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There is a unique opportunity in Pakistan for using AI in health care. In 2017, a competition hosted by Kaggle called Data Science Bowl 2017 (DSB17) was held, which released, for the first time, a dataset of thoracic CT scans with cancer annotations. This provides an opportunit for researchers to develop AI systems that can learn to predict cancer&

The initial goal of this project was to develop a system which could recognize faces in still images. The scope was further extended to the development of advance level application (Attendance System). We developed a web based class attendance system using one shot for identification while learning on few/single shot. Additional features include phone based imaging&.

Chemical and structural analysis of various substance is the primary means to reveal their properties in numerous important applications. Chemical analysis of soil reveals its chemical nature and hence points to appropriate fertilizer plan to address lack of particular chemicals and helps retain its fertility&

Digital computation is deeply ingrained in modern signal processing, and an efficient analogue-to-digital conversion is of fundamental importance. This work proposes novel sampling schemes for the efficient conversion from an analog-to-digital representation of a sparse&.

Modulation is prevalently used in signal processing and communications to, for example, effectively use available spectral bands by multiplexing signals in different frequency bands. Perhaps more closely related to our framework is code division multiple access, where also a binary waveform is pointwise multiplied with data&

We consider the task of recovering two real or complex L-vectors from phaseless Fourier measurements of their circular convolution. Our method is a novel convex relaxation that is based on a lifted matrix recovery formulation that allows a non trivial convex relaxation of the of the bilinear measurements from convolution. We prove that if the two signals belong to known random subspaces of dimensions K and N, then they can be recovered up to the inherent&

We consider the bilinear inverse problem of recovering two vectors, x and w,  RL from their entrywise product. For the case where the vectors have known signs and belong to known subspaces. An immediate formulation of the inverse problem leads to a non-convex optimization program. We use a geometrical insight to formulate a convex relaxation BranchHull, which is posed in the natural parameter space that does not require&

Imaging inverse problems arise in a myriad of applications in signal processing, and computer vision. In particular, image dehazing, and deblurring is required in astronomy to recover sharp images from blurred, hazy, observed galaxy images; image retrieval and denoising from partial measurements for MRI or CT scan in medical imaging; image super resolution, and inpainting for human face recognition from blurred out, low resolution, and corrupted images&