Naira Hovakimyan

About

Naira Hovakimyan is the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), where she also serves as the Director of the AVIATE Center. She holds a Ph.D. in Physics and Mathematics from the Russian Academy of Sciences and an M.S. in Applied Mathematics from Yerevan State University. Her research focuses on machine learning, human-robot interaction for elderly care, safety-critical systems across aerospace, electrical, mechanical, biomedical, and petroleum engineering, multi-vehicle unmanned systems, cyber-physical systems, energy systems, human-robotic space exploration, control of anesthesia, and control in oil production. She has co-authored two books, holds thirteen patents, and has published over 500 refereed articles. Her distinguished contributions have earned her numerous awards, including the AIAA Mechanics and Control of Flight Award, SWE Achievement Award, IEEE CSS Award for Technical Excellence in Aerospace Controls, and the AIAA Pendray Aerospace Literature Award. She is a Fellow of AIAA, IEEE, ASME, and a senior member of NAI. Additionally, she is the cofounder and chief scientist of Intelinair. Her work has been featured in prominent media outlets such as The New York Times, Fox TV, and CNBC.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Machine Learning
• Data Mining
• Computer Security
• Geography
• Mathematics
• Engineering
• Distributed computing

Selected publications

• Modeling yield response to crop management using convolutional neural networks

  Computers and Electronics in Agriculture · 2020 · 98 citations

  Predicting crop yield response to management and environmental variables is a crucial step towards nutrient management optimization. With the increase in the amount of data generated by agricultural machinery, more sophisticated models are necessary to get full advantage of such data. In this work, we propose a Convolutional Neural Network (CNN) to capture relevant spatial structures of different attributes and combine them to model yield response to nutrient and seed rate management. Nine on-fa…

  DOI

• Novel Stealthy Attack and Defense Strategies for Networked Control Systems

  IEEE Transactions on Automatic Control · 2020 · 60 citations

  Senior authorCorresponding

  This article studies novel attack and defense strategies, based on a class of stealthy attacks, namely the zero-dynamics attack (ZDA), for multiagent control systems. ZDA poses a formidable security challenge since its attack signal is hidden in the null space of the state-space representation of the control system and hence it can evade conventional detection methods. An intuitive defense strategy builds on changing the aforementioned representation via switching through a set of carefully craf…

  DOI

• ℒ₁-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

  2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2020 · 46 citations

  This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamic…

  PublisherDOI

• L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

  arXiv (Cornell University) · 2020 · 21 citations

  This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and L1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the L1 adaptive controller robustifies the archit…

  PublisherOA PDFDOI

• Real-Time Linear MPC for Quadrotors on SE(3): An Analytical Koopman-Based Realization

  IEEE Robotics and Automation Letters · 2025-10-27 · 3 citations

  article

  This letter presents an analytical linear parameter-varying (LPV) representation of quadrotor dynamics utilizing Koopman theory, facilitating computationally efficient linear model predictive control (LMPC) for real-time trajectory tracking. By leveraging carefully designed Koopman observables, the proposed approach enables a compact lifted-space evolution that mitigates the curse of dimensionality while preserving the nonlinear characteristics of the system. Although model predictive control (M…

  PublisherDOI

Recent grants

• EAGER: Human centered robotic system design

  NSF · $300k · 2015–2018

• Distributionally Robust Adaptive Control: Enabling Safe and Robust Reinforcement Learning

  NSF · $375k · 2022–2026

• CPS: Medium: Collaborative Research: Against Coordinated Cyber and Physical Attacks: Unified Theory and Technologies

  NSF · $700k · 2017–2021

Frequent coauthors

• Chengyu Cao

  University of Connecticut

  119 shared

• Enric Xargay

  University of Michigan–Ann Arbor

  71 shared

• Isaac Kaminer

  Naval Postgraduate School

  54 shared

• Venanzio Cichella

  University of Iowa

  50 shared

• Petros G. Voulgaris

  49 shared

• Eugene Lavretsky

  46 shared

• Anthony Calise

  40 shared

• Aditya Gahlawat

  University of Illinois Urbana-Champaign

  39 shared

Awards & honors

• AIAA Mechanics and Control of Flight Award (2011)
• SWE Achievement Award (2015)
• IEEE CSS Award for Technical Excellence in Aerospace Control…
• AIAA Pendray Aerospace Literature Award (2019)
• Humboldt Prize for lifetime achievements (2014)

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Kaiyu Guanh-86
• ChengXiang Zhaih-83