Emmanuel LUJAN

Emmanuel LUJAN, Ph.D. Eng.

Postdoctoral Associate @ Julia Lab
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology

Projects

Models, simulations, scientific machine learning, and Julia programming Scientific machine learning is a burgeoning discipline which encompasses classical scientific computing, typically driven by differential equation models, and novel machine learning techniques. We work on accelerating the training of physics-informed neural networks, automatically discretize partial differential equations using neural networks and long-established numerical methods, and its application in different domains such as well extraction models and molecular dynamics. These contributions are implemented in Julia, a high-level, high-performance, dynamic programming language strong in numerical analysis and computational science.

Models and simulations in 5G Narrowband-IoT 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 of the efforts, massive connectivity become particularly demanding in extreme coverage scenarios such as underground or deep inside buildings sites. Here we use novel computational models and simulations to address those issues. We expect to influence future base station software design and implementation, favoring connection support under extreme environments.

Models and simulations in Electroporation Treatments In this line of work, computational/mathematical models are used for studying electroporation (EP) based treatments applied to solid tumors, e.g. irreversible electroporation (IRE), electrochemotherapy (ECT), and gene electrotransfer (GET). 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 kind of therapies present high efficacy and low side effects, they could represent an alternative to traditional methods such surgery, radiotherapy or chemotherapy.

Models and simulations in Tumor Growth 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

Upcoming Publications

Publications in Peer-Reviewed Journals

Publications in Peer-Reviewed Conferences

Abstracts and Posters

© Emmanuel LUJAN. Last modified: December 17, 2021. Website built with Franklin.jl and the Julia programming language.