Photo of Prof. Ganguli by Taras Kohanevych
  • Associate Dean (Assessment), College of Mines and Earth Sciences
  • Professor, Mining Engineering
801-585-0958

Research Statement

Dr. Ganguli has led approximately $13M in projects as primary investigator. He is currently involved in several projects in five different countries, US, Denmark/Greenland, Mongolia, Saudi Arabia and Mexico, on topics ranging from machine learning to training.  Two of the projects involve major mining companies on mine to mill type problems. His ai.sys group is also assisting an interdisciplinary team with linking air quality and other factors to health and educational outcomes. Another effort involves developing natural language processing (NLP) based machine learning tools to analyze textual mine safety data. In the mine training project, he is leading a $1.2M cooperative agreement with the US Department of State to provide training to KTIR, Greenland.

WATCH FOR IT - His research group is scheduled to release their analytics software, UteAnalytics, to the public in 2023.

In addition to computational work, he acknowledges mining as a truly interdisciplinary industry and recognizes the many opportunities afforded to interdisciplinary teams. His past projects, therefore, included topics such as bacterial remediation of acid mine water, enhancement of mineral flotation processes, recovery of rare earths, training simulator for grinding mills, and effectiveness of coal combustion.

 

Research Keywords

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning
  • Mining Engineering
  • Systems Engineering

Research Groups

  • Narmandakh Sarantsatsral, Postdoc. 07/03/2023 - present.

Presentations

  • Ganguli, R., 2023, “Machine Learning in Discovery,” Alaska CORE-CM Stakeholder Meeting, September 13, Fairbanks. Invited Talk/Keynote, Presented, 09/13/2023.
  • Ganguli, R., Pothina, R., Jewbali, A., Allen, L. and Villanueva, J. E., 2023, “Understanding Mine To Mill Relationships at Penasquito Mine,” Peer Reviewed Proceedings, 26th World Mining Congress, Brisbane, Australia, June. Conference Paper, Refereed, Presented, 06/28/2023.
  • Ganguli, R., 2023, “Taking a Stepped Approach to Machine Learning,” Invited Keynote Address, 7th Advanced Process Control Congress, Lima, Peru, June. Invited Talk/Keynote, Presented, 06/23/2023.
    https://tinyurl.com/3fmp97wj
  • Oduro, L. and Ganguli, R., 2023, “Development of a Machine Learning Tool for Mining Industry,” SME Annual Meeting, February. Conference Paper, Presented, 02/28/2023.
  • Pothina, R. and Ganguli, R., 2023, “Role of Grammar, Linguistic Rules, and Contextual Representation in Advancing the Natural Language Processing of Mine Safety Narratives,” SME Annual Meeting, February. Conference Paper, Presented, 02/27/2023.
  • Relating Grinding Attributes from Mill back to Mine Locations Event: Newmont Communities of Practice. Invited Talk/Keynote, Presented, 09/13/2022.
  • Title: Meaningful Artificial Intelligence Is About More Than Just Deep Learning Event: Advanced Process Control Conference, Peru. Invited Talk/Keynote, Presented, 06/24/2022.
  • Online exhibit from the University of Utah, Ganguli, R., Pothina, R. and Nelson, M.G., 2022, “Profile of Bingham Miners,” https://exhibits.lib.utah.edu/s/mining-the-west/page/profile-miners. Other, Presented, 04/01/2022.
    https://exhibits.lib.utah.edu/s/mining-the-west/pa...
  • Pothina, R., Ganguli, R., 2022, “Advancing Natural Language Processing Based Random Forest Models in Analyzing Mine Safety and Health Administration (MSHA) Narratives,” SME Annual Meeting, March. Conference Paper, Presented, 02/28/2022.
  • Leveraging MSHA Database for Developing Natural Language Processing Tools for Mine Safety Analytics, SME Annual Meeting (virtual). Recorded Jan 2021. To be streamed in March 2021. Conference Paper, Presented, 03/04/2021.
  • Webinar hosted by NITK, India. Topic: Opportunities in the US and in mining. Invited Talk/Keynote, Presented, 09/25/2020.
  • Mosaic Phosphates, an international mining company. Invited to talk on analytics to their leadership during their internal conference. Invited Talk/Keynote, Presented, 08/25/2020.
  • Webinar hosted by Anna University, India. Sole speaker. Topic: AI applications in mining. Invited Talk/Keynote, Presented, 06/21/2020.
  • Presentation to Kennecott Bingham Mine Data Group on my research. Other, Presented, 10/2019.

Languages

  • Bengali, functional.
  • Hindi, functional.
  • Telugu, basic.

Geographical Regions of Interest

  • Africa
    Engaged with the African Union. We are currently developing agreements.
  • Denmark
    Greenland, Working with KTIR, Sisimiut and the US Dept of State on mining related curriculum.
  • India
    Serving as Overseas Professor, Anna University, Chennai, and Adjunct Faculty, National Inst. of Technology Karnataka, Surathkal.
  • Mexico
    Applying machine learning at a large mine.
  • Mongolia
    Dr. Ganguli has worked with Erdenet Mining Corporation on mine-mill reconciliation using data mining, sampling and blast movement monitoring. He also led the engineering curriculum design efforts of the American University of Mongolia. In that effort, he worked closely with numerous engineering employers including Oyu Tolgoi and the Government of Mongolia.
  • Peru
    I have been repeatedly invited to give Keynote speeches and engage with mining companies.
  • Saudi Arabia
    Assisting King Fahd University of Minerals and Petroleum establish a mining engineering program.

