Rhythm V.

As a computer vision research scientist, I have a strong foundation in computer science, mathematics, and statistics, allowing me to develop advanced algorithms and models for image and video analysis. My expertise lies in areas such as image classification, object detection, segmentation, tracking, and recognition. I am proficient in deep learning technologies (PyTorch, Nvidia cuda, cudNN), enabling me to design and implement state-of-the-art neural networks for complex visual tasks. I developed traditional CV and deep learning based systems for environmental monitoring and document text analysis. Notable vision tasks involve: 1) Graph based Neural Networks 2) Data driven methods including object detection, image classification, semantic and instance segmentation. I have a solid understanding of the latest advancements in the field, keeping up with the latest research papers and attending conferences to stay updated on emerging techniques. I am skilled in data preprocessing, augmentation, and annotation techniques, ensuring high-quality datasets for training and evaluation. I have a keen eye for detail, enabling me to identify and address challenges related to noise, occlusion, and varying lighting conditions in visual data. Furthermore, I am experienced in leveraging pre-trained models and transfer learning to expedite development and improve performance. I am adept at fine-tuning models for specific domains, optimizing their architectures and hyperparameters to achieve state-of-the-art results.