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

Current Research Interests

My current research is in the area of Reinforcement Learning, with emphasis on using stochastic approximation theory as a means to unify various algorithms currently in vogue. More broadly, I am interested in 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. On the applications front, I am interested in applying ideas from machine learning to problems in computational biology with emphasis on cancer.

Recent Publications

Preprints

  • Rajeeva L. Karandikar and M. Vidyasagar, "Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications," PDF
  • Tadipatri Uday Kiran Reddy and M. Vidyasagar, "Convergence of Momentum-Based Heavy Ball Method with Batch Updating and/or Approximate Gradients," PDF
  • Sourav Chatterjee and Mathukumalli Vidyasagar, "Estimating large causal polytree skeletons from small samples," PDF
  • Rajeeva L. Karandikar and M. Vidyasagar, "Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning," PDF

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

Recent Journal Publications (inverse chronological order)

For older publications, please see the tab "publications"

  • Prashant Shivgunde, Sapana Thakare, Sourav Sen, Madhuri Kanitkar, Manindra Agrawal & Mathukumalli Vidyasagar , “COVID-19 Pandemic in Malegaon: SUTRA over the Three Waves,” Indian Journal of Microbiology, 63, 344–351, 2023.
  • Mathukumalli Vidyasagar, "A Tutorial Introduction to Reinforcement Learning," (Invited Survey Paper), SICE Journal of Control, Measurement and System Integration, 16(1), 172–191, 2023. PDF
  • M. Vidyasagar, "Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods," Mathematics of Controls, Signals and Systems 35, 351-374, 2023. PDF
  • Shantanu Prasad Burnwal and Mathukumalli Vidyasagar, "Modified Error Bounds for Matrix Completion and Application to RL," IEEE Control Systems Letters, 6, 1916-1921, 2022. PDF
  • Shantanu Prasad Burnwal, Mathukumalli Vidyasagar and Kaneenika Sinha, "New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs," The Ramanujan Journal, 57, 1043-1069, 2022. 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

COVID-19-Related Material

Research Papers

  • Prashant Shivgunde, Sapana Thakare, Sourav Sen, Madhuri Kanitkar, Manindra Agrawal & Mathukumalli Vidyasagar , “COVID-19 Pandemic in Malegaon: SUTRA over the Three Waves,” Indian Journal of Microbiology, 63, 344–351, 2023.
  • 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

Preprint

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

Slides of Some Recent Talks

  • "Reinforcement Learning via Stochastic Approximation: Part-1", IIT Bombay, 17 February 2023. PDF
  • "Reinforcement Learning via Stochastic Approximation: Part-2", IIT Bombay, 17 February 2023. PDF
  • "Ramanujan Graphs and the Matrix Completion Problem," Sastra Ramanujan Symposium, 21 December 2022. PDF
  • "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
  • "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