Ph.D. 2019
Joined Fall 2015Now: Senior Research Data Scientist, Bayer R&D
- Ph.D., Mechanical Engineering, Iowa State University, 2019
- M.S., Computer Science, Iowa State University, 2019
- M.S., Mechanical Engineering, Iowa State University, 2017
- B.E., Mechanical Engineering, Jadavpur University, 2015
Sambuddha joined the lab in Fall 2015. His research interests included control systems, cyber-physical systems, dynamics and optimization, and deep and machine learning. His work included anomaly detection in large-scale cyber-physical systems and the use of sparsity-promoting dynamic mode decomposition to extract characteristic features and dynamics of complex fluid flows (e.g., combustion flames) from snapshot data. His doctoral work led to a first-author paper in the Proceedings of the National Academy of Sciences (PNAS). He enjoys playing piano and chess, biking, and shooting.
He graduated from the SCS Lab in 2019 and was a Postdoctoral Associate at the MIT Media Lab, advised by Dr. Pratik Shah. He is now a Senior Research Data Scientist at Bayer R&D.
Research interests: Machine learning, deep learning, computer vision, generative AI, neural network interpretability
Publications with the lab (14)
- Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications — Plant Phenomics (2021)
- Encoding Invariances in Deep Generative Models — arXiv preprint arXiv:1906.01626 (2019)
- Generative models for solving nonlinear partial differential equations — Proc. of NeurIPS Workshop on ML for Physics (2019)
Show all 14
- Binary 2d morphologies of polymer phase separation: Dataset and python toolbox — (2019)
- Interpretable deep learning for guided microstructure-property explorations in photovoltaics — npj Computational Materials (2019)
- A weakly supervised deep learning framework for sorghum head detection and counting — Plant Phenomics, AAAS (2019)
- An explainable deep machine vision framework for plant stress phenotyping — Proceedings of the National Academy of Sciences (2018)
- NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images — (2018)
- Interpretable Deep Learning applied to Plant Stress Phenotyping — arXiv preprint arXiv:1710.08619 (2017)
- High speed video-based health monitoring using 3D deep learning — Dynamic Data-Driven Application Systems (DDDAS) (2017)
- An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling — Cyber-physical systems (2017)
- Data-driven persistent monitoring of Indoor Air Systems — Iowa State University Digital Repository (Iowa State University), Iowa State University (2016)
- Detection and analysis of combustion instability from hi-speed flame images using dynamic mode decomposition — Dynamic Systems and Control Conference 50695, V001T12A005 (2016)
- An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps — 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS … (2016)
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