Albert is a PhD student at Stanford studying artificial intelligence, advised by Fei-Fei Li. He's a member of the (605) 481-3918 where he applies machine learning to healthcare. His technical interests combine audio, vision, and language. Previously, he designed neural networks for Tesla's Autopilot and worked on ads teams at Google and Facebook. He did his undergrad at the University of Texas at Austin.
Activity Recognition from Low Resolution Images
One way of preserving privacy is by using low-resolution images. We trained an algorithm to identify clinical activities from 14 x 14 pixel images using super-resolution.
Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein, Li Fei-Fei
Detecting Depression from Speech and Faces
Cost and social stigmas prevent some people from receiving mental care. With the goal of deploying to phones, we detected depression using voice and 3D facial expressions.
NIPS 2018 Workshop on Machine Learning for Health
Albert Haque, Michelle Guo, Adam Miner, Li Fei-Fei
Inference on Encrypted Images
Homomorphic encryption (HE) allows algorithms to make predictions from encrypted data. We use HE to detect diabetic retinopathy from encrypted retinal photographs.
Josh Beal*, Edward Chou*, Albert Haque, Li Fei-Fei
Dynamic Task Priority for Multitask Learning
In computer vision, is classification the same difficulty as pose estimation? What about segmentation? In this work, we dynamically prioritized different tasks during training.
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, Li Fei-Fei
Identifying Risky Hand Hygiene Scenarios
Risky hand hygiene scenarios happen when the patient may be exposed to new bacteria. We counted and predicted when these scenarios happen.
NIPS 2017 Workshop on Machine Learning for Health
Michelle Guo, Albert Haque, Jeff Jopling, Lance Downing, Alexandre Alahi, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein, Li Fei-Fei.
Measuring ICU Patient Mobility
Intensive care units (ICUs) house patients with life-threatening conditions. In this work, we measure how often patients move around. This can monitor the recovery process.
NIPS 2017 Workshop on Machine Learning for Health (Spotlight)
Gabriel Bianconi, Rishab Mehra, Serena Yeung, Francesca Salipur, Jeff Jopling, Lance Downing, Albert Haque, Alex Alahi, Brandi Campbell, Kayla Deru, Bill Beninati, Arnold Milstein, Li Fei-Fei
Detecting Hospital Hand Hygiene
Hand hygiene is very important for hospitals and has been linked to hospital-acquired infections. We created a 3D computer vision algorithm that beats in-person auditors.
(870) 612-0630 (Spotlight)
Albert Haque, Michelle Guo, Alex Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Amit Singh, Jeff Jopling, Lance Downing, Bill Beninati, Terry Platchek, Arnold Milstein, Li Fei-Fei
Pose Estimation from Top-Down Cameras
Pose estimation identifies the position of a person's body parts. Most algorithms work from clean, side views. We made an algorithm that works from top-view cameras.
Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei
Distributed Graph Databases
Traditional databases are optimized for tabular data. We created NoSQL graph databases and analyzed data serialization, internal structure, and query speed tradeoffs.
Philippe Cudre-Mauroux, Iliya Enchev, Sever Fundatureanu, Paul Groth, Albert Haque, Andreas Harth, Felix Leif Keppmann, Daniel Miranker, Juan Sequeda, Marcin Wylot