Nathalie Risso
- Assistant Professor, Mining and Geological Engineering
- Member of the Graduate Faculty
- (520) 621-6063
- Mines And Metallurgy, Rm. 245
- Tucson, AZ 85721
- nrisso@arizona.edu
Biography
Nathalie Risso is an Assistant Professor Mining and Geological Engineering Department at The University of Arizona. She received the B.S. degree in Electronics engineering from Universidad de Concepcion, and the M.S. and Ph.D degrees in Electrical and Computer engineering from the University of Arizona. Her research interests include Cyber-physical systems, Renewable Energy, and Machine Learning. Prof. Risso is a strong advocate for diversity and inclusion, leading several associated initiatives in STEM related fields.
Degrees
- Ph.D. Electrical and Computer Engineering
- University of Arizona, United States
- M.S. Electrical and Computer Engineering
- University of Arizona, United States
- B.S. Electrical Engineering
- Universidad de Concepcion, Concepcion, Chile
Work Experience
- Freelance (2017 - Ongoing)
Licensure & Certification
- International Engineering Educator, International Society of Engineering Pedagogy (IGIP) (2021)
- Energy Innovation and Emerging Technologies, Stanford University (2020)
Interests
Research
Cyber-physical Systems, Automation and Autonomy, Machine Learning, Renewable Energy
Teaching
Electric Circuits, Machine Learning, Computer Vision, Control Systems
Courses
2024-25 Courses
-
Independent Study
MNE 599 (Fall 2024) -
Machine Learning
MNE 559 (Fall 2024) -
Research
MNE 900 (Fall 2024) -
Thesis
MNE 910 (Fall 2024)
2023-24 Courses
-
Intro to Mine Power Systems
MNE 204 (Spring 2024) -
Thesis
MNE 910 (Spring 2024) -
Independent Study
MNE 599 (Fall 2023) -
Machine Learning
MNE 559 (Fall 2023) -
Thesis
MNE 910 (Fall 2023)
2022-23 Courses
-
Independent Study
MNE 699 (Spring 2023) -
Intro to Mine Power Systems
MNE 204 (Spring 2023)
2021-22 Courses
-
Intro to Mine Power Systems
MNE 204 (Spring 2022)
Scholarly Contributions
Journals/Publications
- Banasiak, D., Budzik, T., Gudra, T., Herman, K., Opielinski, K. J., & Risso, N. (2020).
A Study of a Parametric Method for the Snow Reflection Coefficient Estimation Using Air-Coupled Ultrasonic Waves.
. Sensors (Basel, Switzerland), 20(15). doi:10.3390/s20154267More infoIn this paper, a method for estimating snow pressure reflection coefficient based on non-contact ultrasound examination is described. A constant frequency and air-coupled ultrasound pulses were used in this study, which incorporates a parametric method for reflected energy estimation. The experimental part was carried out in situ in the Antarctic, where the snow parameters were measured along with meteorological data. The proposed method represents a promising alternative for estimating the snow-water equivalent, since it uses a parametric approach, which does not require measurements of absolute values for acoustic pressure.
Proceedings Publications
- Werner, J. D., Akbulut, N. B., Heath, G., Risso, M. ., Riley, D., Anani, A., & Tenorio Gutierrez, V. O. (2024, Spring). Preprint 24-069 Outlining a Roadmap for the Deployment of a DIgital Twin System for the San Xavier Mine Laboratory. In 2024 SME MineXchange. SME Annual Meeting, Feb. 25 – Feb 28, 2024, Phoenix, AZ, 4.More infoImplementing a Digital Twin at the San Xavier Mine Laboratory (Sahuarita, AZ), requires a network redesign with a robust architecture. The goal is to create an ecosystem in where all personnel and equipment can be monitored in real-time from the University of Arizona campus, visualizing the site in a digital terrain model. A wireless mesh will help to test robots with autonomous features. Expected outcomes include data retrieval and analytics, the evolution of communications and safety protocols, tele-operation, and an innovated approach for managing the site with new supervision challenges. A timeline with expected commissioning benchmarks is also included. Keywords: Autonomous Equipment, Data Collection, Digital Twin, Internet of Things, Network-based, Systems Integration, Wireless Mesh
- Risso, N., Johnson, A. M., Olson, E. A., & Sprinkle, J. (2017).
Fuzzy control of an autonomous car using a smart phone
. In 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 1-6.More infoThis paper presents preliminary results on the development of a vision-based controller with a smart phone in the loop for autonomous vehicles. Our approach involves the use of the low cost vision sensors available on a smart phone to implement speed control for a lead-follower application, under stop-and-go traffic conditions. The controller uses fuzzy logic to determine commands based on estimated car position and speed with respect to the target vehicle being followed. We define constraints for system parameters associated with the minimum resolution and distance required for the controller to operate efficiently. The proposed design was successfully demonstrated on the University of Arizona CAT Vehicle by having it identify and follow a chosen human-driven lead vehicle. - Nanez, P., Risso, N., Sanfelice, R. G., & Risso, M. N. (2014).
A symbolic simulator for hybrid equations
. In Proceedings of Summer Simulation Conference 2014.More infoIn this paper, the symbolic simulation of hybrid dynamical systems is studied and an algorithm to compute symbolic solutions for such systems is presented. The tasks to perform such simulations are introduced and an algorithm to symbolically calculate a solution to a hybrid system is presented. The symbolic representation allows the proposed simulator to calculate the actual solution to the system. Benefits and drawbacks of symbolic simulation with respect to the numerical approach are presented. These statements are supported and illustrated in several examples throughout the paper.
Case Studies
- Thangavelautham, J., Momayez, M., Gill, G., Tolmachoff, D., Risso, M. ., Brown Requist, K. W., Lopez, P., Olmos, C., Andreu, M., & Tenorio Gutierrez, V. O. (2022. Durability Demonstration Test Plan(p. 15).More infoDuring the production stages of the durability test, the mining cycle consists of the simulated regolith excavation in the extraction point and transportation to the delivery point.