Dr. Mridula Verma


e-mail : vmridula(at)idrbt(dot)ac(dot)in

Educational Qualifications

  • Ph.D. in Computer Science and Engineering from Indian Institute of Technology (BHU) in 2018
  • M.Tech. in Computer Science and Engineering from Indian Institute of Technology, Roorkee in 2009
  • B.E. in Computer Science and Engineering from Pt. Ravishankar Shukla University, Raipur in 2006.

Research Interests

  • Machine Learning Algorithms
  • Data Mining
  • Data Science
  • Banking Technology

Professional Experience

  • Worked as Assistant Professor in Delhi Technological University from September 2012 to June 2013
  • Worked as Assistant Professor in Galgotia College of Engineering and Technology from July 2010 to December 2011.


  • IIT (BHU) Institute Fellowship for PhD from July 2013 to January 2018
  • GATE Scholarship from August 2007 to July 2009
  • Merit Scholarship from Bhilai Steel Plant, SAIL for Bachelors in Engineering from 2002 to 2006.


  • Association for Computing Machinery
  • The Institution of Engineers (India)

Recent Publications

  • Journals
    • Mridula Verma and K K Shukla (2020), Convergence analysis of accelerated proximal extra-gradient method with applications. Neurocomputing, volume 388, pp 288-300. Impact Factor: 4.438.
    • D R Sahu, Ariana Pitea, Mridula Verma (2020), A New Iteration technique for nonlinear operators as concerns convex programming and feasibility problems. Numerical Algorithms, volume 83, pp 421–449, Impact Factor: 2.064.
    • Mridula Verma and K K Shukla (2017), "A New Accelerated Proximal Technique for Regression with High-dimensional Datasets", Knowledge and Information Systems (KAIS), Vol. 53, Issue 2, pp. 423–438. Acceptance Rate < 19.1%, IF: 2.004
    • Mridula Verma and K K Shukla (2017), "A New Accelerated Proximal Gradient Technique for Regularized Multitask Learning Framework", In Pattern Recognition Letters, Vol. 95, pp. 98-103, 2017, ISSN 0167-8655, IF: 1.995
    • Mridula Verma, D R Sahu and K K Shukla (2017), "VAGA: A Novel Viscosity-based Accelerated Gradient Algorithm: Convergence Analysis and Application to Multitask Regression. Applied Intelligence", IF: 1.904
    • Mridula Verma, S Asmita and K K Shukla (2016), "A Regularized Ensemble of Classifiers for Sensor Drift Compensation". IEEE Sensors Journal, Vol. 16, No. 5, pp. 1310-1318, Acceptance Rate < 30%, Impact Factor: 2.512.
  • Conferences
    • Prayas Jain, Mridula Verma, K K Shukla (2020), Convergence Rate Analysis of Viscosity Approximation based Gradient Algorithms, Proceedings of International Joint Conference on Neural Networks (IJCNN 2020), Glasgow (UK), A Ranked conference by CORE.
    • Mridula Verma and K K Shukla (2017), "Fast Multi-Modal Unified Sparse Representation Learning", Proceedings of ACM International Conference on Multimedia Retrieval, June 2017. (Conference Ranked #1 in the field of Multimedia Retrieval), Bucharest, Romania (acceptance rate: 37%)
    • Mridula Verma, Prayas Jain, K K Shukla (2016), "A New Faster First Order Iterative Scheme for Sparsity-based Multitask Learning", Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, pp. 1603-1608
    • Mridula Verma, K K Shukla (2016), "Performance Comparison of Proximal Methods for Regression with Nonsmooth Regularizers on Real Datasets", Proceedings of Fifth International Conference on Computing, Communications and Informatics (ICACCI-2016), Jaipur. (Acceptance Rate: 23%)
    • Ramashish Gaurav, Mridula Verma, K K Shukla (2016), "Informed Multimodal Latent Subspace Learning via Supervised Matrix Factorization", Proceedings of Tenth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2016), IIT Guwahati. (Acceptance Rate: 22%).

Ph.D. Guidance

  • Hemraj Singh, Salient Object Detection in Video using Deep Learning Approaches, National Institute of Technology, Warangal. In progress since 2020

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