Demo Data from EDBT 2026 / reviewers in the wild / expert
Christine Betts
dblp:236/4916
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › fluid dynamics
microfluidics |
0.4 | 1 | 2019 | Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics Platform · ASPLOS 2019 |
Embedded and real-time systems
cyber-physical system platforms |
0.4 | 1 | 2019 | Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics Platform · ASPLOS 2019 |
Methods — techniques the papers use, named apart from their topics
runtime resource management · 0.8computer vision-based error correction · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics PlatformabstractMicrofluidic devices promise to automate wetlab procedures by manipulating small chemical or biological samples. This technology comes in many varieties, all of which aim to save time, labor, and supplies by performing lab protocol steps typically done by a technician. However, existing microfluidic platforms remain some combination of inflexible, error-prone, prohibitively expensive, and difficult to program. We address these concerns with a full-stack digital microfluidic automation platform. Our main contribution is a runtime system that provides a high-level API for microfluidic manipulations. It manages fluidic resources dynamically, allowing programmers to freely mix regular computation with microfluidics, which results in more expressive programs than previous work. It also provides real-time error correction through a computer vision system, allowing robust execution on cheaper microfluidic hardware. We implement our stack on top of a low-cost droplet microfluidic device that we have developed. We evaluate our system with the fully-automated execution of polymerase chain reaction (PCR) and a DNA sequencing preparation protocol. These protocols demonstrate high-level programs that combine computational and fluidic operations such as input/output of reagents, heating of samples, and data analysis. We also evaluate the impact of automatic error correction on our system's reliability. Max Willsey, Ashley P. Stephenson, Chris Takahashi, Pranav Vaid, Bichlien Nguyen, Michal Piszczek, Christine Betts, Sharon Newman, Karin Strauss, Luis Ceze |
ASPLOS | 7 |