Signals, Information, and Algorithms Laboratory
Professor Gregory W. Wornell


Selected Publications

  1. M. Shen, Y. Bu, P. Sattigeri, S. Ghosh, S. Das, and G. W. Wornell, "Post-hoc Uncertainty Learning using a Dirichlet Meta-Model", in Proc. AAAI Conf. Artif. Intel. (AAAI-2023), (Washington, DC), February 2023.
  2. T. Arikan, A. Weiss, H. Vishnu, G. B. Deane, A. C. Singer, and G. W. Wornell, "An Architecture for Passive Joint Localization and Structure Learning in Reverberant Environments", J. Acoust. Soc. Am., vol. 153, no. 1, pp. 665-677, January 2023.
  3. S. Medin, F. Durand, W. T. Freeman, and G. W. Wornell, "Can Shadows Reveal Biometric Information?", in Proc. Winter Conf. Appl. Comp. Vision. (WACV-2023), (Waikoloa, HI), January 2023.
  4. A. Lancho, A. Weiss, G. Lee, J. Tang, Y. Bu, Y. Polyanskiy, and G. W. Wornell, "Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals", in Proc. Global Commun. Conf. (GLOBECOMM-2022), (Rio de Janeiro, Brazil), December 2022.
  5. A. Shah, R. Dwivedi, D. Shah, and G. W. Wornell, "On Counterfactual Inference With Unobserved Confounding", in Proc. NeurIPS 2022 Workshop on Causality for Real-World Impact (CML4Impact-22), (New Orleans, LA), December 2022.
  6. X. Chen, L. Liu, D Guo, and G. W Wornell, "Asynchronous Massive Access and Neighbor Discovery Using OFDMA", IEEE Trans. Inform. Theory, November 2022.
  7. J. W. Choi, G. Chowdhary, A. C. Singer, H. Vishnu, A. Weiss, G. W. Wornell, and G. Deane, "Online Segmented Recursive Least-Squares for Multipath Doppler Tracking", in Proc. Underwater Comm., Networking Conf. (UCOMMS-2022), (Lerici, Italy), August 2022.
  8. Gary C.F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell, "Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation", in Proc. Int. Workshop Mach. Learning Signal Process. (MLSP-2022), (Xi'an, China), August 2022.
  9. A. Weiss, T. Arikan, and G. W. Wornell, "Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach", in Proc. Int. Workshop Mach. Learning Signal Process. (MLSP-2022), (Xi'an, China), August 2022.
  10. A. Weiss, E. Huang, O. Ordentlich, and G. W. Wornell, "Blind Modulo Analog-to-Digital Conversion", IEEE Trans. Signal Processing, vol. 70, pp. 4586-4601, August 2022.