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.

ChexNet - Radiologist-Level Pneumonia Detection

Active project for 3 months with Jeremy Irvin and Professor Matt Lungren, Professor Andrew Ng

We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our model, CheXNet, is a 121-layer convolutional neural network that inputs a chest X-ray image and outputs the probability of pneumonia along with a heatmap localizing the areas of the image most indicative of pneumonia. We train on ChestX-ray14, the largest publicly available chest X-ray dataset. We find that the model exceeds the average radiologist performance at the pneumonia detection task on both sensitivity and specificity.

Cardiologist-Level Arrhythmia Detection

Active project for a year with Awni Hannun and Professor Andrew Ng

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.


Active project for 2 years with Professor Percy Liang

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.


Active project for 4 years with Brad Girardeau

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.


From Jan 2013 up to May 2015 with Professor Andrew Ng

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.

Recommend Papers

From Jul 2015 up to Dec 2015 with Professor Andrew Ng, Professor Yoshua Bengio et al.

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.

Chord Recognition

From Oct 2014 up to Dec 2014 with Brad Girardeau and Toki Migimatsu

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.


Active project for a year

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.


From Jun 2014 up to Sep 2014 with Ethan Fast and Professor Michael Bernstein

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.


From Jan 2013 up to Mar 2013 with Vincent Su

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.