Nathalie Risso

About

Nathalie Risso is a tenure-track assistant professor at the School of Mining Engineering and Mineral Resources at the University of Arizona. She directs the Mine Automation and Autonomous Systems Laboratory, focusing on integrating automation within a cyber-physical systems approach to enable autonomous behavior in safety-critical environments such as mining applications. Her research emphasizes developing solutions for mining in harsh, low-connectivity environments where safety, robustness, and autonomous systems collaboration are key requirements. Risso has received the 2023 SME Freeport-McMoRan Inc. Career Development Grant to advance research related to AI-driven cyber-physical systems for mining. She has extensive consulting experience in automation and autonomous systems for the mining and energy industries and has led multiple research and development initiatives in AI, machine learning, and advanced control systems.

Research topics

• Waste management
• Mining engineering
• Petroleum engineering
• Geology
• Engineering

Selected publications

• Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

  International Journal of Coal Geology · 2024 · 62 citations

  PublisherDOI

• Machine Learning Algorithms for Semi-Autogenous Grinding Mill Operational Regions’ Identification

  Minerals · 2023-10-25 · 10 citations

  articleOpen access

  Energy consumption represents a significant operating expense in the mining and minerals industry. Grinding accounts for more than half of the mining sector’s total energy usage, where the semi-autogenous grinding (SAG) circuits are one of the main components. The implementation of control and automation strategies that can achieve production objectives along with energy efficiency is a common goal in concentrator plants. However, designing such controls requires a proper understanding of proces…

  PublisherOA PDFDOI

• Error reduction in long-term mine planning estimates using deep learning models

  Expert Systems with Applications · 2023-01-02 · 10 citations

  articleSenior author
  PublisherDOI

• Application of Machine Learning in Mine Safety: A State-of-The-Art Review

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  reviewOpen access
  PublisherDOI

• MPC-based Traction Control for Electric Vehicles

  2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA) · 2022-10-24 · 3 citations

  article

  The car technology shift to powertrain electrification gives plenty of new ways to improve safety, one of such is the regenerative brake sustained by the capability of the instant torque that an electric motor can produce. However, traction control is something that needs to be discussed and study in order to maximize safety, acceleration, and braking for electric vehicles. This paper examines and simulates a torque control technique, ensuring a secure and safe acceleration, and braking procedur…

  PublisherDOI

Frequent coauthors

• Angelina Anani

  Rogers (United States)

  14 shared

• Jaime Rohten

  University of Bío-Bío

  11 shared

• Moe Momayez

  Arizona Geological Survey

  9 shared

• Sefiu O. Adewuyi

  University of Arizona

  9 shared

• Pedro G. Campos

  9 shared

• Pedro Lopez

  Arizona Geological Survey

  9 shared

• Vladimir Esparza

  6 shared

• Ricardo G. Sanfelice

  University of California, Santa Cruz

  6 shared

Labs

• Mine Automation and Autonomous Systems LaboratoryPI

Awards & honors

• SME Freeport-McMoRan Inc. Career Development Grant (2023)
• Life-Cycle Management of Tailings Facilities, The Tailings C…
• International Space University Executive Space Course (2024)
• International Society of Engineering Pedagogy (IGIP) Interna…
• Stanford University Energy Innovation and Emerging Technolog…

Similar researchers at University of Arizona

• Xiquan Dongh-52
• William K.Smithh-50
• Steven Archerh-45