00 pm
- Undergraduate Affairs Experiential Learning Expo3:00 pm
- Visit from Bean the Comfort Dog3:30 pm
- [HBC] Careers in Biotech: Venture Investments4:00 pm
- Educational Leadership & Policy Studies (EdD) Info Session6:00 pm
- Student Made Winter Market Pop-Up6:00 pm
- PhD Seminar Series: Holiday Party12:00 pm
- Study Break: Bullet Journaling & Buns1:00 pm
- ECE PhD Thesis Defense: Ashley Antony Gomez1:30 pm
- MSE Talks: Alon Gorodetsky3:00 pm
ECE PhD Thesis Defense: Yangruirui Zhou
ECE PhD Thesis Defense: Yangruirui Zhou
Title: Constraint-Driven Microfluidic Design Automation Frameworks for Scalable Biological Networks
Presenter: Yangruirui Zhou
Advisor: Professor Douglas Densmore
Chair: Professor Rabia Yazicigil
Committee: Professor Ayse Coskun, Professor Wenchao Li, Professor Catherine Klapperich.
Google Scholar Link: https://scholar.google.com/citations?user=Zel-iSQAAAAJ&hl=en
Abstract: Microfluidic devices allow precise, programmable control of fluids for biological and chemical workflows, but their design and operation still rely heavily on manual expertise. This dissertation develops computational tools that automate key stages of microfluidic experimentation, making these systems more reliable, scalable, and accessible.
The work introduces methods for verifying whether experimental protocols are compatible with specific device architectures, algorithms for organizing and partitioning large biological networks, and a web-based platform that automatically generates microfluidic layouts from high-level specifications. Together, these approaches reduce design time, prevent execution errors, and support complex biological workflows that would be difficult to construct manually.
These contributions establish a unified framework for constraint-driven microfluidic automation and point toward a future in which high-level biological designs can be rapidly translated into reproducible experimental platforms.
| When | 2:30 pm - 4:30 pm on 11 December 2025 |
|---|---|
| Building | PHO 339 |