Scientific Computing

Scientific Computing transforms mathematical theory into powerful tools for discovery. This research line advances high-performance and distributed computing to tackle data- and compute-intensive problems across science and engineering. At IE, researchers design scalable infrastructures, simulation frameworks, and computational models that make it possible to analyze and optimize complex systems, from molecular interactions to planetary dynamics.

Blending mathematics, algorithms, and computer science, scientific computing turns natural phenomena into computable representations. Through numerical methods, simulation, and parallel computation, it enables researchers to explore processes too vast or intricate for direct observation. By bridging theory and application, IE’s work in scientific computing drives innovation across disciplines, revealing new insights, refining predictions, and shaping the future of scientific discovery.

Lines of Research

High-performance and distributed computing for large-scale simulations
Tensor compression, data reduction, and uncertainty quantification
Multiscale modeling for molecular, biological, and physical systems
Computational optimization and parallel algorithm design
AI-driven simulation and scientific workflow automation
Visualization and interpretability of complex scientific data

Applications

Cryo-electron microscopy and structural biology
Climate and molecular dynamics simulations
High-throughput scientific computing for health and materials research
Tensor-based sensitivity analysis and data compression
Scalable computation for pandemic modeling and bioinformatics

Faculty

Publications

Seijo, Francisco, et al. “Contrasting state land and fire use policies condition fire regime seasonality and size in two Central Spain forest landscapes.” Land Use Policy 147 (2024): 107338.

Link to publication »

Branco, Pedro, Nico Döttling, and Akshayaram Srinivasan. “Two-Round Maliciously-Secure Oblivious Transfer with Optimal Rate.” Annual International Conference on the Theory and Applications of Cryptographic Techniques. Cham: Springer Nature Switzerland, 2024.

Link to publication »

Branco, Pedro, et al. “Black-box non-interactive zero knowledge from vector trapdoor hash.” Annual International Conference on the Theory and Applications of Cryptographic Techniques. Cham: Springer Nature Switzerland, 2025.

Link to publication »

Shah, Pranav NM, Ruben Sanchez-Garcia, and David I. Stuart. “TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms.” Biological Crystallography 81.2 (2025).

Link to publication »

Alsaadi, Aymen, et al. “Adaptive Protein Design Protocols and Middleware.” 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2025.

Link to publication »

Scientific Computing

Lines of Research

Applications

Faculty

Eduardo Castelló

Matteo Turilli

Pedro Branco

Rubén Sánchez-García

Publications

Seijo, Francisco, et al. “Contrasting state land and fire use policies condition fire regime seasonality and size in two Central Spain forest landscapes.” Land Use Policy 147 (2024): 107338.

Branco, Pedro, Nico Döttling, and Akshayaram Srinivasan. “Two-Round Maliciously-Secure Oblivious Transfer with Optimal Rate.” Annual International Conference on the Theory and Applications of Cryptographic Techniques. Cham: Springer Nature Switzerland, 2024.

Branco, Pedro, et al. “Black-box non-interactive zero knowledge from vector trapdoor hash.” Annual International Conference on the Theory and Applications of Cryptographic Techniques. Cham: Springer Nature Switzerland, 2025.

Shah, Pranav NM, Ruben Sanchez-Garcia, and David I. Stuart. “TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms.” Biological Crystallography 81.2 (2025).

Alsaadi, Aymen, et al. “Adaptive Protein Design Protocols and Middleware.” 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2025.

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