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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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DR-IRMPC is a risk-constrained infinite-horizon optimal control framework to solve these class of problems in an iterative manner.
PyCM is a multi-class confusion matrix library written in Python that is a proper tool for post-classification model evaluation.
Pymilo is an open source Python package to export pre-trained machine learning models in a transparent way.
Nava is a Python library that allows users to play sound in Python without any dependencies or platform restrictions.
Published in Journal of Open Source Software, 2018
PyCM is a multi-class confusion matrix library written in Python that is a proper tool for post-classification model evaluation.
Recommended citation: S. Haghighi, M. Jasemi, S. Hessabi, and A. Zolanvari, “PyCM: Multiclass confusion matrix library in Python,” Journal of Open Source Software, vol. 3, no. 25, p. 729, 2018. https://joss.theoj.org/papers/10.21105/joss.00729.pdf
Published in 2nd International Congress on Science and Engineering, 2019
This study was aimed to solve the problem of penalty kick goalkeeping for RoboCup Small Size soccer robots using a Q-learning approach.
Recommended citation: A. Zolanvari, M. Shirazi and M. Menhaj, "A q-learning approach for controlling a robotic goalkeeper during penalty procedure," in II International Congress on Science and Engineering, Hamburg-Germany, 2019. https://shorturl.at/mBEOZ
Published in 2022 European Control Conference (ECC), 2022
This paper considers a risk-constrained infinite-horizon optimal control problem and proposes to solve it in an iterative manner. (ECC 2022 Best Student Paper Award Finalist)
Recommended citation: A. Zolanvari and A. Cherukuri, "Data-driven distributionally robust iterative risk-constrained model predictive control," in 2022 European Control Conference (ECC), 2022. https://ieeexplore.ieee.org/abstract/document/9838319
Published in IEEE 61st Conference on Decision and Control (CDC), 2022
This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents.
Recommended citation: A. Cherukuri, A. Zolanvari, G. Banjac and A. R. Hota, "Data-driven distributionally robust optimization over a network via distributed semi-infinite programming," in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022. https://ieeexplore.ieee.org/abstract/document/9992604
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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