Dr. Mridula Verma
- 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.
- Machine Learning
- Deep Learning
- Federated Learning
- AI/ML Applications in Banking sector
- 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.
- Subrata Das (with Dr Ayon Chakraborty, IIT Madras), “Machine learning assisted sensing in IoT systems”. In progress since 2021.
- Hemraj Singh (with Dr Ramalingaswamy Cheruku, NITW), “Video Salient Object Detection using Lightweight Deep Learning Approaches”. In progress since 2020.
- Zarka Bashir (with Prof C. Krishna Mohan, IIT Hyderabad), “Federated Learning”. In progress since 2021.
- Association for Computing Machinery
- The Institution of Engineers (India)
- 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.