Emmanuel Lujan, PhD

🏛︎ Research Scientist at MIT’s Computer Science and Artificial Intelligence Laboratory and member of the Julia Lab.

⚛ Research interests lie at the intersection of artificial intelligence, scientific simulation, and high-level high-performance programming—with applications in computational linear algebra and materials science.

✉ eljn@mit.edu

Projects

Julia & AI to generate the connective tissue of linear algebra The DARPA–MIT SmartSolve project seeks to advance AI‑guided algorithmic discovery and accelerate computations by generating improved strategies for algorithm and architecture selection. Our current work targets challenges in computational linear algebra, addressing the increasing complexity of choosing efficient solvers, data formats, precision settings, and hardware resources for structurally diverse matrices—an area where conventional approaches leave significant room for improvement. Our methodology involves building a comprehensive performance database through systematic benchmarking and applying automated Pareto analysis to reveal optimal trade‑offs between accuracy and speed. This database serves as the foundation for a data‑driven model that synthesizes dispatch strategies tailored for high‑performance linear algebra software.

Julia & AI to scale the chemical diversity of machine learning interatomic potentials The Center for the Exascale Simulation of Materials in Extreme Environments (CESMIX) seeks to advance the state-of-the-art in predictive simulation by connecting quantum and molecular simulations of materials with state-of-the-art programming languages, compiler technologies, and software performance engineering tools, underpinned by statistical inference and uncertainty quantification. In particular, we work on enhancing the learning of interatomic potentials through novel algorithms and tooling for large-scale atomistic data curation, leveraging recent advances in Julia for high-performance computing abstractions and scientific machine learning.

5G NB-IoT to connect sensors in extreme environments One of the IoT challenges is providing communication support to an increasing number of sensors. In recent years, a narrowband radio technology has emerged to address this situation: Narrowband Internet of Things (NB-IoT), which is an integral part of 5G. Despite the efforts, massive connectivity becomes particularly demanding in extreme coverage scenarios such as underground or deep inside buildings sites. We use novel computational models and simulations to enhance the future design of base station software, favoring connection support in extreme environments.

Modeling & simulation to unravel electroporation in cancer treatments Computational/mathematical models are used to study electroporation based treatments applied to solid tumors, e.g. irreversible electroporation, electrochemotherapy, and gene electrotransfer. We seek optimal combination of electrode geometries, field intensity, pulse length, heat distribution and conductivity to induce neoplastic cells death of ​​primary tumor preserving most of healthy tissue. These types of therapies present high efficacy and low side effects, they could represent an alternative to traditional methods such surgery, radiotherapy or chemotherapy.

Exploring tumor growth through diffusion–convection modeling Computational oncology, which encompasses any form of computer-based modeling related to tumor biology and cancer therapy, have become target of numerous studies. In particular, mathematical models based on reaction-diffusion equations describing tumor proliferation and invasion into peripheral host tissue have proved to be of clinical relevance. In this context, we described the micro-environmental influence on micro-tumor infiltration patterns through in-silico/in-vitro experimentation. In order to simulate the core growth and peripheral tumor cell infiltration, considering a benign and a malignant stages, we implemented a reaction-diffusion based model, with spatially variable diffusion coefficient, into a three-dimensional domain. We hope to shed light in current therapy optimization strategies.

Publications

Pre-prints

Publications in peer-reviewed journals and conferences

Presentations, posters, and abstracts

CC BY-SA 4.0 Emmanuel LUJAN. Last modified: September 04, 2025. Website built with Franklin.jl and the Julia programming language.