Akash Jani is a specialist structural geologist with over a decade of experience in the Iron Ore industry in greenfield & brownfield exploration and mining activities. A graduate of the University of Auckland, he holds a Bachelor of Science degree with a specialization in Geophysics and Applied Mathematics. Akash’s strong foundation in both quantitative and geological sciences has equipped him with a unique perspective in understanding the Earth’s complex structures. Akash really enjoys using technology to generate innovative ideas and improvement suggestions that would generate more value add to the business workflow while maintaining high technical standards and productivity.

Tarrant Elkington is a mining engineer and strategic consultant with extensive experience in mine planning, resource evaluation, and technical advisory across a range of commodities, including gold, iron ore, lithium, and base metals. He is General Manager at Snowden Optiro, where he supports mining companies in optimising project value through integrated technical and economic analysis. Tarrant has led and contributed to numerous studies, from early-stage concept evaluations through to feasibility studies and operational optimisation. His expertise includes open pit and underground mine design, cut-off grade strategy, reconciliation, and emerging areas such as mine electrification and advanced optimisation methods. In addition to consulting, he is actively involved in knowledge sharing through industry presentations, training courses, and technical content, helping bridge the gap between theory and practical application in geology and mining.

Dr. Kadri Dagdelen is a professor in the Mining Engineering Department at Colorado School of Mines, where he has been shaping the future of mining engineering education and research since 1992. Holding a Ph.D. in Mining Engineering with a focus on geostatistics from the Colorado School of Mines and a Master of Engineering from L’ecoledes Mines de Paris, Dr. Dagdelen brings a robust academic and practical background to his teaching and research activities. Dr. Dagdelen’s expertise encompasses mine planning, optimization, and geostatistics. He has developed and taught a variety of courses, ranging from computer applications to advanced geostatistical ore reserve estimation to operations research applications in mining. His professional tenure at Homestake Mining Company as a Manager and Senior Mine Planning Engineer saw him spearheading numerous innovative projects that significantly advanced technical computing and ore control functions in U.S. mine operations. His scholarly contributions include over 38 technical publications and presentations at international conferences, underscoring his prominent role in the global mining engineering community. Dr. Dagdelen's commitment to the field is further evidenced by his awards from SME and his long-standing involvement in this leading professional society.

Julien Herrero is a postdoctoral fellow in the COSMO lab at McGill University working in geostatistics, and more specifically on the development of new orebody modeling methods. He is currently working on kernel methods to produce a new high-order geostatistical simulation workflow. His research also focuses on probabilistic inverse problems, Bayesian methods, and uncertainty quantification for subsurface modeling. With a background in geostatistics, geophysics, and reservoir engineering, he developed approaches such as transdimensional MCMC during his PhD thesis (Nancy school of Geology, France, 2022-2025) to better characterize geological structures and their uncertainties. This work aims to improve the reliability of subsurface models for applications in geosciences and energy.

Yanyan Mu is a Post-Doctoral Researcher at the Stochastic Mine Planning Laboratory within the Department of Mining and Materials Engineering at McGill University. He hold a PhD in Electrical Engineering from McGill University, providing a rigorous mathematical foundation for their work in advanced computational modelling. His research is positioned at the critical intersection of generative machine learning and geostatistics, with specialized expertise in Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for orebody simulation.