AI/Machine Learning researcher/Data Scientist/Software engineer focused on Computer Vision, Image processing, Audio-visual Speech processing, Natural Language processing, Multi-modal analysis, AI based medical diagnostics & therapeutics. Led more than 13 teams in ML/DL, collaborated with many more in R&D. Having 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), YOLOv3, DenseNet, UNet, Autoencoder, Generative Adversarial Networks (GAN), Word2Vec (SG, CBOW), GloVe in projects, I am committed to utilizing them for product design/development, to build commercially viable, cost-effective, optimal technological solutions. My specialties include AI/ML/DL software product development, Algorithm development, Multi-modal analysis, Mathematical analysis, Signal processing. I have completed more than five research projects in deep learning in the past six months and am most fascinated and intrigued by hybrid techniques. Published three research journal papers in this exciting field in two months recently and working on several more currently. I have experience in Deep Learning research for over six 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. My Email: [email protected], [email protected], [email protected], [email protected] My GitHub Profile: ©