About

Dr. Rajive Ganguli is the Malcolm McKinnon Endowed Professor in the Department of Mining Engineering at the University of Utah. He has been working on artificial intelligence applications for the mining industry for the last two decades. His research interests include addressing interdisciplinary grand challenges of the mining industry. Dr. Ganguli's office is located in WBB room 302, and he can be reached by phone at 801-585-0958 or via email at rajive.ganguli@utah.edu.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• Sociology
• Engineering
• Mining engineering
• Linguistics
• Economics
• Demographic economics
• Geography
• Medicine
• Geology
• Demography

Selected publications

• BoxRF: A New Machine Learning Algorithm for Grade Estimation

  Applied Sciences · 2025-04-17 · 1 citations

  articleOpen accessCorresponding

  A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inverse distance methods, with the robustness of random forest (RF) methods that come from forming numerous random groups of data. The method was applied to a porphyry copper deposit, and results were compared to various ML methods, including XGBoost (XGB), k-nearest neighbors (KNN), neural nets (NN), and RF. Scikit-learn RF (SRF) performed the best (R2 = 0.696) among the ML methods but underperformed BoxRF (R2 = 0.751). The results were confirmed through a five-fold cross-validation exercise where BoxRF once again outperformed SRF. The box dimensions that performed the best were similar in length to the ranges indicated by variogram modeling, thus demonstrating a link between machine learning and traditional methods. Numerous combinations of hyperparameters performed similarly well, implying the method is robust. The inverse distance method was found to better represent the grade–space relationship in BoxRF than median values. The superiority of BoxRF over SRF in this dataset is encouraging, as it opens the possibility of improving machine learning by incorporating domain knowledge (principles of geology, in this case).

  PublisherOA PDFDOI

• Predictive Summary Model—a Domain-Guided Approach to Generate Informative Summaries

  Mining Metallurgy & Exploration · 2025-08-13

  articleSenior author
  PublisherDOI

• Transforming Uinta Basin Earth Materials for Advanced Products (TUBE-MAP)

  2024-12-31

  reportOpen access

  The objectives of this project were to quantify, assess, and plan to enable the transformation of Uinta Basin earth resources, such as coal, oil shale, resin, rare earth elements, and critical minerals into high value metal, mineral, and carbon-based products. The specific major goals were 1) basinal assessments and initial planning (Task 2), 2) basinal assessment for waste stream reuse with associated plan development (Task 3), 3) basinal strategies development for infrastructure, industries, and business (Task 4), 4) technology assessment, development, and field-testing plan (Task 5), 5) technology innovation center plan (Task 6), and 6) stakeholder outreach and education plan (Task 7).

  PublisherOA PDFDOI

• Bringing Alaska's Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) into Perspective

  2024-12-31

  report

  The final report outlines the outcomes of the Alaska CORE-CM Program, funded by the U.S. Department of Energy under award DE-FE0032050. Led by the University of Alaska Fairbanks and the Alaska Division of Geological and Geophysical Surveys, with assistance from other organizations, the project assessed Alaska's potential for Carbon Ore, Rare Earth Elements, and Critical Minerals (CORE-CM). Leveraging advanced analytical techniques, the project identified high-potential resource basins, evaluated geochemical and satellite data, and conducted targeted field investigations. Findings revealed promising concentrations of critical minerals in legacy samples and newly collected materials. The project also investigated innovative extraction technologies, including BioExtraction and use of supercritical CO2, which show significant promise for sustainable resource recovery. Additionally, the study explored the reuse of waste streams from active mining operations and coal byproducts such as using alkali-activated coal ash to manufacture concrete. Infrastructure and logistical challenges in Alaska’s remote regions are discussed, alongside strategies to establish a Technology Innovation Center aimed at advancing CORE-CM development in Alaska. The report includes actionable insights to support Alaska’s critical role in securing domestic supplies of essential minerals while addressing economic, environmental, and technological challenges.

