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

• Integrating Artificial Intelligence into Ventilation on Demand: Current Practice and Future Promises

  Sensors · 2026-02-05

  articleOpen access

  The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human-cyber-physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments.

  PublisherDOI

• Mine Planning Adaptations for the Integration of Autonomous Haulage Systems

  Preprints.org · 2026-02-26

  preprintOpen access

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherOA PDFDOI

• MINDS: A Modular Multi-Agent Decision-Support Framework for Dynamic Strategic Mine Planning

  Mining · 2026-04-02

  articleOpen accessCorresponding

  Strategic Mine Planning (SMP) creates the long-term economic baseline for mining operations, yet economic variability necessitates Dynamic Mine Planning (DMP) to rapidly stress-test those financial assumptions. Currently, this capability is hindered by fragmented software ecosystems that require manual data handoffs, slowing iteration and breaking the audit trail between market data and valuation models. While Generative AI affords an opportunity to automate these workflows, its adoption in the mining industry is stalled by concerns over data quality and the risk of uncritical acceptance of automated outputs. Addressing these challenges, this paper describes the Mine Intelligence and Decision Support (MINDS) framework. We present MINDS as a modular reference architecture that uses Large Language Model (LLM) agents to orchestrate the economic evaluation process while maintaining strict engineering oversight. The system integrates a conversational interface with a multi-agent assessment layer that acts as an adversarial review, assessing price assumptions against market intelligence before generating economic valuation scenarios. A proof-of-concept using the Marvin copper benchmark evaluates the framework, demonstrating automated request-to-report orchestration, execution stability with an average debate latency of 10.69 s and a transparent decision audit trail. These findings show that MINDS can systematize economic scenario analysis without sacrificing the governance and verification required for definitive feasibility studies.

  PublisherOA PDFDOI

• Surface Mine Planning Adaptations for the Integration of Autonomous Haulage Systems: A Review

  Preprints.org · 2026-05-19

  preprintOpen access

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherDOI

• Cluster-Based Machine Learning Modeling for Particle Size Variability in SAG Mill Feed

  Mining Metallurgy & Exploration · 2026-04-22

  articleCorresponding
  PublisherDOI

• A Proposed Concept for Classifying Uniaxial Compressive Strength (Ucs) from Swir Hyperspectral Data

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A proposed concept for classifying uniaxial compressive strength (UCS) from SWIR hyperspectral data

  Engineering Geology · 2025-08-19 · 2 citations

  articleOpen access

  With the development of lower-cost and portable spectral imagers and spectral radiometers, the question arises: Can hyperspectral image data be used to estimate the Unconfined Compressive Strength (UCS) of rock? Reflectance, emissivity, absorption, and transmission are fundamental properties of rock and minerals. This study focuses on correlating data from non-destructive hyperspectral images and destructive test methods. Hyperspectral images of 32 altered granite samples were acquired in the Shortwave Infrared (SWIR). The reflectance from the 1000 to 2500 nm range of core samples was analyzed. The primary objective of this study is to identify key spectral features that correlate with rock strength and classify samples into ISRM strength categories for weak, moderately strong, and strong rock. The methodology encompasses data preprocessing, feature extraction based on deviations from the mean spectral response, and statistical analysis to identify significant spectral components. The k-Nearest Neighbor (kNN) classifier demonstrated reliable performance for moderately strong and strong rock categories, achieving an overall accuracy of 90 %. This paper outlines the experimental procedure, machine learning analysis methods, and a recommended path forward for further developing this technique. The ultimate goal is to develop additional methods for quantifying UCS from hyperspectral images of both surface and drill core data, utilizing International Society of Rock Mechanics (ISRM) classification guidelines. • A method to classify granite rock strength from SWIR Hyperspectral data is presented. • No single spectral or mineralogical feature can reliably classify rock strength. • SWIR spectra detect alteration minerals like clays to help identify weak zones. • Combining spectral features enables rock strength classification with 90 % accuracy. • Sampling and ML classification trials are outlined.

  PublisherDOI

• Mine2Twin: a Synergistic Industry-Academia Collaboration to Improve Engineering Skills for Industry 5.0

  2025-04-22 · 1 citations

  article1st authorCorresponding

  Industry 5.0 is reshaping workforce requirements by emphasizing the integration of human creativity with advanced technologies, such as automation and artificial intelligence. Teaching engineering skills within the context of Industry 5.0 and large industrial applications has become increasingly complex. Active learning and hands-on experimentation are critical to improving understanding and skill development in this rapidly evolving environment. This paper presents the development of a set of scenario-based learning experiences using an industry-validated digital twin to address these challenges, specifically in the context of mineral processing. While based on a small test group, preliminary results indicate that these scenario-based experiences significantly improve students' understanding of complex mineral processing concepts. Additional testing and validation is still ongoing. Feedback from testers suggests that the tool could also enhance other coursework, research, and capstone projects by offering a practical, interactive approach to learning. The study builds on industry-accepted tools to improve engineering curricula by integrating real-world applications into academic programs. Furthermore, it provides a model for incorporating advanced technologies, such as digital twins, into engineering coursework, aligning with the needs of Industry 5.0.

  PublisherDOI

• Blending Characterization for Effective Management in Mining Operations

  Minerals · 2025-08-22 · 2 citations

  articleOpen accessCorresponding

  Ore blending plays a critical role in ensuring feed consistency and optimizing downstream processes in the mining industry. Despite its importance, effective blending remains challenging due to ore variability and operational constraints. This review focuses exclusively on modern, data-driven blending methodologies, with particular emphasis on the application of data science and machine learning (ML) in predicting key process variables and supporting real-time decision-making. It discusses core challenges such as data quality, feature engineering, and model generalization, alongside enabling technologies including sensor integration, automation platforms, and real-time data acquisition systems. By consolidating the recent literature and highlighting emerging trends, this work outlines future directions for advancing intelligent blending systems and underscores the importance of standardized, high-quality data in the development of robust digital solutions for mineral processing.

  PublisherOA PDFDOI

• Mining the Text: Automating Safety Insights from Mining Accident Reports

  2025-05-23

  article

  Mining safety remains a critical focus globally, with continuous efforts to reduce accidents and enhance safety standards. A key component of these efforts involves analyzing mining accident reports—comprehensive textual resources that provide valuable insights into the root causes and contributing factors of accidents. This study applies tools from Natural Language Processing and Topic Modeling to the analysis of fatality reports obtained from the United States Mine Safety and Health Administration (MSHA). This work seeks to automate the identification of latent themes and patterns within accident reports, streamlining the process for enhanced safety insights. A Latent Dirichlet Allocation model, optimized using Term Frequency-Inverse Document Frequency (TF-IDF) weighting and the Coherence Value metric were used to determine the optimal number of topics. Extracted topic keywords were processed using ChatGPT-4o, a general large language model (LLM), to generate coherent and interpretable topic summaries. The results demonstrate the potential of combining topic modeling with LLMs to streamline mining accident analysis, reducing manual review efforts while extracting actionable safety insights. However, findings also highlight challenges in language ambiguity, domain-specific terminology, and misclassification, reinforcing the need for tools that consider domain adaptation. This study underscores the importance of responsible AI deployment in occupational safety, ensuring that AI-driven solutions complement human decision-making rather than replace it.

  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…