Research Project Scientist
PostDoc with Tom Mitchell
Dr. Arabshahi's research focuses on recovering hidden hierarchical structures underlying large scale data and leveraging hierarchies to develop accurate models for real world problems. She conducts research on neural programming, topic models, spectral methods and probabilistic graphical models. Arabshahi is currently working on the Learning by Instruction Agent (LIA) project in Dr. Tom Mitchells's lab.
Dr. Aragam's main interests involve problems at the intersection of high-dimensional statistics and machine learning, with a focus on developing scalable algorithms with sound theoretical guarantees. He is particularly interested in graphical models, multi-task learning, and learning in nonconvex and distributed settings. Much of his work is motivated by applications to computational biology, genomics, and causal inference.
PostDoc with Byron Yu
PostDoc with Pradeep Ravikumar
igh-dimensional probabilistic graphical models, visualization of machine learning models (particularly graphical models), and unsupervised deep learning for generative probabilistic models
Project Scientist with Tom Mitchell
Dr. Liang has interests in broad computer vision, deep learning directions and analyzing biological images. Specifically, her research works mainly focus on generating hierarchical knowledge representation for the images, including the basic object detection and images, and the higher-level visual reasoning.