Keynote Speakers, IAMG Awardees, and IUGG Medalist
Keynote Speakers
- Dr. Arja Jewbali, Head of Global Resource Management, Newmont Corporation, USA
- Prof. Peter Dowd, The University of Adelaide, Australia
- Prof. Tapan Mukerji, Stanford Center for Earth Resources Forecasting (SCERF), and the Stanford Center for Geological Hydrogen (SCGH) projects, Stanford University, USA
- Prof. Louis J. Durlofsky, Department of Energy Science and Engineering, Stanford University, USA
IAMG Awardees
- Prof. Peter Atkinson, Lancaster University, UK - Krumbein Medal 2026
- Prof. Denis Allard, INRAE, France - Georges Matheron Lecturer 2026
- Mr. Mohan Srivastava , Resource Estimation Consultant, RedDot3D Inc. - Distinguished Lecturer 2027
IUGG Medalist
- Prof. Ebru Bozdağ, Colorado School of Mines, USA - IUGG Vladimir Keilis-Borok Medal 2026
Keynote Speakers↑
Arja Jewbali↑
Arja Jewbali is Head, Resource Management, Global for Newmont. She holds a Doctorate in Geostatististics from the University of Queensland and has close to 20 years of experience having worked extensively across gold, silver, iron ore, and base metal deposits. Throughout her career, she has built and audited resource models, led global teams, and developed geostatistical best practices that improve mine planning outcomes.. Before joining Newmont in 2011 she was employed by Rio Tinto Iron Ore in a number of positions including Resource Geologist and Senior Geostatistician. She has co-authored multiple papers on geostatistics and resource modeling.
Talk title: coming soon
Coming soon.
Peter Dowd↑
Professor Peter Dowd is Professor of Mining Engineering at the Adelaide University, Australia. Prior to his current appointment, he worked at universities in Canada and the UK. He is currently Director of the Australian Research Council Industrial Transformation Training Centre for Integrated Operations for Complex Resources at the University of Adelaide. He is an elected Fellow of the Royal Academy of Engineering and an elected Fellow of the Australian Academy of Technological Sciences and Engineering. His research areas are in mathematical geosciences applied to natural resources and to environmental and climate variables. More recently, in collaboration with European colleagues, he has published widely in hydrology, groundwater, climatography, fractal analysis of karst landscapes, closed depressions on the surfaces of the moon and Mars, and fractal analysis of the Martian landscape. He was awarded the 2016 IAMG Krumbein Medal for his contributions to geostatistics and mathematical geosciences. He was President of the IAMG for the period 2020-2024 and is currently Past-President of the IAMG for 2024-2028.
Talk title: Coming soon.
Tapan Mukerji↑
Tapan Mukerji is a Professor (Research) in the Departments of Energy Science & Engineering, Geophysics and Earth & Planetary Sciences at Stanford University. He received his B.S, Physics, and M.S. (Tech.), Geophysics from BHU, India, and his Ph.D. in Geophysics from Stanford University. The focus of Mukerji’s multi-disciplinary research, with students and colleagues, has been on integrating rock physics, wave propagation physics, spatial data science, and machine learning and their broad applications in subsurface characterization, stochastic geomodeling, uncertainty quantification and value of information in Earth sciences. He uses theoretical, computational, and statistical methods, to discover and understand fundamental relations between geophysical data and rock properties, to quantify uncertainty in subsurface models, and to address value of information for decision making under uncertainty. Prof. Mukerji was awarded the Society of Exploration Geophysicists’ Karcher award in 2000 and the ENI award in 2014 for pioneering innovations in theoretical and practical rock physics for seismic reservoir characterization. He co-directs the Stanford Center for Earth Resources Forecasting (SCERF), and the Stanford Center for Geological Hydrogen (SCGH) projects.
Talk title: coming soon
Coming soon.
Louis J. Durlofsky↑
Louis J. Durlofsky is the Otto N. Miller Professor of Earth Sciences in the Department of Energy Science and Engineering at Stanford University. He co-directs the Stanford Smart Fields Consortium and the Stanford Center for Carbon Storage. Earlier in his career, Durlofsky was affiliated with Chevron Energy Technology Company. He holds a BS degree from Pennsylvania State University, and MS and PhD degrees from Massachusetts Institute of Technology, all in chemical engineering. His research interests include subsurface flow simulation, geological carbon storage, data assimilation and uncertainty quantification, optimization, and deep learning-based surrogate modeling.
Talk title: Data assimilation for subsurface flow using deep learning-based surrogate models
coming soon
IAMG Awardees↑
Peter Atkinson - Krumbein Medal↑
Peter Atkinson is Distinguished Professor of Spatial Data Science at Lancaster University, Lancaster, UK where he was also Executive Dean of the Faculty of Science and Technology from 2015 to 2025 and interim Executive Dean of the Faculty of Health and Medicine from 2018 to 2019. Professor Atkinson’s research interests are highly interdisciplinary with a focus on methods for remote sensing, spatial statistics and artificial intelligence applied to a wide range of grand challenge-motivated questions in science, including in land systems, natural hazards, agriculture, ecology and epidemiology. He is highly regarded for his research on geostatistical change-of-support theory and downscaling in Earth observation. Professor Atkinson is a Fellow of the Learned Society of Wales and is an inaugural highly ranked scholar on ScholarGPS (2024, 2025) and ISI highly cited researcher (2023, 2024). He received the Cuthbert Peek Award of the Royal Geographical Society-Institute of British Geographers (RGS-IBG) in 2024, was the 2020 Distinguished Lecturer of the International Association of Mathematical Geosciences (IAMG), and Laureat of the Peter Burrough Medal of the International Spatial Accuracy Research Association (ISARA) in 2016. He was awarded the Belle van Zuylen Chair with Utrecht University, Utrecht, The Netherlands in 2014 and was Visiting Fellow at Green-Templeton College, Oxford University 2012-14. Professor Atkinson is founding Editor-in-Chief of Science of Remote Sensing and Associate Editor of Environmetrics.
