Carlos Jerez Hanckes

I am an Associate Professor of Numerical Analysis at KTH, where I lead research at the frontier of wave propagation, boundary integral methods, and uncertainty-aware scientific computing. With two decades of experience as a researcher, academic leader, and founder, I focus on bridging the gap between deep mathematical theory and high-impact industrial applications.

I joined KTH in 2026 to further my work in developing the next generation of numerical solvers for complex physical systems. Throughout my career, I have secured funding for approximately 20 research projects (totaling ~250 MSEK), serving as Principal Investigator or Project Lead on 14 of those initiatives.

Research Leadership & Innovation

My research lies at the intersection of Numerical Analysis and Scientific Computing, with a focus on developing high-order, robust discretization techniques for wave-matter interactions. My work addresses the computational challenges of high-frequency scattering and the inherent uncertainties of real-world physical domains.

• Boundary Integral Equations & Operator Preconditioning: I am the originator of Multiple Trace Formulations (MTFs) for wave transmission problems. My work in this area focuses on the design of robust Calderón preconditioners, ensuring that boundary element systems remain well-conditioned regardless of mesh refinement or physical parameters—a critical requirement for large-scale industrial simulations.

• Uncertainty Quantification (UQ) in Wave Propagation: I develop advanced algorithms for scattering with random shapes and parameters, employing Galerkin BEM and perturbation theories to quantify how geometric uncertainties propagate through electromagnetic and acoustic systems. This is pivotal for the reliability of sub-wavelength technologies and biomedical imaging.

• Scientific Machine Learning (SciML): I am currently pioneering structure-preserving neural networks for PDEs. Unlike black-box AI, this research focuses on embedding physical constraints and geometric invariants directly into the architecture of deep learning models, ensuring that data-driven predictions remain consistent with the underlying conservation laws of physics.

• Applied Computational Physics: I translate these theoretical foundations into interdisciplinary solutions, including nanophotonic design, radar cross-section (RCS) optimization, and electropermeabilization modeling for cellular bio-physics.

Industrial Samverkan & Entrepreneurship

My approach to the "Third Assignment" (samverkan) is rooted in the operational experience of managing large-scale academic-industrial ecosystems. I specialize in aligning high-level research objectives with the strategic needs of the public and private sectors.

• Academic Executive Leadership: As Dean of the Faculty of Engineering and Sciences at UAI (Universidad Adolfo Ibáñez), I oversaw the strategic and financial direction of a major multi-disciplinary faculty. I was the architect of the Institute for Mathematical and Computational Engineering (IMC UC), specifically designed to foster cross-disciplinary research and industrial translation.

• National-Scale Data Governance: In my role as Executive Director of the Data Observatory Foundation, I managed a strategic partnership involving the Chilean Ministries of Economy and Science, Amazon Web Services (AWS), and the ALMA Observatory. I led the development of large-scale, open-access data infrastructures for the global scientific community and government decision-support from Covid-19 to large AI-assisted astronomical surveys.

• Research-Driven Entrepreneurship: My work as a founder (Atheneum.ai, hcmfront.com) is a direct extension of my research. I have successfully scaled deep-tech platforms that utilize numerical modeling and data analytics to solve structural inefficiencies in human capital and industrial procurement.

• Institutional Bridge-Building: I serve as a catalyst for "Triple Helix" collaborations, translating complex mathematical frameworks—such as large-scale wave simulations and predictive modeling—into actionable intelligence for radar design, biomedical imaging, and industrial R&D.