All Publications


  • Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World IEEE INTERNET OF THINGS JOURNAL Firouzi, F., Farahani, B., Daneshmand, M., Grise, K., Song, J., Saracco, R., Wang, L., Lo, K., Angelov, P., Soares, E., Loh, P., Talebpour, Z., Moradi, R., Goodarzi, M., Ashraf, H., Talebpour, M., Talebpour, A., Romeo, L., Das, R., Heidari, H., Pasquale, D., Moody, J., Woods, C., Huang, E. S., Barnaghi, P., Sarrafzadeh, M., Li, R., Beck, K. L., Isayev, O., Sung, N., Luo, A. 2021; 8 (16): 12826-12846
  • Ethical issues in using ambient intelligence in health-care settings. The Lancet. Digital health Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S. S., Wieten, S., Cho, M. K., Magnus, D., Fei-Fei, L., Schulman, K., Milstein, A. 2020

    Abstract

    Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.

    View details for DOI 10.1016/S2589-7500(20)30275-2

    View details for PubMedID 33358138

  • Unsupervised Learning of Long-Term Motion Dynamics for Videos Luo, Z., Peng, B., Huang, D., Alahi, A., Li Fei-Fei, IEEE IEEE. 2017: 7101–10
  • Label Efficient Learning of Transferable Representations across Domains and Tasks Luo, Z., Zou, Y., Hoffman, J., Fei-Fei, L., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017