Ph.D. 2020
Joined Fall 2016Now: Graphics Software Engineer, Intel
- Ph.D., Mechanical Engineering (co-major: Computer Engineering), Iowa State University, 2020
- B.Tech., Industrial and Product Design, NIT Rourkela, 2015
Sambit joined the lab in Fall 2016. His research interests included cyber-physical systems, cyber-manufacturing, computational geometry, GPU computing, and visualization. He has a keen interest in playing musical instruments and loves to travel and read.
He graduated from the SCS Lab in July 2020 and is currently a Graphics Software Engineer at Intel.
Research interests: Cyber-physical systems, cyber-manufacturing, computational geometry, GPU computing, visualization
Publications with the lab (10)
- Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models — Computer Aided Geometric Design (2021)
- A Machine Learning Framework for Decision Support in Design and Manufacturing — ASME eBooks (2021)
- Orthogonal distance fields representation for machine-learning based manufacturability analysis — International Design Engineering Technical Conferences and Computers and … (2020)
Show all 10
- Spatiotemporally constrained action space attacks on deep reinforcement learning agents — Proceedings of the AAAI conference on artificial intelligence (2020)
- Multi-Level 3D CNN for Learning Multi-Scale Spatial Features — CVPR 2019 Workshop on Deep Learning for Geometric Shape Understanding (2019)
- Multi-resolution 3D convolutional neural networks for object recognition — arXiv preprint arXiv:1805.12254 (2018)
- Learning localized features in 3D CAD models for manufacturability analysis of drilled holes — Computer Aided Geometric Design (2018)
- A Machine-Learning Framework for Design for Manufacturability — arXiv preprint arXiv:1703.01499 (2017)
- Learning and visualizing localized geometric features using 3d-cnn: An application to manufacturability analysis of drilled holes — NIPS 2017 Symposium on Interpretable Machine Learning (2017)
- Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes — arXiv preprint arXiv:1612.02141 (2016)
sambitg (@) iastate.eduGoogle Scholar