Research

Current Research Activities of
Mathukumalli Vidyasagar
Fellow of The Royal Society
SERB National Science Chair &
Distinguished Professor
Indian Institute of Technology Hyderabad


Notes

I specifically chose the above photo, crow's feet and all, just to highlight the point that research should be fun!

This page contains a description of my current research interests, recent papers and papers awaiting publication, and recent seminar presentations. Reprints of scientific papers that are already published can be found under "publications" while other writings can be found under (what else?) "other writings."

Contents

Current research interests
COVID-19-Related Material
Recent Publications
Slides of some recent talks

Current Research Interests

My current research interests are in the broad areas of machine learning, systems and control theory, and their applications. Until recently I was exploring the area of compressed sensing, that is, determining high-dimensional but low-complexity objects from a small number of measurements, and the intersection between compressed sensing and control theory. I am now returning my earlier research area of machine learning using statistical methods, with emphasis on reinforcement and deep learning. On the applications front, I am interested in applying ideas from machine learning to problems in computational biology with emphasis on cancer.

COVID-19-Related Material

Research Papers

  • Manindra Agrawal, Madhuri Kanitkar and Mathukumalli Vidyasagar, "SUTRA: An Approach to Modelling Pandemics with Asymptomatic Patients,and Applications to COVID-19," PDF
  • Manindra Agrawal, Madhuri Kanitkar and M. Vidyasagar, "Modelling the spread of the SARS-CoV-2 pandemic - Impact of lockdowns & interventions," Indian Journal of Medical Research PDF
  • Shaurya Kaushal, Abhineet Singh Rajput, Soumyadeep Bhattacharya, M. Vidyasagar, Aloke Kumar, Meher K. Prakash , and Santosh Ansumali, "Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model", PLoS One PDF
  • Santosh Ansumali, Shaurya Kaushal, Aloke Kumar, Meher K. Prakash and M. Vidyasagar, "Modelling a Pandemic with Asymptomatic Patients, Impact of Lockdown and Herd Immunity, With Applications to SARS-CoV-2," Annual Reviews in Control 50, 432-447, 2020. PDF

Talks, Slides, etc.

  • "Mathematical Aspects of Modelling the COVID-19 Pandemic}, vrtual talk for IISER Pune and TIFR Mumbai, 20 November 2020. PDF Youtube Link
  • Video recording of the Press Meet regarding COVID-19 report Video
  • Presentation of the COVID-19 India National Supermodel Committee PDF
  • Popular article on the findings of the COVID-19 India National Supermodel Committee PDF
  • COVID-19 National Super Model Committee, "Indian Supermodel for Covid-19 Pandemic" PDF

Recent Publications

Books

  • Mark W. Spong, Seth Hutchinson and M. Vidyasagar, "Robot Modeling and Control, 2nd Edition," John Wiley, 2020. Link
  • M. Vidyasagar, "An Introduction to Compressed Sensing," SIAM, Philadelphia, 2020. Link

Preprints

  • Manindra Agrawal, Madhuri Kanitkar and Mathukumalli Vidyasagar, "SUTRA: An Approach to Modelling Pandemics with Asymptomatic Patients,and Applications to COVID-19," PDF

Recent Journal Publications (inverse chronological order)

