Assistant Professor at Department of Computer Science and Engineering, IIT Jodhpur
Jodhpur, Rajasthan, India
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Summary
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Senior
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Hardik Jain is an assistant professor in computer science with a PhD in 3D computer vision from Technische Universität Berlin and roughly a decade of experience spanning academic research and industrial R&D. His work bridges deep learning for 3D mesh parameterization and practical data-science solutions for rail transport and medical imaging, including contributions to the widely used CGAL library on surface mesh parameterization. He has driven applied projects from surgical navigation and motion-compensated face registration to automated symbol detection for railway engineering, demonstrating a knack for turning complex vision algorithms into deployed systems. Known for combining rigorous theoretical insight (Magna cum Laude PhD) with hands-on engineering, he is now shaping the next generation of researchers and practitioners at IIT Jodhpur.
10 years of coding experience
6 years of employment as a software developer
Bachelor’s Degree, Electronics and Instrumentation Engineering, First Division with Honours, Bachelor’s Degree, Electronics and Instrumentation Engineering, First Division with Honours at Shri G S Institute of Technology & Science
Higher Secondary, Higher Secondary at B K Birla Centre for Education Pune, India
Indian Institute of Technology Roorkee
Doctor of Philosophy - PhD, Computer Vision, Magna cum Laude, Doctor of Philosophy - PhD, Computer Vision, Magna cum Laude at Technische Universität Berlin
High School, Physics Chemistry Mathematics, High School, Physics Chemistry Mathematics at Lions Convent Higher Secondary School Sendhwa, India
Contributions:7 commits, 1 PR, 1 comment in 6 months
Contributions summary:Hardik contributed to the CGAL library by implementing and modifying algorithms related to surface mesh parameterization. Their work involved adding new parameterization schemes, such as the iterative authalic parameterization and comparing them with existing methods. The changes also included modifying the cotangent weights initialization and other related improvements. Additionally, the user added a comparison example demonstrating different parameterization methods.
Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface
Contributions:15 commits, 1 PR, 11 pushes in 2 years 2 months
parameterizationsurfacepythonsymmetric
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