- 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.
- 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)
- Hemraj Singh, Mridula Verma and Ramalingaswamy Cheruku (2023), “Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild”. IEEE Transactions on Instrumentation and Measurement (Accepted). Impact Factor: 5.6.
- 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.