Amir Niaraki-Asli

Amir Niaraki-Asli

Position
  • PhD Student
Amir is a Ph.D. student in the Computer Science department with a background in Mechanical Engineering. His expertise lies at the intersection of computer vision and deep learning. Amir has experience applying machine learning to organic data, including anomaly detection in neural cell cultures and plants. His Ph.D. research focuses on 3D scene perception and RL-based robotic motion control for localization and monitoring tasks.

Area of Expertise

  • Computer Vision
  • RL for Robotics
  • Generative AI

Education

  • B.S., Mechanical Engineering, University of Tehran, 2017
  • M.E., Mechanical Engineering, Iowa State University, 2020
  • M.S. Computer Science, Iowa State University, 2024
  • Ph.D., Mechanical Engineering, Iowa State University, 2021

Publications

  1. A. Niaraki, J. Roghair, and A. Jannesari, “Visual exploration and energy-aware path planning via reinforcement learning,” arXiv preprint arXiv:1909.12217, 2021.
  2. J. Roghair, K. Ko, A. E. Niaraki Asli, and A. Jannesari, “A vision based deep reinforcement learning algorithm for UAV obstacle avoidance,” in Intelligent Systems and Applications, vol. 294, LNNS, pp. 115–128, 2021.
  3. A. Niaraki, Energy-aware 3D path planning via reinforcement learning for aerial object detection and mapping, M.S. thesis, Iowa State University, 2024.
  4. A. E. Niaraki Asli, J. Guo, K. R. Williams, P. L. Lai, X. Wang, R. Montazami, and N. N. Hashemi, “Viability of neural cells on 3D printed graphene bioelectronics,” Biosensors, vol. 9, no. 4, p. 112, 2019.
  5. A. E. Niaraki Asli, J. Guo, P. L. Lai, R. Montazami, and N. N. Hashemi, “High-yield production of aqueous graphene for electrohydrodynamic drop-on-demand printing of biocompatible conductive patterns,” Biosensors, vol. 10, no. 1, p. 6, 2020.
  6. M. C. McNamara, A. E. Niaraki Asli, J. Guo, J. Okuzono, R. Montazami, and N. N. Hashemi, “Enhancing the conductivity of cell-laden alginate microfibers with aqueous graphene for neural applications,” Frontiers in Materials, vol. 7, p. 61, 2020.
  7. A. Niaraki, M. C. McNamara, R. Montazami, and N. N. Hashemi, “Graphene microelectrodes for real-time impedance spectroscopy of neural cells,” ACS Applied Bio Materials, vol. 5, no. 1, pp. 113–122, 2022.
  8. A. Niaraki, M. A. Shirsavar, S. S. Aykar, M. Taghavimehr, R. Montazami, and N. N. Hashemi, “Minute-sensitive real-time monitoring of neural cells through printed graphene microelectrodes,” Biosensors and Bioelectronics, vol. 210, p. 114284, 2022.
  9. M. A. Shirsavar, A. Niaraki, and N. N. Hashemi, “Electrochemical characterization of dopamine in neural cells with flexible biosensors,” Journal of Manufacturing Science and Engineering, vol. 144, no. 9, p. 091009, 2022.
  10. V. Nasirian, A. E. Niaraki-Asli, S. Aykar, M. Taghavimehr, R. Montazami, and N. Hashemi, “Capacitance of flexible polymer/graphene microstructures with high mechanical strength,” 3D Printing and Additive Manufacturing, vol. 11, no. 1, pp. 242–250, 2024.
  11. V. Nasirian, A. E. Niaraki-Asli, S. Aykar, M. Taghavimehr, R. Montazami, and N. Hashemi, “Polyethylene glycol wrapped alginate/graphene hollow microfibers as flexible supercapacitors,” Unpublished, 2021.
  12. G. A. Clarke et al., “Advancement of sensor integrated organ-on-chip devices,” Sensors, vol. 21, no. 4, p. 1367, 2021.
  13. M. C. McNamara et al., “Behavior of neural cells post manufacturing and after prolonged encapsulation within conductive graphene-laden alginate microfibers,” Advanced Biology, vol. 5, no. 11, p. 2101026, 2021.
  14. N. Alimoradi et al., “Fabrication of conductive hollow microfibers for encapsulation of astrocyte cells,” bioRxiv, pp. 2022–03, 2022.
  15. A. E. Niaraki Asli, Real-time monitoring of neuronal cells through electrohydrodynamic patterning of flexible graphene microelectrodes, Ph.D. dissertation, Iowa State University, 2021.
  16. M. Kiani, E. Houshfar, A. E. Niaraki Asli, and M. Ashjaee, “Combustion of syngas in intersecting burners using the interferometry method,” Energy & Fuels, vol. 31, no. 9, pp. 10121–10132, 2017.