Software Titles

  • UteAnalytics. Disclosed intellectual property. A machine learning Windows based tool for the domain expert. It is being distributed for free via https://mining.utah.edu/ai.sys It has been downloaded by many around the world. Release Date: 07/24/2023. Inventors: Rajive Ganguli, Lewis Oduro.
  • Dynamic Mill Simulation Training Software. A simulator for training mining mill operators to teach them fundamentals of a grinding circuit. Unlike typical simulators in the market, this dynamic simulator is not static. Release Date: 06/2018. Inventors: Conceived by Ganguli, and proposal led by Ganguli. Development led by Ghosh, T., Ganguli, R., etc.

Publications

  • Ganguli, R., Pothina, R., Jewbali, A., Allen, L. and Villanueva, J. E., 2023, “Understanding Mine To Mill Relationships at Penasquito Mine,” Peer Reviewed Proceedings, 26th World Mining Congress, Brisbane, Australia, June. Published, 06/30/2023.
  • Ganguli, R., 2023, “Machine Learning: The Language of Safety”, Accepted, In: Mine Safety and Health: Approaches from the Field, SME Publications. Accepted, 04/28/2023.
  • Pothina, Rambabu & Ganguli, Rajive (2023). Contextual Representation in NLP to Improve Success in Accident Classification of Mine Safety Narratives. MDPI. Vol. 13. Published, 04/27/2023.
    https://www.mdpi.com/2075-163X/13/6/770
  • Oduro, Lewis & Rajive Ganguli (2022). Development of a Data Analytics & Machine Learning Tool for the Mining Industry. Crimson Publishers. Published, 09/26/2022.
  • Rambabu Pothina & Rajive Ganguli (2022). The Importance of Specific Phrases in Automatically Classifying Mine Accident Narratives Using Natural Language Processing. MDPI. Published, 07/21/2022.
    https://doi.org/10.3390/knowledge2030021
  • Rajive Ganguli, Sean Dessureault & William Pratt Rogers (2022). Advances in Computational Intelligence Applications in the Mining Industry. MDPI. Published, 02/01/2022.
    https://www.mdpi.com/books/pdfview/book/4987
  • Sarantsatsral, N., Ganguli, R., Pothina, R. & Tumen-Ayush,B.A (2021). A Case Study of Rock Type Prediction Using Random Forests: Erdenet Copper Mine, Mongolia. Minerals. Vol. 11. Published, 09/28/2021.
    https://www.mdpi.com/2075-163X/11/10/1059
  • Mendoza, D.L., Benny, T.M., Ganguli, R., Pothina, R., Pirozzi, C.S. & et al (2021). The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas. MDPI. Vol. 1, 186-202. Published, 08/04/2021.
    https://www.mdpi.com/2673-8112/1/1/16/pdf
  • Rajive Ganguli & Preston Miller, Rambabu Pothina (2021). Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. MDPI. Vol. 7. Published, 07/17/2021.
    https://www.mdpi.com/2075-163X/11/7/776
  • Ganguli R. (2020) Water in Mongolia: Sources, Uses and Issues, with Special Emphasis on Mining. In: Regmi G., Huettmann F. (eds) Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives. Springer, Cham. Published, 05/05/2020.
    https://link.springer.com/chapter/10.1007/978-3-03...
  • Pimenta, Eduardo de Melo & Ganguli, R. and Pothina, R. (2020). Modification and Enhanced Testing of Data Mining-Based Algorithm to Detect Subtle Errors in Temperature Sensors in Gold Stripping Circuit. Mining, Metallurgy & Exploration. Published, 02/07/2020.
    http://link.springer.com/article/10.1007/s42461-02...
  • Pothina, R. & Ganguli, R. (2020). Detection of Subtle Sensor Errors in Mineral Processing Circuits Using Data-Mining Techniques. Springer. Published, 01/2020.
    https://doi.org/10.1007/s42461-020-00176-y
  • Ganguli, R. & Cook, D. R. (2018). Rare earths: A review of the landscape. Cambridge University Press. Vol. 5. Published, 06/2018.
    https://doi.org/10.1557/mre.2018.7
  • Srivastava, V, Akdogan, G., Ghosh, T. & Ganguli, R (2018). Dynamic Modeling and Simulation of A Sag For Mill Charge Characterization. Minerals and Metallurgical Processing. Vol. 35. Published, 02/2018.
    https://doi.org/10.19150/mmp.8287
  • Ganguli R., Chieregati A. & Purvee A. (2017). Fundamental error estimation and accounting in the blasthole sampling protocol at a copper mine. (pp. 49-54). Vol. 69. Mining Engineering. Published, 11/01/2017.
  • Ganguli R., Purvee A., Sarantsatsral N. & Bat N. (2017). Investigating particle size: Distribution of blasthole samples in an openpit copper mine and its relationship with grade. (pp. 29-33). Vol. 69. Mining Engineering. Published, 02/01/2017.
  • Arku, D. & Ganguli, R. (2014). Investigating Utilization Of Aggregated Data: Does It Compromise Information Gleaning. Mining Engineering. Published, 06/2014.
  • Aggarwal, S. & Ganguli, R. (2011). Refining Automated Modeling of Operational Data by Identify the Most Important Input Factors. Mining Engineering. Published, 12/2011.
  • Dutta S., Bandopadhyay S., Ganguli R. & Misra D. (2011). Critical assessment of machine learning algorithms as estimation techniques for a polymetallic ore deposit. (pp. 899-914). 35th APCOM Symposium - Application of Computers and Operations Research in the Minerals Industry, Proceedings. Published, 12/01/2011.