Professor Mathukumalli Vidyasagar holds the Cecil & Ida Green Chair in Systems Biology Science and is also a member of The Royal Society. His research interests are in the broad area of system and control theory, and its applications. Presently, he is working on:

  • Compressed sensing, including sparse solutions to large under-determined problems, and the intersection between compressed sensing and control theory.
  • Applying ideas from machine learning to problems in computational biology with emphasis on cancer.

Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).

Recent Submissions

  • A Tutorial Introduction Compressed Sensing 

    Vidyasagar, Mathukumalli (IEEE, 2019-01-09)
    In this half-day tutorial, the author will present an introduction to the field of compressed sensing. Compressed sensing refers to the recovery of high-dimensional but low-complexity objects from a small number of linear ...
  • An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory 

    Ahsen, Mehmet Eren; Vidyasagar, Mathukumalli (Microtome Publishing, 2019-01)
    In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik- Chervonenkis (VC-) dimension of the set of half-spaces ...
  • Compressed Sensing with Binary Matrices: New Bounds on the Number of Measurements 

    Lotfi, Mahsa; Vidyasagar, Mathukumalli (Institute of Electrical and Electronics Engineers Inc., 2019-01-09)
    In this paper we study the problem of compressed sensing using binary measurement matrices. New bounds are derived for the number of measurements that suffice to achieve robust sparse recovery, and the number of measurements ...
  • Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer 

    Ahsen, Mehmet Eren; Boren, Todd P.; Singh, Nitin K.; Misganaw, Burook; Mutch, David G.; Moore, Kathleen N.; Backes, Floor J.; McCourt, Carolyn K.; Lea, Jayanthi S.; Miller, David S.; White, Michael A.; Vidyasagar, Mathukumalli (2017-03-27)
    Background: Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients ...
  • Exploiting Ordinal Class Structure in Multiclass Classification: Application to Ovarian Cancer 

    Misganaw, Burook; Vidyasagar, Mathukumalli
    In multiclass machine learning problems, one needs to distinguish between the nominal labels that do not have any natural ordering and the ordinal labels that are ordered. Ordinal labels are pervasive in biology, and some ...