I am working under Dr. Jean-Christophe Pesquet on stability and verification of neural networks used in open-loop and closed-loop control of dynamical systems. As part of my Ph.D., I am also working on modeling the dynamics of heavy electrical motors using neural networks under the guidance of Dr. Marc Castella, Nicolas Henwood, and Al-Kassem Jebai. I am also associated with Granular AI where I work on leveraging satellite imagery and geospatial algorithms for socio-economic problems. In INRIA OPIS, I work on AI for Radiology with Dr. Émilie Chouzenoux and Dr. Nathalie Lassau from Institut Gustave Roussy.
I was a Junior Research Fellow at Computer Vision and Machine Learning Lab (IIIT Delhi) under Dr. Chetan Arora where I worked on egocentric video understanding. I have worked with Dr. Angshul Majumdar on applications of generative models to collaborative filtering and energy management systems. I have also worked with Dr. P.B. Sujit on reinforcement learning for control problems. I completed M. Tech. in 2017 from IIIT-Delhi.
S. Verma, R. Verma, and P.B. Sujit. MAPEL: Multi-Agent Pursuer-Evader Learning using Situation Report, (Accepted in IJCNN 2019) [PDF].
S. Verma, S. Singh, and A. Majumdar. Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring, (Accepted in ICASSP 2019) [PDF].
S. Verma. Action Recognition in Egocentric Videos, (M. Tech. Thesis 2017) [IIITD archive].
Note: I write blogs to learn and practice mathematical concepts, technical writing, and aesthetic presentation. These posts should never be taken as a primary source of information. For a clear and accurate understanding, please visit citations.
Why do bi-temporal data fail in modeling urban sprawl? Introducing a large multi-date change detection dataset. Deriving urban growth index from Sentinel-2 multi-date images. Exploring U-Net, RNN, QRNN to handle multi-date input.
ESA's SAFE and DigitalGlobe's FLAME provide solutions to atmospheric correction and base layer matching problems respectively. Both are proprietary products and require intensive computing resources and are not suitable for processing large maps in limited time. Can GANs solve atmospheric correction, tile blending, and cloud correction problem?
Unsupervised feature learning from satellite images using cross-channel encoder based on split-brain architecture. Feature learning using hyperspectral images, constructing thermal bands from RGB and vice-versa. Normalized cuts on learned features for semantic segmentation.
Optimal solution using MILP for ATC scheduling and multi-agent track-n-tag game. Using Branch and Bound method to select "equidistant" sentinel-2 capture dates for large scale inference.
Lipschitz constrained training of DNN for bounded control of electric motors.
Activity and action recognition in first-person videos. Two datasets of short and long activities in Plumbing and PC Assembling domain respectively. Baselines of two-stream CNN, I3D, SlowFast, and C3D networks.