Lillian Aisha Wood

Junior Full Stack Engineer @ TASK

About Me

About Me

Hello there! My name is Lillian A. Wood. I'm a UCSD Class of 2022 graduate in Cognitive Science - Machine Learning and Neural Computation, with a minor in Business.

I have always been excited to learn, which drew me to software engineering. Starting from a machine learning background, I began designing in my last year of college and continued into full stack Game Design, and currently POS software. I've implemented both front and backend projects, from machine learning algorithms in Python to C# and VB web apps in the .Net framework. I enjoy any atmosphere where I can solve complex problems with unique solutions!

Some more fun facts: I am lucky to be a San Diego native. In my free time I enjoy salsa dancing, exploring the city, traveling, and trying different boba shops.

Lillian Wood Headshot

Projects

Sentiment Analysis Detection of Positive and Negative Steam™ Reviews

Sentiment Analysis Detection of Positive and Negative Steam™ Reviews

Compared sentiment analysis models to automatically detect whether a user's review of a game on Steam is positive or negative based on language alone.

Drew compelling conclusions, digging deeper into what classifies positive and negative attributes of gamer speech. Implemented multiple classification models for sentiment analysis and compared performance metrics. Investigated and communicated background research. Described data and performed cleaning and exploratory data analysis through Python and Github.

UCSD Course: Supervised Machine Learning Algorithms

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Weather or Not: The impact of climate and temperature on Covid-19

Weather or Not: The impact of climate and temperature on Covid-19

Explored the impact of locational temperature and climate differences on COVID-19 case numbers in the U.S.

Acted as group initiator by coordinating and facilitating project implementation. Implemented linear regression models using Python and Github. Obtained dataset, cleaned and described data, and conducted exploratory data analysis. Composed background work, developed hypothesis, communicated analysis and findings.

UCSD Course: Data Science in Practice

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Finding Waldo

Finding Waldo

Used Principle Component Analysis (PCA) to answer the age-old question, "Where's Waldo?"

Successfully re-created Waldo's face using PCA model for facial recognition. Based on prior research on convolutional networks and PCA, this project investigated the effectiveness of a simplified model for identifying Waldo's face in an image. Challenges and areas for future improvement of the model were identified.

UCSD Course: Unsupervised Machine Learning

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Re-designing Canvas' Web & Mobile Apps

Re-designing Canvas' Web & Mobile Apps

Re-designed the web and mobile app versions of Canvas (UCSD's Learning Management System) to provide a better user experience for students.

Students reported an average of 4.5/5 satisfaction rating compared to the original version of Canvas. Utilized an iterative design process, including extensive UI/UX research and iterative prototyping, to improve Canvas design. Acted as project coordinator. Increased team efficiency by organizing meetings, leading group meetings, and facilitating project timeline planning.

UCSD Course: Cognitive Design

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