I am interested in increasing access to medical technologies, particularly in low-resource settings. As a PhD student, I develop computational and bio-analytical technologies for early detection of disease, presently focusing on methods to increase sensitivity and multiplexing capabilities in diagnostic devices. Through developing these systems, I get to explore and play with subjects such as statistical modeling, image processing, manipulation and design of molecular systems, and optimization techniques. As a student, I have gotten to take classes ranging from many project based AI/ML computation courses to mathematics in linear dynamical systems to deep dives into chemistry of therapeutic drug development. As I wrap up my PhD, I look forward to bringing my wide base of experiences in both computational and biological realms towards breakthroughs in precision health and diagnostics amenable to lower resource settings at the last mile.
I also am always excited to teach and mentor, and have been involved with a myriad of opportunities including curriculum development and teaching AI/ML to high school students in US and India, K-12 STEM outreach in US, Scratch curriculum teaching to teachers in Taiwan, and graduate level courses such as Biological macromolecules to Stanford students! Im always happy to chat about how to best reach and inspire students and people of all ages, so please reach out!
Honors & Awards
Stanford Graduate Fellowship (SGF), Stanford University (2017-Present)
Graduate Research Fellowship, NSF (2019-Present)
Whitaker International Fellow, Whitaker International (2015-2016)
U.S. Student Fulbright Scholar, Fulbright (2015-2016)
High density DNA data storage library via dehydration with digital microfluidic retrieval.
2019; 10 (1): 1706
DNA promises to be a high density data storage medium, but physical storage poses a challenge. To store large amounts of data, pools must be physically isolated so they can share the same addressing scheme. We propose the storage of dehydrated DNA spots on glass as an approach for scalable DNA data storage. The dried spots can then be retrieved by a water droplet using a digital microfluidic device. Here we show that this storage schema works with varying spot organization, spotted masses of DNA, and droplet retrieval dwell times. In all cases, the majority of the DNA was retrieved and successfully sequenced. We demonstrate that the spots can be densely arranged on a microfluidic device without significant contamination of the retrieval. We also demonstrate that 1TB of data could be stored in a single spot of DNA and successfully retrieved using this method.
View details for PubMedID 30979873
- Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics Platform ASSOC COMPUTING MACHINERY. 2019: 183–97
Random access in large-scale DNA data storage
2018; 36 (3): 242-+
Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted. Here, we encode and store 35 distinct files (over 200 MB of data), in more than 13 million DNA oligonucleotides, and show that we can recover each file individually and with no errors, using a random access approach. We design and validate a large library of primers that enable individual recovery of all files stored within the DNA. We also develop an algorithm that greatly reduces the sequencing read coverage required for error-free decoding by maximizing information from all sequence reads. These advances demonstrate a viable, large-scale system for DNA data storage and retrieval.
View details for DOI 10.1038/nbt.4079
View details for Web of Science ID 000426698700018
View details for PubMedID 29457795