About

Pranav Thombre is a highly skilled Machine Learning Engineer with a passion for deep learning, computer vision, and data science. He is currently pursuing a Master's degree in Computational Data Science from the School of Computer Science at Carnegie Mellon University. Pranav has a strong academic background and holds a Dual Degree in Computer Science and Engineering from the Indian Institute of Technology (ISM) Dhanbad. Pranav has gained extensive experience in the field of Machine Learning through his internships at various prestigious institutions. He is currently working as a Machine Learning Intern at the Institute for Neuro and Bioinformatics at the Universität zu Lübeck. During his internship, he worked with Prof. Thomas Martinetz to design and test various neural network architectures to train a machine to play chess. Pranav's research led to the development of a convolutional neural network architecture with a single hidden layer that achieved the best performance. Prior to this, Pranav worked as a Machine Learning Intern at ETS Montreal, where he designed an adaptive tracking system for facial detection software. He also worked as a Research Intern at the Institute of Data Science at the National University of Singapore, where he devised methods to synthesize synthetic retinal images to increase the accuracy of existing deep learning-based diabetic retinopathy diagnosis methods. Pranav's technical skills include expertise in ML, Data Science, Computer Vision, Deep Learning, SQL, and Microservices. He has a keen interest in research and has published several research papers in the field of Machine Learning. Pranav is an excellent team player and has worked on various projects with cross-functional teams. He is a quick learner and adapts to new technologies and tools with ease. Pranav's academic achievements, coupled with his practical experience, make him a valuable addition to any team. He is a highly motivated and dedicated professional with 2.19 years of relevant experience in the field of Machine Learning.