Director, Strategic Mathematics Solutions
Dr. Monica Martinez-Canales, Sr. Principal Engineer, is responsible for driving new opportunities in data analytics, AI/DL/ML algorithms, and mathematics-based breakthroughs for deployment in operational processes, software, platforms, accelerators or Silicon and driving new use cases for Edge-to-Data-Center data analytics, supporting software optimization on Intel Architecture, and providing insights on industry trends.
Monica is an interdisciplinary leader, combining disciplines of Mathematics, Computer Science, Biological Science, and Environmental Science. Her work involves decision making in complex and uncertain environments, big data analytics, numerical operations research algorithms, distributed compute, Bayesian Statistics, multi-scale/multi-physics/multi-fidelity models, machine learning and artificial intelligence.
Monica has been Director and Research Lead of Applied Research & Pathfinding team for the Autonomous Vehicle R&D Data Center Platform, delivering scalable and distributed perception, privacy, and learning for Autonomous Vehicles; Director of Strategic Mathematics Solutions / Applied Mathematics exploring Clifford algebras and their applications; and Director of Big Data for Science & Technology in Genomics and Precision Medicine developing Big Data and HPC architectures and data flow processes linking genome data to disease risk. Monica joined Intel in 2008 as a Principal Engineer leading Strategic Initiatives in Validation Business Intelligence and Analytics within the Platform Validation Engineering Group developing operational processes to accelerate product delivery schedules.
Prior to joining Intel, Monica had been a Principal Member of the Technical Staff at Sandia National Laboratories, and Principal Investigator leading award-winning research in verification, validation, and quantifications of margins under uncertainty of multi-fidelity, multi-disciplinary complex systems within defense and energy programs. Monica completed a National Science Foundation Post-Doctoral Fellowship at Stanford University. She also conducted postdoctoral research at University of Texas at Austin, where she developed Hilbert Space Filling Curve-based key-value indexing to manage domain decomposition parallelization strategies of Finite Element Models of hyperbolic partial differential equations on Intel Paragon, Cray Y-MP, IBM SP2, and heterogeneous clusters. Monica earned a Ph.D. in Computational and Applied Mathematics from Rice University and received a B.S. in Mathematics from Stanford University. Monica is author of multiple peer-reviewed journal articles. Monica has been a long-time STEM/STEAM advocate and mentor.