Dr. Hand's primary research interest lies in the intersection of computer vision and machine learning, with an emphasis on understanding human perception. She has performed on several IARPA projects related to face recognition and action recognition from images and video. Her research focuses on bridging the gap between human and computer vision using research in human perception and machine learning.
- Emily M. Hand, Carlos Castillo, Rama Chellappa, "Predicting Facial Attributes in Video using Temporal Coherence and Motion-Attention," Winter Conference on Applications of Computer Vision, 2018. PDF
- Emily M. Hand, Carlos Castillo, Rama Chellappa. "Doing the Best We Can with What We Have: Multi-Label Balancing with Selective Learning for Attribute Prediction," in AAAI Conference on Artificial Intelligence, 2018. PDF
- Emily M. Hand and Rama Chellappa. "Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification," in AAAI Conference on Artificial Intelligence, 2017.
- Maya Kabkab, Emily M. Hand, Rama Chellappa, "On the Size of Convolutional Neural Networks and Generalization Performance," in International Conference on Pattern Recognition, 2016.
- Leslie N. Smith, Emily M. Hand, Timothy Doster, "Gradual DropIn of Layers to Train Very Deep Neural Networks," in Computer Vision and Pattern Recognition, 2016.
- Intro to Machine Learning
- Intro to Artificial Intelligence