  PublisherDOI

• Contextual Representation in NLP to Improve Success in Accident Classification of Mine Safety Narratives

  Minerals · 2023-06-03 · 12 citations

  articleOpen accessSenior author

  Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in the mining industry, in the current work, BERT architecture is adapted in developing the MineBERT model to accomplish the classification of accident narratives from the US Mine Safety and Health Administration (MSHA) data set. In the past multi-year research, several machine learning (ML) methods were used by authors to improve classification success rates in nine significant MSHA accident categories. Out of nine, for four major categories (“Type Groups”) and five “narrow groups”, Random Forests (RF) registered 75% and 42% classification success rates, respectively, on average, while keeping the false positives under 5%. Feature-based innovative NLP methods such as accident-specific expert choice vocabulary (ASECV) and similarity score (SS) methods were developed to improve upon the RF success rates. A combination of all these methods (“Stacked” approach) is able to slightly improve success over RF (71%) to 73.28% for the major category “Caught-in”. Homographs in narratives are identified as the major problem that was preventing further success. Their presence was creating ambiguity problems for classification algorithms. Adaptation of BERT effectively solved the problem. When compared to RF, MineBERT implementation improved success rates among major and narrow groups by 13% and 32%, respectively, while keeping the false positives under 1%, which is very significant. However, BERT implementation in the mining industry, which has unique technical aspects and jargon, brought a set of challenges in terms of preparation of data, selection of hyperparameters, and fine-tuning the model to achieve the best performance, which was met in the current research.

  PublisherOA PDFDOI

• The Importance of Specific Phrases in Automatically Classifying Mine Accident Narratives Using Natural Language Processing

  Knowledge · 2022-07-29 · 5 citations

  articleOpen accessSenior author

  The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents.

  PublisherOA PDFDOI

• Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

  Minerals · 2022-01-05 · 4 citations

  articleOpen access1st authorCorresponding

  This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...]

  PublisherOA PDFDOI

• Advances in Computational Intelligence Applications in the Mining Industry

  2022-02-11

  bookOpen access1st authorCorresponding

  This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

  PublisherDOI

• Gaining Insight from Semi-Variograms into Machine Learning Performance of Rock Domains at a Copper Mine

  Minerals · 2022-08-23 · 4 citations

  articleOpen accessSenior author

  Machine learning (ML) is increasingly being leveraged by the mining industry to understand how rock properties vary at a mine site. In previously published work, the rock type, granodiorite, was predicted with high accuracy by the random forest (RF) ML method at the Erdenet copper mine in Mongolia. As a result of the optimistic results (86% overall success rate), this paper extended the research to determine if ML would be successful in modeling rock domains. Rock domains are groups of rocks that occur together. There were two additional goals. One was to determine if the variograms could predict or help understand how ML methods would perform on the data. The second was to determine if 2D modeling would perform well given the disseminated nature of the deposit. ML methods, multilayer perceptron (MLP), k-nearest neighborhood (KNN) and RF, were applied to model six rock domains, D0–D5, in 2D and 3D. Modeling performance was poor in 2D. Prediction performance accuracy was high in 3D for the domains D1 (92–94%), D2 (94–96%) and D4 (85–98%). Note that the domains D1 and D2 together constituted about 80% of the samples. Conclusions drawn in this paper are based on the results of 3D modeling since 2D modeling performance was poor. Prediction performance appeared to depend on two factors. It was better for a domain when the domain was not a minuscule proportion of the sample. It was also better for domains whose indicator semi-variogram (ISV) range was high. For example, though D4 only contributed 15% of the samples, the range was high. MLP did not perform as well as KNN and RF, with RF performing the best. The hyperparameters of KNN and RF suggested that performance was best when only a small number of samples were used to make a prediction. One overall summary conclusion is that the two most important domains, D1 and D2, could be predicted with high accuracy using ML. The second summary conclusion is that semi-variograms can provide insight into ML performance.

  PublisherOA PDFDOI

• Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine

  Minerals · 2021 · 26 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.

  PublisherOA PDFDOI

Frequent coauthors

• Sukumar Bandopadhyay

  Ansys (United States)

  13 shared

• B. Samanta

  Jadavpur University

  9 shared

• Rambabu Pothina

  University of Utah

  9 shared

• Sridhar Dutta

  5 shared

• Daniel Mendoza

  5 shared

• Jon C. Yingling

  University of Kentucky

  5 shared

• Erik T. Crosman

  West Texas A&M University

  3 shared

• J.S. Puri

  The Alberta Paraplegic Foundation

  3 shared

Labs

• Mining Engineering - The University of UtahPI

Awards & honors

• Western Presidential Endowed Chair in Mining Safety & Health