Team Members

Yuksel Asli Sari, PhD, P.Eng

Yuksel Asli Sari is an Associate Professor at the Robert M. Buchan Department of Mining at Queen’s University in Kingston, Canada. She received BSc, Master’s and PhD degrees from McGill University. With her background in computer science, she focuses on the computer applications in the mining industry. She has developed tools for open pit mine planning (finding pit limits, block extraction sequencing and block routing) and stope optimization (stope layout planning, stope sequencing). Also, she has designed mathematical models for dig-limit optimization, open pit mine planning with landfilling option and to determine the stope limits. Her research interests include short term and long term underground and surface mine planning, data analytics and machine learning applications in mine optimization and mine automation.

Dr. Emmanuel J. A. Appianing

Dr. Emmanuel J. A. Appianing is a Postdoctoral Research Fellow and Sessional Professor of Mining Engineering at the Goodman School of Mines and MIRARCO Mining Innovations, Laurentian University, Sudbury, Ontario. He also serves as a Research Assistant at the Mining Optimization Laboratory (MOL) within the Bharti School of Engineering and Computer Science. Dr. Appianing holds a Ph.D. in Natural Resources Engineering (Mining Engineering) from Laurentian University, as well as a B.Sc. and M.Phil. in Mining Engineering from the University of Mines and Technology (UMaT), Tarkwa, Ghana. With over 12 years of combined experience in both industry—spanning open-pit and underground operations—and academic research, Dr. Appianing has developed deep expertise across a broad spectrum of mining disciplines. His areas of specialization include but not limited to: Mine planning and design, Production scheduling optimization, Rock mechanics and ground control, Mine equipment automation and reliability, Drilling and blasting technologies His current research centers on strategic mining options optimization for resource development and long-term planning, with a particular focus on stochastic mine planning under geological and operational uncertainty.

Saad Salman

Saad Salman holds a master’s degree in mining engineering from the University of Engineering and Technology, Pakistan. He previously worked as a Research Associate at the National Center of Artificial Intelligence, Pakistan, where he focused on applying AI to mining, developing algorithms for short and long-term mine planning, as well as predictive analysis. 
In September 2023, Saad began his PhD in the Robert M. Buchan Department of Mining at Queen’s University under the supervision of Dr. Yuksel Asli Sari, co-supervised by Prof. Dr. Julain Ortiz Cabrera. His research focuses on using data and artificial intelligence to improve the operation of mineral processing plants. Specifically, he is working to shift decision-making from traditional experience and intuition-based methods to data-driven methods that support real-time control and boost operational efficiency. His studies are based on Ni-Cu flotation systems in the Sudbury Mining District of Ontario, in collaboration with leading mining companies.

Batur Tokac

Batur Tokac is a Ph.D. student in the Robert M. Buchan Department of Mining at Queen’s University, working in the Intelligent Mining Systems Lab. He holds MASc and BSc degrees in Mining Engineering from Queen’s University (GreenMVC Lab) and Middle East Technical University, respectively. With strong academic performance, he represents a new generation of mining engineers who blend traditional mining expertise with computational intelligence and sustainable innovation. Batur’s research takes a multidisciplinary approach, integrating data science and machine learning to tackle complex problems in stochastic mine planning, electric haulage systems, and production scheduling. What sets Batur apart is his remarkable ability to think outside conventional boundaries, developing novel simulation-based frameworks that integrate discrete-event modeling, genetic algorithms, and data-driven optimization to enhance energy-efficient decision-making in surface mining operations. His current work develops comprehensive frameworks for battery-electric vehicle implementation in mining operations, using genetic algorithms, machine learning, and reinforcement learning approaches. His research combines uncertainty quantification, AI techniques, and economic constraints to address the transition from diesel to electric mining fleets. He brings industry experience to his research and works toward advancing sustainable mining practices through computational modeling and data-driven methods that support the industry’s move to low-carbon, intelligent mining systems.

Ricardo Quevedo

Ricardo Quevedo holds a Master of Applied Science in Mining Engineering from Queen’s University in Kingston, Canada. He has previous experience as a Geomechanics Engineer, where he used machine learning methods to assess geomechanical risk in large-scale underground cave mine operations. Ricardo is currently pursuing his PhD in The Robert M. Buchan Department of Mining at Queen’s University under the supervision of Dr. Yuksel Asli Sari and co-supervision of Dr. Stephen McKinnon. His research focuses on designing digital environments to enable more effective data gathering in underground mines, creating coherent spatial structures for machine learning analysis applied to major hazards. Specifically, he aims to incorporate data analytics and advanced computational methods like machine learning into the traditional methods of analysis to better understand complex phenomena in cave mines. His work is being conducted within Dr. Sari’s research group at Queen’s University.

Jaime Seguel

Jaime Seguel is a Metallurgical Civil Engineer with a BSc. from Universidad Técnica Federico Santa María in Valparaíso, Chile, specialized in mineral processing. He has worked as a Metallurgical Data Analyst, Process Engineer, and Advanced Analytics Translator Engineer at Codelco Chile. He is currently pursuing a Master of Applied Science in Mining Engineering at Queen’s University in Canada.

Yizhi Wang

With a focus on AI-driven high-temperature aluminum alloy design for aerospace applications, Yizhi is passionate about accelerating materials discovery through thermodynamic modeling combined with machine learning. When not struggling to debug simulations from a cozy sofa with a glass of whisky, you’ll find him playing the cello, listening to jazz, hosting BBQs, or mixing cocktails with the precision of a metallurgist.

Evan Melrose

Evan Melroseis a master’s student in computer engineering at Queen’s University, working in the E-Power Lab under the supervision of Prof. Yuksel Asli Sari and Prof. Majid Pahlevani. His research focuses on plant phenotyping and digital twinning, with an emphasis on developing computational methods for analyzing 3D point cloud data and integrating thermal imaging. He applies machine learning and computer vision techniques to model plant structure and behaviour.