Ph.D. 2021
Joined Fall 2017Now: Senior Applied Scientist, Generative AI, Amazon Web Services
- Ph.D., Mechanical Engineering, Iowa State University, 2021
- M.S., Mechanical Engineering, Iowa State University, 2019
- B.E., Mechanical Engineering, Jadavpur University, 2017
Tryambak joined Dr. Soumik Sarkar's lab in Fall 2017. His research focused on designing deep learning algorithms for large-scale image/video data, multivariate time-series data, and text data, with applications in healthcare, agriculture, vision, and cyber-physical systems. He also pursued a concurrent master's degree in Computer Science. In summer 2019, as an intern in the Machine Learning group at Lawrence Livermore National Laboratory (LLNL), he developed deep learning fusion models for clinical prediction tasks using electronic health record datasets. His other interests are story writing, reading, sketching, and playing guitar.
He graduated from the SCS Lab in March 2021 and is currently a Senior Applied Scientist in Generative AI at Amazon Web Services.
Research interests: Generative AI, machine learning, deep learning, cyber-physical systems
Publications with the lab (17)
- Deep Learning and Dynamic Mode Decomposition for Characterizing Combustion Instability — (2026)
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction — Frontiers in Artificial Intelligence (2026)
- An LSTM-based approach to detect transition to lean blowout in swirl-stabilized dump combustion systems — Energy and AI (2024)
Show all 17
- A deep learning approach to detect lean blowout in combustion systems — arXiv preprint arXiv:2208.01871 (2022)
- 3D convolutional selective autoencoder for instability detection in combustion systems — Energy and AI (2021)
- Crop yield prediction integrating genotype and weather variables using deep learning — Plos one (2021)
- Cross-modal virtual sensing for combustion instability monitoring — arXiv preprint arXiv:2110.01659 (2021)
- Spatiotemporal attention for multivariate time series prediction and interpretation — ICASSP 2021-2021 IEEE international conference on acoustics, speech and … (2021)
- Interpreting the impact of weather on crop yield using attention — NeurIPS Workshop on AI for Earth Sciences (2020)
- Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems — Lecture notes in computer science (2020)
- Interpretable Deep Learning for Monitoring Combustion Instability — IFAC-PapersOnLine (2020)
- An explainable framework using deep attention models for sequential data in combustion systems — NeurIPS 2019 workshop on machine learning and the physical sciences. NeurIPS (2019)
- Deep time series attention models for crop yield prediction and insights — NeurIPS Workshop on Machine Learning and the Physical Sciences (2019)
- Deep learning algorithms for detecting combustion instabilities — Dynamics and Control of Energy Systems (2019)
- Characterizing Combustion Instability Using Deep Convolutional Neural Network — (2018)
- Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a rijke tube — (2018)
- Temporal attention and stacked LSTMs for multivariate time series prediction — (2018)
tryambak (@) iastate.eduLinkedInGoogle Scholar