India Science Webinar Series: Introduction to Deep Learning and Applications in Image Processing


Are you a student or a researcher working with large datasets? Do you want to build Deep Learning Models? Join this webinar to explore Deep Learning concepts, use MATLAB Apps for automating your labelling, and generate CUDA code automatically.


  • Create, modify and analyze Deep Learning architectures
  • Automate ground-truth labelling of image, audio and video data
  • Accelerate training on GPUs/cloud platforms

About the Presenters

Shayoni Dutta, PhD

Senior Application Engineer, MathWorks

Shayoni Dutta is a Senior Application Engineer at MathWorks focusing on technical computing. Her core experience lies in computational Biology models and simulation, advanced statistics, machine/deep learning, medical imaging and clinical-trial analytics. 

Prior to joining MathWorks, Shayoni worked as a data scientist at Bayer and before that as an Imaging scientist at Sun Pharma Advanced research center. Parallelly she has served as adjunct faculty for the last 6 years at National Institute of Forensic sciences and Criminology under home Ministry. 

She has a PhD. in Computational Biology from Indian Institute of Technology, Delhi. She has published and reviewed papers in numerous international conferences and journals. 

Praful Pai, PhD

Education Technical Evangelist, MathWorks

Praful works with the Education Team at MathWorks India, where his focus is on collaborating with faculty, researchers, and students to make STEM education engaging and accelerating research in science and engineering. 

He completed his undergraduate studies in Biomedical Engineering from Manipal Institute of Technology, and his MS and PhD from the Department of Electronics & Electrical Communication Engineering at Indian Institute of Technology, Kharagpur. Prior to joining MathWorks, he worked as a Research Scientist with the National Brain Research Centre, Gurgaon on developing an MRI brain template for the Indian population. He is passionate about learning, teaching, and research in multi-disciplinary domains involving instrumentation, signal/image processing, statistics, and machine learning. 

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