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Lisa C Liao

Design and Engineering Portfolio

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ABOUT ME

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Hello there!

 

My name is Lisa, and I’m a UI/UX Designer and Software Engineer passionate about creating human-centered technology!

 

I currently work at Google as a Software Engineer and Front-End Engineering Lead on the Cross Google Engineering team, the group responsible for defining and driving the technical roadmap underpinning Google’s $50B+ annual engineering investments. In this role, I help teams across Google’s diverse product areas align with company-wide technical guidance.

Previously, I worked at Microsoft on Microsoft Outlook and at Stanford Medicine on infectious disease research, where I collaborated with the Stanford Healthcare Innovation Lab to develop an application that analyzes wearable health data to detect the onset of COVID-19 an average of four days before symptom onset. This work is published in Nature.

 

I received my Master’s degree in Computer Science and my Bachelor’s degree in Symbolic Systems (Human-Computer Interaction concentration) with a minor in Creative Writing from Stanford University.

 

In my free time, I enjoy writing and am an award-winning short story and young adult fiction author, playing the piano and am an award-winning musician and composer, and training for triathlons as an Ironman triathlete.

 

Welcome to my portfolio!

PORTFOLIO

Here are my most recent design and engineering projects!

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MyPHD: COVID-19
 Detection

In collaboration with the Stanford Healthcare Innovation Lab, I developed the MyPHD Android app, an app that analyzes a user's wearable device health data to detect the onset of COVID-19 an average of four days before symptom onset. As one of the first designers on the team as well as the first engineer to work on the Android app, I created the full first version of the app and led its development after expansion of the team. A paper on our research is published in Nature.

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PLANIT: TRAVEL WITH A GAME PLAN

Planit is an app that gamifies the travel planning process to help travelers overcome the familiar struggle of planning for group destination trips: getting the input of family and friends on joint travel plans. One of my first mobile app projects, Planit was designed and developed collaboration with a few friends! Click "Learn More" below to view our findings and design process.

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SHIELD: MAKE AND DONATE MASKS

Made in the beginning of the COVID-19 pandemic amidst wide shortages of personal protective equipment (PPE), Shield is an app that helps people make masks both for themselves and to donate to places in need. With Shield, users can find approved face mask patterns, learn to sew face masks, and connect with organizations that accept donations of homemade face masks. I created the full design of the app, as well as developed the Make Masks and Recommendation screens of the app. The "Donations" tab was developed in collaboration with a few friends!

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RECIDIVISM: THE GAME

Recidivism: the Game is a system game designed to teach people about recidivism. Players take on the role of someone who must confront the strong incentives of turning to crime when there is a lack of other economic opportunities. By playing this game, players learn how imprisoning people without a proper reentry plan can actually incentivize further crime. After hearing about the severity and complexity of recidivism, two friends and I, designed and developed the first version of the game to raise awareness of the issue before we expanded the team. Our research shows that the game successfully teaches players about the recidivism cycle, motivates players to care more about recidivism, is fun and engaging, and leads to a new mental model.

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WORDCLOUD

WordCloud is a new way for families to grow closer while learning new vocabulary words from watching fun YouTube Kids videos together. A few friends and I worked together to design WordCloud to help families with parents who don’t speak fluent English help their children learn new vocabulary when not in school.

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MAHJONG AI PLAYER

A few friends and I developed an AI agent that plays Mahjong independently. Trained via Deep Q-learning, the agent reliably beats baseline players at a success rate of 90%, with the other 10% being draws.

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