Yanting Ma

Yanting Ma
  • Biography

    Yanting's research interests are mainly in algorithm design and analysis for inverse problems arising in computational sensing using statistical inference and optimization techniques. Additionally, she is generally interested in applied probability and convex analysis. Her PhD research focused on algorithmic and theoretical studies of approximate message passing, as well as provably convergent optimization algorithms for nonlinear diffractive imaging. Her postdoctoral work developed principled methods for dead time compensation for single-photon detectors based on Markov chain modeling.

  • Recent News & Events

    •  NEWS   Research on Intelligent Power Amplifier is Cover Story of Microwave Journal
      Date: April 15, 2021
      MERL Contacts: Mouhacine Benosman; Rui Ma; Koon Hoo Teo
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning
      Brief
      • The cover article in the April issue of Microwave Journal features MERL and MELCO's invited paper entitled "A New Frontier for Power Amplifiers Enabled by Machine Learning". Our recent research applying ML for optimizing operating conditions of advanced power amplifier designs is highlighted.

        Since 1958, Microwave Journal has been the leading source for information about RF and Microwave technology, design techniques, news, events and educational information. Microwave Journal reaches 50,000 qualified readers monthly with a print magazine that has a global reach.
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    •  TALK   Prof. Pere Gilabert gave an invited talk at MERL on Machine Learning for Digital Predistortion Linearization of High Efficient Power Amplifier
      Date & Time: Tuesday, February 16, 2021; 11:00-12:00
      Speaker: Prof. Pere Gilabert, Universitat Politecnica de Catalunya, Barcelona, Spain
      MERL Host: Rui Ma
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
      Brief
      • Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. The use of new radio 5G spectrally efficient signals with high peak-to-average power ratios (PAPR) occupying wider bandwidths only aggravates such compromise. When considering wide bandwidth signals, carrier aggregation or multi-band configurations in high efficient transmitter architectures, such as Doherty PAs, load-modulated balanced amplifiers, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be unacceptably high. This has a negative impact in the DPD model extraction/adaptation, because it increases the computational complexity and drives to over-fitting and uncertainty.
        This talk will discuss the use of machine learning techniques for DPD linearization. The use of artificial neural networks (ANNs) for adaptive DPD linearization and approaches to reduce the coefficients adaptation time will be discussed. In addition, an overview on several feature-extraction techniques used to reduce the number of parameters of the DPD linearization system as well as to ensure proper, well-conditioned estimation for related variables will be presented.
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  • MERL Publications

    •  Seidel, S.W., Murray-Bruce, J., Ma, Y., Yu, C., Freeman, W.T., Goyal, V.K., "Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder", IEEE Transactions on Computational Imaging, DOI: 10.1109/​TCI.2020.3037405, Vol. 7, pp. 58-72, December 2020.
      BibTeX
      • @article{Seidel2020dec,
      • author = {Seidel, Sheila, W. and Murray-Bruce, John and Ma, Yanting and Yu, Christopher and Freeman, William, T. and Goyal, Vivek K},
      • title = {Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder},
      • journal = {IEEE Transactions on Computational Imaging},
      • year = 2020,
      • volume = 7,
      • pages = {58--72},
      • month = dec,
      • doi = {10.1109/TCI.2020.3037405}
      • }
    •  Lin, C., Sels, D., Ma, Y., Wang, Y., "Stochastic optimal control formalism for an open quantum system", Physical Review, DOI: 10.1103/​PhysRevA.102.052605, Vol. 102, pp. 052605, December 2020.
      BibTeX TR2020-163 PDF
      • @article{Lin2020dec,
      • author = {Lin, Chungwei and Sels, Dries and Ma, Yanting and Wang, Yebin},
      • title = {Stochastic optimal control formalism for an open quantum system},
      • journal = {Physical Review},
      • year = 2020,
      • volume = 102,
      • pages = 052605,
      • month = dec,
      • doi = {10.1103/PhysRevA.102.052605},
      • url = {https://www.merl.com/publications/TR2020-163}
      • }
    •  Ma, Y., Boufounos, P.T., Mansour, H., Aeron, S., "Multiview Sensing with Unknown Permutations: An Optimal Transport Approach", arXiv, November 2020.
      BibTeX
      • @article{Ma2020nov,
      • author = {Ma, Yanting and Boufounos, Petros T. and Mansour, Hassan and Aeron, Shuchin},
      • title = {Multiview Sensing with Unknown Permutations: An Optimal Transport Approach},
      • journal = {arXiv},
      • year = 2020,
      • month = nov
      • }
    •  Yu, L., Liu, D., Mansour, H., Boufounos, P.T., Ma, Y., "Blind Multi-Spectral Image Pan-Sharpening", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP40776.2020.9053554, April 2020, pp. 1429-1433.
      BibTeX TR2020-047 PDF Video
      • @inproceedings{Yu2020apr,
      • author = {Yu, Lantao and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T. and Ma, Yanting},
      • title = {Blind Multi-Spectral Image Pan-Sharpening},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {1429--1433},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053554},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-047}
      • }
    •  Ma, Y., Lodhi, M.A., Mansour, H., Boufounos, P.T., Liu, D., "Inverse Multiple Scattering With Phaseless Measurements", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP40776.2020.9053430, April 2020, pp. 1519-1523.
      BibTeX TR2020-041 PDF Video
      • @inproceedings{Ma2020apr,
      • author = {Ma, Yanting and Lodhi, Muhammad Asad and Mansour, Hassan and Boufounos, Petros T. and Liu, Dehong},
      • title = {Inverse Multiple Scattering With Phaseless Measurements},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {1519--1523},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053430},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-041}
      • }
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  • Videos

  • MERL Issued Patents

    • Title: "Systems and Methods of Fusing Multi-angle View HD Images Based on Epipolar Geometry and Matrix Completion"
      Inventors: Liu, Dehong; Ma, Yanting; Mansour, Hassan; Kamilov, Ulugbek; Taguchi, Yuichi; Boufounos, Petros T.; Vetro, Anthony
      Patent No.: 10,212,410
      Issue Date: Feb 19, 2019
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