I am a Ph.D. student in the Stanford Machine Learning Group where I'm advised by Professor Andrew Ng and Professor Percy Liang. My research focuses on developing machine learning algorithms and applying AI solutions to high-impact problems.
Our deep learning algorithm exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. Dataset 500x larger than previously studied corpora used to train a deep convolutional neural network.
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. With 107,785 question-answer pairs on 536 articles, SQuAD is significantly larger than previous reading comprehension datasets.
A single class can transform a life. A popular introduction to programming class leads to the discovery of a passion for computer science, a social dance class exposes a deep appreciation for artistic expression--experiences like these are at the core of a Stanford education. Yet out of the 5000 classes offered here, students only have time to take less than 1% during their undergraduate career. This small selection of classes determines the foundation on which passions are developed - passions that lead to great innovations and great discoveries that change the world. Some students arrive at Stanford with clear visions of their futures. Others need a little time to explore and decide what to do with their lives. Edusalsa lets students find the classes where they can discover their passions, equipping them with new tools on their path of intellectual discovery, infusing life and vitality into the Stanford experience.
Research in Autonomous Driving spanning Computer Vision, Artificial Intelligence, and Crowdsourcing. My undergraduate honors research introduced Driverseat, a technology for embedding crowds around learning systems for autonomous driving.
Piloted for the Deep Learning Symposium at NIPS '15, Recommend-Papers was built in order to facilitate discussion of the most recent deep learning breakthroughs and explore an alternative mechanism for selecting presentations. In order to let the broader research community (including the authors of research papers) contribute to the discussion, Recommend-Papers allowed members to post papers and comment on them, and PC members to hold private discussions.
Can a computer identify the chord I'm playing on a guitar simply by listening to it? How well does machine learning perform on the task realtime? Could we leverage that technology to give realtime feedback to an instrument learner? This research presents a prototype of an online tool for real-time chord recognition. It fuses traditional techniques in machine learning with the capabilities of new web technologies such the the Web Audio API, and WebSockets.
Machine Learning Experiments (mlx) is a blog to showcase machine learning work intended to showcase machine learning experiments not just in their final polished form, but also highlight the thought process that guides research.
Research in Human Computer Interaction, and Natural Language Processing, exploring how we could teach a computer enough about human actions to enable predictive application interfaces that could, for example, recommend ice cream shops upon learning that a person was having dinner.
Singing is awesome, powerful, and personal. Can we simplify, for amateur singers, the process of exploring new songs to sing along to? Vocalet provides a simple interface for singing enthusiasts to enjoy. It's easy to sing along to karaoke versions of songs, and get inspired by cover artists.
Stanford spaghetti creates opportunities for students to meet other students around campus.