Biosignal Research Engineer | InteraXon Inc, Toronto ON

Mar 2021 - Current

In March 2021, I joined InteraXon, a neurotech company, as a full-time Biosignal Research Engineer. My responsibilities include:

1. Developing advanced machine learning and deep learning algorithms for processing EEG and other biosignals for use in meditation and sleep
2. Planning experiments, collecting and analyzing data for in-house studies
3. Contributing to the development of internal research tools

Bioinformatics Analyst | Massachusetts General Hospital and Harvard Medical School, Boston MA

Aug 2018 - Mar 2021

Afer the end of my graduate studies, I worked at Massachusetts General Hospital as a Bioinformatics Analyst under the supervision of Alice Lam Md, PhD.

The primary objective of my work was to develop Artificial Intelligence (AI) tools for the automated analysis of EEG signals in normal and epileptic patients. I was involved in the publication of 5 peer-reviewed papers, including 3 first-author papers, which can be found here ,here and here.

My work included:

1. Developing deep learning algorithms for the automated sleep staging of long-term scalp EEG recordings.
2. Developing deep learning algorithms for the detection of interictal epileptiform discharges from scalp EEG and intracranial EEG.
3. Designing and building Graphical User Interfaces for annotating EEG recordings.

Graduate Student Researcher | Massachusetts General Hospital and Harvard Medical School, Boston MA

Jan 2017 - Feb 2018

I spent the second year of my graduate studies as an exchange student at Harvard Medical School after receiving a fellowship from the Bertarelli Program in Translational Neuroscience and Neuroengineering. I worked as a graduate student researcher at the Cortical Physiology Lab (CashLab) situated at Massachusetts General Hospital, and headed by Sydney Cash Md, PhD.

During that year I:

1. Developed machine learning algorithms for monitoring the electrical activity of deep neural structures using scalp EEG and intracranial EEG.
2. Tested the reliability of novel PEDOT microelectrodes for use in intraoperative monitoring. Responsibilities included setting up the recording system in the Operating Room and post-surgery analysis of the recordings.

Research and Development Intern | Cardiodiagnostics, Beirut, Lebanon

Oct 2016 - Jan 2017

Before the start of my second year of graduate studies, I did an internship at Cardiodiagnostics SAL, a MedTech company specialized in providing cloud-based cardiac monitoring solutions.

Using Electrocardiogram (ECG) signals from Cardiodiagnostic's proprietary database and the MIT-BIH arrhythmia database, I:

1. Developed automated machine learning algorithms for analyzing R-R intervals and the detection of Atrial Fibrillation, with sensitivities and specificities above 95%.
2. Enhanced previous algorithms used for premature ventricular contractions (PVC), yielding a sensitivity of 92% and a precision of 81%.
3. Enhanced previous algorithms for QRS detection, acheiving sensitivities and specificities above 99%.

Summer Intern | Leman Micro Devices, Lausanne, Switzerland

Jul 2016 - Sep 2016

Following the first year of my graduate studies, I did an internship at Leman Micro Devices (LMD), a MedTech startup located in EPFL University's innovation park.

Using LMD's V-Sensor, a sensor-module integrated into mobile smartphones and that can measure your vital signs, I studied the potential applications of combining simultaneous measurements of Electrocardiogram (ECG), Seismocardiogram (SCG), and Photoplethysmogram (PPG).

After surveying the literature for these potential applications, I:

1. Wrote custom scripts using Matlab to automatically process and annotate ECG, SCG, and PPG signals.
2. Using these annotations, calculated several measures of heart health such as systolic time intervals and diastolic time intervals.

Industrial Trainee | Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia

May 2014 - Aug 2014

Following my 3rd year of undergraduate studies, I did an internship at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Brisbane, Australia, under the supervision of Professor Raja Jurdak.

During that internship, I analyzed a Twitter dataset with more than six million geotagged tweets posted in Australia to assess the reliability of Twitter as a proxy for studying human mobility patterns. I:

1. Characterized the movement patterns of individuals by analyzing their sequences of geotagged tweets and calculating different mobility features.
2. Used statistical modeling tools to capture interesting trends in human mobility.

My work contributed to the publication of a peer-reviewed article with over 300 citations to date, and which can be found here