Pulkit Kumar

I am a senior data scientist at ParallelDots, where I am currently working in computer vision and speech recognition with its applications in market research. I am also a research associate under Dr. Anubha Gupta at IIIT, Delhi where I am exploring the application of computer vision in medical imaging diagnostics.

I did my bachelors at Netaji Subhas Institute of Technology, New Delhi in Information Technology, where I was advised by Dr. Anand Gupta in my Bachelor Thesis Project. During my junior and senior year, I split my time studying at NSIT and interning with the amazing team at ParallelDots.

Email / CV / Google Scholar / LinkedIn

Research

	Prototypical metric transfer learning for continuous speech keyword spotting with limited training data Harhita Seth, Pulkit Kumar*, Muktabh M. Srivastava In submission* A novel few-shot technique of combining prototypical network's loss with the metric loss and using transfer learning to form prototypes of domain specific keywords for their detection in continous speech.
	LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer Simmi Mourya, Sonaal Kant, Pulkit Kumar, Ritu Gupta , Anubha Gupta In submission* A deep learning framework for classifying immature leukemic blasts and normal cells by fusing Discrete Cosine Transform (DCT) domain features extracted via CNN with the Optical Density (OD) space features.
	U-Segnet: Fully convolutional neural network based automated brain tissue segmentation tool Pulkit Kumar , Pravin Nagar , Chetan Arora , Anubha Gupta International Conference on Image Processing (ICIP), 2018 A hybrid of SegNet and U-Net architecture for segmentation of Grey Matter, White Matter and Cerebrospinal Fluid in brain MRI.
	Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs Pulkit Kumar* , Monika Grewal, Muktabh M. Srivastava International Conference Image Analysis and Recognition (ICIAR)*, 2018 Combining boosting and cascading with DenseNets to detect all the pathologies in the Chest X-Ray 8 dataset.
	RADNET: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans Monika Grewal, Muktabh M. Srivastava, Pulkit Kumar* , Srikrishna Varadarajan* International Symposium of Biomedical Imaging (ISBI), 2018 A Deep Learning model combining DenseNets with attention and LSTMs to detect haemorrhage from brain CT scans which matches the accuracy of senior radiologists.
	Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks Srikrishna Varadarajan, Muktabh M. Srivastava, Monika Grewal, Pulkit Kumar** Poster in International Symposium of Biomedical Imaging (ISBI), 2018 Used multi-context feature embeddings from a pre-trained VGG model with nearest neighbours to train RelationNets for anatomic labelling in brain CT Scans.
	A Big Data Analysis Framework Using Apache Spark and Deep Learning Anand Gupta, Hardeo Thakur, Ritvik Shrivastava, Pulkit Kumar, Sreyashi Nag International Conference of Data Mining (ICDM) workshop on Data Science and Big Data Analytics (DSBDA), 2017 A cascaded approach to predict the approval of H-1B visas on factors such as qualification, salary, location of job etc.

He is a great guy.