Ph.D. Student at the School of Mines of Paris and Data scientist at Eramet, specializing in leveraging data & AI to enhance safety, optimize operations, and drive sustainable practices in the mining industry. Dedicated to reshaping mining practices through innovative applications of data science and machine learning.

Paweł Stefaniak received the M.Sc. degree in Geoinformatics (2011), the D.Sc. degrees in Mining Transport (2016) and the post-doctoral degree in mining sciences (2023) from Faculty of Mining Faculty of Geoengineering, Mining and Geology at Wroclaw University of Science and Technology. In 2014, he started working for KGHM Cuprum R&D Centre Ltd as a Business Analyst in area of analytical applications in the mining industry, especially mining machinery and processes monitoring, predictive maintenance, energy efficiency, decision support system and the application of IoT and robotics solutions. Since 2012 he serves as head of the System Analytics Department in KGHM Cuprum R&D Centre Ltd. He is author of more than 100 works with over 20 published in leading journals and indexed conference proceedings.

Professional of scientific Analytic Engineer at PT. Pamapersada Nusantara, I am passionate about anything related to data and machining systems, specifically on gaining insights and setting up the appropriate strategies for both accordingly. This benefits from my major where I started to take specifications course related to machine maintenance analysis. In the last year, I am developing my skills by working on an electric car development project at my campus that deals with various things from the non-technical side. In there I have the responsibility to support car development, from making BOM documents to building business analysis documents with the team.

Dedicated and accomplished Associate Professor in the Department of Petroleum and Mining Engineering with a Ph.D. and over a decade of academic experience. Expertise in mine production planning, mine design, Artificial intelligence techniques in specialized fields, hydraulic fracturing, and optimal placement of drilling wells. Published extensively in reputable journals and conferences, contributing to advancements in mining and petroleum engineering. Experienced in leading research projects that integrate artificial intelligence with industry applications, fostering strong collaborations between academia and industry. Adept at curriculum development, student engagement, and driving innovation through research and partnerships. Committed to advancing the field through knowledge dissemination and industry-driven solutions.

Milka Madahana is currently a Lecturer at the School of Mining Engineering, at the University of the Witwatersrand, where she conducts data science, control, automation and application of digital technologies in the mining industry related research work, and she also lectures data science related courses. She holds doctorate, Master’s degree and Bachelor of Science in Engineering which were all conferred by the University of the Witwatersrand. She has worked as a Data scientist for the Business Intelligence Service Unit of the University of the Witwatersrand and continues to collaborate with institutions of higher learning as well as the industry both locally and internationally on data science and automation related projects. She has authored and co-authored several peer-reviewed journals and conference papers, as well as two book chapters in areas of application of artificial intelligence and machine learning concepts to mining systems. Her other research interests include modelling, automation, and control of engineering systems. She has also reviewed articles for several international journals and conferences in line with her research interests.