INTERVIEW

Can you describe your educational and professional background?

I am a Biomedical Engineer from Escuela Colombiana de Ingeniería Julio Garavito and Universidad del Rosario. During my undergraduate studies, I developed a strong interest in medical signal and image processing, which led me to present at the Annual Congress of the Colombian Association of Engineering Schools (ACOFI). Following graduation, I joined the Rehabilitation Engineering Research Group (SIRA), where I discovered my passion for control systems and robotics.

After completing my undergraduate degree, I gained clinical experience at Clínica Infantil Colsubsidio, a leading pediatric hospital in Colombia, where I became familiar with state-of-the-art diagnostic and therapeutic technologies. Subsequently, I explored the biomedical industry at LAS Electromedicina, one of Colombia’s largest distributors of ICU and imaging equipment.

Missing the academic research environment, I pursued a research-focused Master’s degree in Electronic and Computer Engineering at Universidad de los Andes. I received a full scholarship and served as a laboratory teaching assistant for Control Systems Analysis, further specializing in control theory and machine learning. My thesis research, which focused on biomechanical analysis through computer vision and Colombian Sign Language classification, received the award for Best Thesis Presentation.

I then returned to EEG signal processing, supporting my studies through a tutorship in the Master’s in Artificial Intelligence program at Universidad de los Andes and consulting projects with Belcorp on neurophysiological evaluation of consumer preferences. Currently, I have been awarded a doctoral fellowship with Neuronanotech to work on the in-vivo assessment of nanotechnologies for smart neuromodulation.

What prompted you to apply as a doctoral candidate within the Doctoral Network?

My decision to apply to the Doctoral Network began when my advisor presented me with this opportunity. Upon contacting Dr. Miguel Valencia to discuss the project in detail, I immediately recognized that it brought together all of my research interests in a uniquely coherent way. The project’s integration of neural signal processing, closed-loop control systems, and translational neurotechnology felt like a natural continuation of my academic trajectory—from my early work in biomedical signal processing and control systems, through my Master’s research combining computer vision with biomechanical analysis, to my recent experience in EEG-based neurophysiological assessment.

What struck me most during my discussions with Dr. Valencia was how closely the desired candidate profile aligned with my specific skill set and research vision. The project’s emphasis on intelligent neuromodulation requires precisely the interdisciplinary foundation I have developed: expertise in biosignal processing, control theory, machine learning, and a deep commitment to translating research into clinical applications. This convergence felt almost serendipitous.

Beyond the technical alignment, the Neuronanotech network’s collaborative, multi-institutional structure offers the ideal environment to develop systems with genuine clinical impact. The opportunity to work within a European doctoral network, accessing complementary expertise across neuroscience, nanotechnology, and clinical translation, represents exactly the training environment I need to improve my skills as a researcher.

What does your research consist on?

My research focuses on developing intelligent neuromodulation systems for Parkinson’s disease through the integration of nanotechnology-based neural interfaces and real-time closed-loop control. The overarching goal is to assess the suitability of nanostructured electrodes and ultra-sensitive spintronic magnetic sensors for restoring neural dynamics of motor control in animal models.

Currently, I am developing a closed-loop control system that acquires EEG signals in real-time, computes the instantaneous phase and energy of the beta band, and delivers phase-specific stimulation to counteract parkinsonian symptoms. This adaptive Deep Brain Stimulation (aDBS) approach aims to selectively modulate pathological beta oscillations—a hallmark of Parkinson’s disease—by synchronizing therapeutic stimulation with the ongoing neural dynamics.

The broader project encompasses several key objectives: (1) in-vivo assessment of the long-term biocompatibility of novel sensing and stimulation interfaces, (2) development of advanced signal processing techniques to enhance the sensitivity and noise robustness of multimodal recordings (electrophysiological and magnetic), (3) characterization of the relationship between magnetic and electrophysiological measurements, and (4) functional validation of the smart neuromodulation framework in freely-moving animal models of Parkinson’s disease.

This work integrates expertise from CIMA’s Neurophysiological Monitoring and Control Laboratory and the Cajal Institute’s Neurophysiology and Synaptic Plasticity Laboratory, with planned secondments at CEA for nanotechnology integration and BSL for implantable prototype design.

More information on NeuroNanotech

This Programme is supported by the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks. Grant agreement ID: 101169352.