Plamen Nikolov, PhD
Bio / Timeline
2024
Post-Doctoral Research Affiliate
@ Georgetown University Medical Center
Computational models of speech processing.
2018 - 2024
PhD in Neuroscience
@ Georgetown University
Thesis on Neural Dynamics of Speech. NIH T32 & TL1 Fellow.
2019
Research Fellow
@ Lawrence Livermore National Laboratory
ML framework for TBI recovery trajectories.
2014 - 2018
Research Engineer
@ Johns Hopkins University
Cardiac 'Digital Twins' and clinical trials for arrhythmia.
2011 - 2013
MS in Biomedical Engineering
@ Virginia Commonwealth University
Thesis on cognitive-visuomotor multitasking.
2008 - 2011
BS in Biomedical Engineering
@ Virginia Commonwealth University
Senior Capstone: Real-time BCI system.
Research & Projects
Neural Dynamics of Speech Perception & Production
2018 - 2024PhD Researcher @ Georgetown University
Investigation of computational models engaged during auditory and spoken word speech processing, with potential applications for neuroscience-based speech therapy.
- Developed methods for EEG image acquisition and decoding of very fast (millisecond range) neural dynamics involving significant motion artifacts.
- Developed multivariate analysis pipelines using HPC clusters to analyze large neural image datasets (cut preprocessing time from 3 weeks to 2 days).
- Led interdisciplinary team investigating evidence of internal inverse and forward models in the brain.
- Secured independent funding (NIH T32 & TL1) and led grant proposals from concept to award.
[Neuroscience][EEG][HPC][Signal Processing]
Connectome-Based ML for TBI Recovery
2019Research Fellow @ Lawrence Livermore National Laboratory
Built an end-to-end machine learning framework to model recovery trajectories in traumatic brain injury patients.
- Built connectome-based ML framework using 2-week post-injury fMRI from the TRACK-TBI cohort.
- Processed functional connectivity matrices to extract graph/network features.
- Trained elastic net, ridge, and gradient boosting models to predict Glasgow Outcome Scale-Extended (GOSE) at 3/6/12 months.
[Machine Learning][fMRI][Python][TBI]
Cardiac 'Digital Twins' & Arrhythmia Prediction
2014 - 2018Research Engineer @ Johns Hopkins University
Large-scale electrophysiology simulations to analyze and predict arrhythmia behavior using patient-specific models.
- Built MR-derived, patient-specific cardiac models and ran large-scale EP simulations.
- Delivered the first clinical trial using virtual heart EP simulations to inform ventricular tachycardia ablation planning (Nature Biomedical Engineering).
- Led two industry-academic partnerships to productionize finite-element solvers for biologically realistic heart models.
BCI & Real-Time Bio-Signal Processing
2008 - 2013Research Assistant / BS Capstone @ Virginia Commonwealth University
Design and implementation of non-invasive brain-computer interface technologies.
- Designed visual stimulus-based BCI system integrated within a real-time EEG pipeline.
- Wrote assembly-level firmware for deterministic timing and achieved four-class control for on-screen navigation.
- Developed algorithms for motor imagery BCI, validating timing determinism and signal integrity.
- Conducted empirical evaluations to optimize accuracy, false-activation rate, and user training time.
[BCI][EEG][Embedded Systems][Real-time]
Featured Talks
2024
Neural Dynamics of Speech Processing
Georgetown University Neuroscience Seminar • Washington, DC
Overview of my doctoral research on how the brain processes spoken language using rapid neural dynamics.
2019
Machine Learning for TBI Recovery
LLNL Summer Student Symposium • Livermore, CA
Presenting a framework for predicting recovery trajectories in TBI patients.
Publications
- Virtual-heart simulations to predict patient-specific risk of arrhythmiaNikolov P, Trayanova N, et al.Nature Biomedical Engineering • 2018
Blog
I write about neuroscience, AI, and machine learning. Check out my blog here.