Talk title: coming soon
Coming soon.
Denis Allard - Georges Matheron Lecturer 2026↑
Denis Allard is Research Director at INRAE, the French National Research Institute for Agriculture, Food and the Environment. He holds a Master of Science and a PhD from the Ecole des Mines de Paris, France. He had appointments in the Statistics Department, University of Washington (Seattle, USA) and worked with British Petroleum as Geostatistician. He joined INRAE in Avignon in 1996 and found it to be an excellent place for research. He never left. From 2005 to 2011 he has been the head of the BioSP (Biostatistics and Spatial Processes) research unit at INRAE, Avignon. Recent contributions include the characterization of anisotropy for random fields, new classes of multivariate space-time cross-covariance functions, efficient geostatistical simulation algorithms in various settings, SPDEs for spatio-temporal data, flexible geostatistical methods for compositional data and aggregation of distributions in climate science. He is currently associate editor for Spatial Statistics and has served on its editorial board since the journal was launched in 2012. He has been associate editor for Computing and Statistics and Mathematical Geosciences. His research covers a wide range of topics in geostatistics and spatial statistics for modeling and analyzing spatio-temporal data, with applications in geosciences, environment and climate sciences. Currently, he is the Principal Investigator of the Geolearning Chair, a joint research program between BioSP and the Geostatistics team at Ecole des Mines de Paris.
Talk title: Coming soon.
Coming soon.
Mohan Srivastava - Distinguished Lecturer 2027↑
Mo is an author of “An Introduction to Applied Geostatistics”, and of more than 50 technical articles on the theory and practice of geostatistics. He has applied geostatistics to mineral deposits, petroleum reservoirs, environmental contamination, climate change, animal populations and epidemiology. He has taught geostatistics in public short courses and in several universities, most recently at the Universitat Politècnica de València. Outside of his day-job as a consultant, Mo “cracked the code” of an instant scratch lottery game, allowing him to separate winners from losers without scratching anything off the face of the cards (www.wired.com/2011/01/cracking-the-scratch-lottery-code). He also finds time to write, and is the 2013 winner of the Canada Writes Prize for Non-Fiction for his short story "The Gods of Scrabble" (www.cbc.ca/books/the-gods-of-scrabble-by-mo-srivastava-1.4112595). In 2016, Mo was chosen by the Professional Geoscientists of Ontario as the inaugural recipient of their Award of Merit for significant career contributions to geosciences. He was chosen by the Society of Mining Engineers as the 2025 recipient of the Harry Parker Award of Excellence for outstanding communication of geostatistical and spatial statistical concepts.
Talk title: Regional assessment of mining potential for the Ring of Fire region in Northern Ontario
IUGG Medalist↑
Ebru Bozdağ - IUGG Vladimir Keilis-Borok Medal 2026↑
Ebru Bozdağ is an Associate Professor in the Department of Applied Mathematics and Statistics with a joint appointment in the Department of Geophysics at the Colorado School of Mines, USA. Earlier Bozdağ was a tenured Associate Professor (Maître de Conférence) and held a Chaire d'Excellence position at Géoazur, University of Côte d'Azur, Nice, France, and an Associate Research Scholar at Princeton University, USA. She earned her PhD in Seismology from Utrecht University, The Netherlands, and MSc/BcS in Geophysics from Istanbul Technical University, Türkiye. Bozdağ's research focuses on computational and global seismology, leveraging 3D seismic wave simulations to advance our understanding of Earth's and other planets' internal structures and dynamics and mitigate seismic hazards. She aims to improve existing tomographic and observational techniques, develop new methodologies, and apply them to seismic observations to construct multiscale and multiparameter tomographic models, with a particular emphasis on global full-waveform inversions to image Earth's mantle. Her work heavily relies on high-performance computing, seamlessly integrating large-scale numerical simulations and optimization techniques with large, heterogeneous datasets to harness computational power and big data in seismology to address geoscientific problems.
Talk title: Global Seismology in the Era of High-Performance Computing: Full-Waveform Modeling Insights into Earth’s Deep Interior
Advances in numerical methods and solvers for seismic wave propagation, and high-performance computing have been transformative for seismology, enabling us to account for the full complexity of wave propagation in seismic tomography and leading to the development of first-generation global full-waveform models. While we can successfully address the intricacies of wave propagation, the next step is to incorporate better physics also in inversions through appropriate parameterization. In this presentation, we will outline our efforts to explore the Earth’s interior, from sedimentary basins to the outer core, using 3D full-waveform modeling and construction of next-generation mantle models focusing on the interpretation of upper-mantle anisotropy based on our recent radially and azimuthally anisotropic global full-waveform inversion model. We will also discuss the next challenges in global seismology, and how to further improve the resolution of global tomographic models in the context of parameterization, trade-off between seismic parameters, and integration of emerging data in full-waveform inversions to tackle the global data coverage problem, to have better constraints on the dynamics and the thermochemical structure of the Earth’s deep interior.