M.S. 2016
Now: AI/ML Specialist, Deloitte
- M.S., Mechanical Engineering, Iowa State University, 2016
- B.S., Mechanical Engineering, Iowa State University, 2014
Kin worked on multi-agent systems and applied machine learning to physical engineering problems. His projects covered network topology control in multi-agent proximity networks, deep learning for microfluidic pillar sculpting, low-light image enhancement (LLNet), decision-making in cyber-physical systems, and prognostics and health monitoring. He received the Iowa State University Graduate College Research Excellence Award in 2016. Outside research, he has a passion for creative work, from digital art and music to video editing and programming.
He graduated from the SCS Lab in December 2016 and joined United Technologies Research Center (later Raytheon Technologies Research Center) in East Hartford, CT, where he became a Principal Research Engineer in AI/ML. He is now an AI/ML Specialist at Deloitte.
Research interests: Machine learning, computer vision, low-light image enhancement, anomaly detection, predictive maintenance
Publications with the lab (17)
- Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems — Knowledge-Based Systems (2021)
- A deep learning framework for causal shape transformation — Neural Networks (2018)
- Deep Learning for Engineering Big Data Analytics — Big Data Analytics (2017)
Show all 17
- Data-driven root-cause analysis for distributed system anomalies — (2017)
- Deep learning for flow sculpting: Insights into efficient learning using scientific simulation data — Scientific reports (2017)
- LLNet: A deep autoencoder approach to natural low-light image enhancement — Pattern Recognition (2017)
- Deep Action Sequence Learning for Causal Shape Transformation — arXiv preprint arXiv:1605.05368 (2016)
- Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes — arXiv preprint arXiv:1612.02141 (2016)
- Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video — arXiv preprint arXiv:1603.07839 (2016)
- A Deep 3D Convolutional Neural Network Based Design for Manufacturability Framework — arXiv preprint arXiv:1612.02141 (2016)
- Deep value of information estimators for collaborative human-machine information gathering — 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS … (2016)
- Topology control in mobile sensor networks using information space feedback — (2016)
- Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities — (2016)
- Prognostics of combustion instabilities from hi-speed flame video using a deep convolutional selective autoencoder — International Journal of Prognostics and Health Management (2016)
- Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video. — CoCo@ NIPS (2015)
- Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis — Annual Conference of the PHM Society (2015)
- Hierarchical feature extraction for efficient design of microfluidic flow patterns — Feature Extraction: Modern Questions and Challenges (2015)
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