  • Shantanu Prasad Burnwal, Mathukumalli Vidyasagar and Kaneenika Sinha, "New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs," to appear in The Ramanujan Journal PDF
  • Manindra Agrawal, Madhuri Kanitkar and M. Vidyasagar, "Modelling the spread of the SARS-CoV-2 pandemic - Impact of lockdowns & interventions," Indian Journal of Medical Research, 153, 175-181, January & February 2021. PDF
  • Shaurya Kaushal, Abhineet Singh Rajput, Soumyadeep Bhattacharya, M. Vidyasagar, Aloke Kumar, Meher K. Prakash , and Santosh Ansumali, "Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model", PLoS One PDF
  • Santosh Ansumali, Shaurya Kaushal, Aloke Kumar, Meher K. Prakash and M. Vidyasagar, "Modelling a Pandemic with Asymptomatic Patients, Impact of Lockdown and Herd Immunity, With Applications to SARS-CoV-2," Annual Reviews in Control 50, 432-447, 2020. PDF
  • Shantanu Prasad Burnwal and Mathukumalli Vidyasagar, "Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery,” IEEE Transactions on Signal Processing 68, 3834-3848, 2020. PDF
  • Mahsa Lotfi and Mathukumalli Vidyasagar, "Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions," IEEE Transactions on Signal Processing 68, 3008-3021, 2020. PDF
  • Masaaki Nagahara, Debasish Chatterjee, Niharika Challapalli and Mathukumalli Vidyasagar, "CLOT Norm Minimization for Continuous Hands-off Control," to appear in Automatica PDF
  • Shashank Ranjan and Mathukumalli Vidyasagar, "Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsity," IEEE Transactions on Signal Processing, 67(11), 2854-2867, June 1, 2019. PDF
  • Mehmet Eren Ahsen and and Mathukumalli Vidyasagar, "An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory," Journal of Machine Learning Research, 20, 1-23, 2019. PDF
  • Nitin Singh, Mehmet Eren Ahsen, Niharika Challapalli, Hyun-Seok Kim, Michael A. White and M. Vidyasagar, "Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer," (Invited Paper), IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 4(3), 123-139, September 2018. PDF
  • Mahsa Lotfi and Mathukumalli Vidyasagar, "A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices," IEEE Transactions on Signal Processing, 66(15), 4079-4089, August 1, 2018. PDF
  • Mathukumalli Vidyasagar, "Machine learning methods in the computational biology of cancer," Annual Reviews in Control, 43, 107-127, 2017. PDF
  • Mehmet Eren Ahsen, Niharika Challapalli and Mathukumalli Vidyasagar, "Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect," Journal of Machine Learning Research, 18, 1-24, July 2017. PDF
  • Mehmet Eren Ahsen et al., "Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer," BMC Genomics, 18(Suppl 3), No. 233, 1-12, March 2017. PDF
  • M. Eren Ahsen and M. Vidyasagar, "Error bounds for compressed sensing algorithms with group sparsity: A unified approach," Applied and Computational Harmonic Analysis, 43, 212-232, 2017. PDF
  • Nitin Singh and M. Vidyasagar, "bLARS: An Algorithm to Infer Gene Regulatory Networks," IEEE Transactions on Computational Biology and Bioinformatics, 13(2), 301-314, March-April 2016. PDF
  • Burook Misganaw et al., "Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer," Cancer Informatics, 14 (Suppl5), 45-55, March 2016. PDF
  • Burook Misganaw and M. Vidyasagar, "Exploiting Ordinal Class Structure in Multiclass Classification: Application to Ovarian Cancer," IEEE Life Sciences Letters, 1(1), 1-4, 2015. PDF
  • M. Vidyasagar, \Identifying predictive features in drug response using machine learning: Opportunities and challenges," Annual Review of Pharmacology and Toxicology , 55(1), 1-29, 2015.
  • M. Vidyasagar, "Machine learning methods in the computational biology of cancer," Proceedings of the Royal Society, Part A, 2014, PDF
  • M. Vidyasagar, "An elementary derivation of the large deviation rate function for finite state Markov processes," Asian Journal of Control, 16(1), 1-19, January 2014. PDF
  • M. Eren Ahsen and M. Vidyasagar, "Mixing coefficients between discrete and real random variables: Computation and properties", IEEE Transactions on Automatic Control, 59(1), 34-47, January 2014. PDF
  • M. Vidyasagar, "A metric between probability distributions on finite sets of different cardinalities and applications to order reduction", IEEE Transactions on Automatic Control, 57(10), 2464-2477, October 2012. PDF
    For a longer version with more details, see the arXiv version
  • M. Vidyasagar, "Probabilistic methods in cancer biology", European Journal of Control, 17(5-6), 483-511, September - December 2011. PDF
  • M. Vidyasagar, "The complete realization problem for hidden Markov models: A survey and some new results", Mathematics of Control, Signals and Systems, 23(1), 1-65, 2011. PDF

Slides of Some Recent Talks

  • "Mathematical Aspects of Modelling the COVID-19 Pandemic}, vrtual talk for IISER Pune and TIFR Mumbai, 20 November 2020. PDF Youtube Link
  • "Machine Learning Methods in Computational Cancer Biology", talk for a biology audience, Institute of Microbial Technology, 10 January 2020. PDF
  • "Ramanujan Graphs and the Matrix Completion Problem," IISER Pune, 30 August 2019. PDF
  • "Machine Learning Methods in Computational Cancer Biology", talk for an engineering audience, Indian Institute of Technology Guwahati, 17 March 2019. PDF
  • "An Introduction to Compressed Sensing, Part-I: Vector Recovery", Indian Insttute of Technology Guwahati, 17 March 2019. PDF
  • "An Introduction to Compressed Sensing, Part-II: Matrix Recovery", Indian Insttute of Technology Guwahati, 17 March 2019. PDF
  • "India at 70", talk on the occasion of the 70th anniversary of Indian Independence, University of Illinois, 19 October 2017. PDF