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NIM: Modeling and Generation of Simulation Inputs via Generative Neural Network

Published in Winter Simulation Conference, 2020

🎉🎉🎉 Nominated as Finalist for Best Contributed Theoretical Paper

Description: We developed Neural Input Modeling (NIM), a novel framework to model and generate simulation input processes using Generative Neural Networks. This makes input modeling easier for both simulation novice and experts.

Website: Neural Input Modeling

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NIM: Modeling and Generation of Simulation Inputs via Generative Neural Network

Published: December 18, 2020

I gave an oral presentation at Winter Simulation Conference 2020 on the motivation, technical details and experiments for a novel simulation input modeling framework called Neural Input Modeling, which I’m developing at UMass Amherst under the supervision of Professor Peter Haas. Click to watch the talk and learn more details.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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