Avigyan Sinha

Background

About Avigyan Sinha

ML/Deep Learning engineer focused on Computer Vision NLP Multi-modal analysis and AI-based medical diagnostics. Supervised several teams and collaborated in R&D. Customized and implemented (from scratch) cutting-edge deep learning architectures like Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) LeNet AlexNet VGGNet GoogLeNet (Inception) Residual Network (ResNet) SqueezeNet MobileNet R-CNN Fast R-CNN Faster R-CNN Mask R-CNN Single Shot Detector (SSD) YOLO DenseNet UNet Autoencoder Generative Adversarial Networks (GAN) Word2Vec (SG CBOW) GloVe Transformers Attention Mechanism etc. in projects. Committed to utilizing them for product design/development to build commercially viable cost-effective optimal technological solutions. Published and working on research papers on ML and Deep Learning. I have experience in ML DL research of over seven years. My interest lies in industrial R&D positions in the fields of Deep Learning Computer Vision Audio-Visual Speech Recognition NLP AI-based medical diagnosis & therapeutics and multi-modal analysis. I believe that machine learning holds the key to put an end to human suffering improve the quality of lives and extend our imagination capability beyond physical impediments. Email: avigyan.sinha@vasoptic.com asinha12@alumni.jh.edu avigyan15@gmail.com GitHub: https://github.com/avigyan Google Scholar: https://scholar.google.com/citations?user=I2MYLGcAAAAJ&hl=en