Biao Chen

John E. and Patricia A. Breyer Professor in Electrical Engineering

Electrical Engineering and Computer Science

3-179 CST

bichen@syr.edu

315.443.3332

Personal Website

Degree(s):

  • Ph. D., University of Connecticut

Lab/Center Affiliation:

  • Communication Laboratory

Areas of Expertise:

  • Information Theory
  • Signal Processing
  • Statistical Learning Theory

Chen’s area of research interest mainly focuses on information theory, signal processing, and foundational theory to machine learning, with applications to wireless communications and sensor networks. On the applied side, he has worked extensively on software radio system design, including leading two student teams to compete as finalist in the DARPA Spectrum Challenge and DARPA Spectrum Collaboration Challenge. His most recent endeavors include the development of passive RF sensing theory and systems for a variety of indoor situational awareness missions.

Honors and Awards:

IEEE Fellow (2015)

NSF CAREER Award (2006)

Selected Publications:

  • Y. Liu, T. Wang, Y. Jiang and B. Chen, “Harvesting Ambient RF for Presence Detection Through Deep Learning” , IEEE Trans. Neural Networks and Learning Syst., vol. 33, no. 4, pp. 1571-1583, April 2022, doi: 10.1109/TNNLS.2020.3042908.
  • S. Zhu, B. Chen, Z. Chen and P. Yang, “Asymptotically Optimal One- and Two-Sample Testing With Kernels,” in IEEE Transactions on Information Theory, vol. 67, no. 4, pp. 2074-2092, April 2021, doi: 10.1109/TIT.2021.3059267.
  • G. Xu, W. Liu and B. Chen, “A Lossy Source Coding Interpretation of Wyner’s Common Information,” in IEEE Transactions on Information Theory, vol. 62, no. 2, pp. 754-768, Feb. 2016, doi: 10.1109/TIT.2015.2506560.
  • H. Chen, B. Chen and P. K. Varshney, “A New Framework for Distributed Detection With Conditionally Dependent Observations,” in IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1409-1419, March 2012, doi: 10.1109/TSP.2011.2177975.
  • X. Shang, G. Kramer and B. Chen, “A New Outer Bound and the Noisy-Interference Sum–Rate Capacity for Gaussian Interference Channels,” in IEEE Transactions on Information Theory, vol. 55, no. 2, pp. 689-699, Feb. 2009, doi: 10.1109/TIT.2008.2009793.
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