About Me
I received my Bachelor’s degree in Mechanical Engineering from the City College of New York. While I was studying there, in 2018, I joined Professor Jing Fan’s research on producing multilayered flow-focusing microcapillary devices with three other students for a year. We were designing a novel device that could encapsulate microscopic droplets with larger different-component droplets, which could potentially be used to create multilayered medical capsules that dissolve at a specific rate within the body, creating timed, multi-drug treatments. After months of designing and iterating, we found the optimal design and filed a US Provisional Patent Application in 2019 under Professor Jing Fan.
After receiving my bachelor’s degree, I got accepted into Columbia University Bridge to CS MS program, and started to explore the beauty of computer science. While in the Bridge Program, I participated in Georgetown University Hoya Hacks Hackathon with another three students as a team. We competed with more than 400 participants and won the “Build a Game Track”. After one year of study, I will enter the MS portion in Fall 2022 with an expected graduation of Dec 2023. I am particularly interested in the fileds of software engineering, database management and data science.
Besides interest in STEM fields, I also love music. I studied violin & viola at the Juilliard Pre-college while I was in high school, and became principal violist of the pre-college symphony later. I was also accepted into the New York Youth Symphony and ended up performing many times in Carnegie Hall, both solo and with the NYYS. I have been teaching violin in a private music school in weekends for many years, and many of my students have received high scores in New York State School Music Association festivals.
About My Advisor
Eugene Wu is an Associate Professor of Computer Science at Fu Foundation School of Engineering and Applied Science at Columbia University. He develops systems and algorithms for modern interactive data analysis. His research focuses on the full interactive data analysis stack: from data cleaning and preparation, to scalable systems for interactive exploration interfaces, to automatic interface generation, to explanation tools that help explain anomalies encountered during data analysis. His current project is the Data Visualization Management System, which integrates concepts from database research, such as declarative languages, query optimization, and lineage, with interactive visualizations, making it easier to design, architecture, build, and scale rich visual data exploration systems.
Professor Wu’s research spans the areas of core database optimization, stream processing systems, crowdsourcing, data visualization, data cleaning, and HCI. His work includes SASE, one of the first high performance complex event processing systems for high throughput data streams; Scorpion, which introduced a novel analysis feedback system that explains anomalies that analysts find in data visualizations; ActiveClean, the first interactive data cleaning algorithm designed for data science; and Precision Interfaces, the first large-scale automatic interface generation system. His current work in data visualization management systems draws connections between data visualizations and data processing systems and unifies them under a single system abstraction. The interdisciplinary nature of his research leads Wu to work closely with researchers in information visualization, perception, theory, and machine learning.
Professor Wu received a BS in electrical engineering and computer science from UC Berkeley in 2006, a PhD in electrical engineering and computer science from MIT in 2015, and was a Postdoctoral Fellow at UC Berkeley in 2015.
See here for Professor We’s websites: https://www.engineering.columbia.edu/faculty/eugene-wu http://www.cs.columbia.edu/~ewu
About My Project
Billions of dollars of crops are lost each decade to disasters (Link), and climate change may impact where and how much of different crops can be produced (Link). To counter the ever-present and growing risk to crops, insurance programs have been created to help farmers all over the world.
Some of these programs employ index insurance (i.e., insurance that provides a payout at a trigger reached on some index). Index insurance creates a clear way to provide payouts, but it also simplifies the complexity of the world. This creates a dilemma; how do you design a good trigger so no one is left behind while also incentivizing people to produce as much as possible? One approach is to try and adjust your quantitative index for feedback from in-person assessments.
In partnership with the Financial Instruments Sector Team (FIST) of the International Research Institute for Climate and Society at the Columbia Climate School, our team under Professor Wu works to develop an open-source toolkit for index insurance. That means we can easily deploy a database and corresponding visualizations that allow people to tweak the parameters that go into the payout trigger. The database can execute queries against satellite and other data in an efficient manner allowing for rapid data visualizations. The end objective is to create an interface where end-users can tweak their parameter choices based on how the visuals change.
In the case of farming, the tool helps to ensure that a trigger can be reached in which both insurance providers and farming communities believe fair. In short, it helps make a trigger that encourages the adoption of farm insurance, lessening the greater risk to crops that we live with today. More generally, the work we are doing will allow others to deploy low-cost index insurance schemes that take into account both quantitative and qualitative data.
Our toolkit runs on open source technologies including Svelte, DBT, Flask, and duckDB web assembly among others.