Obada Al Zoubi

Machine Learning/Biomedical Research Scientist

I'm an applied machine learning researcher focusing on the biomedical domain and working across various domains from a single cell, protein design, and epigenomics modeling to brain imaging, BCI, and neurofeedback. I am generally interested in advancing science when there is an opportunity. I acquired a broad experience in many fields of biomedical research by working across several academic institutes and laboratories in the US, including MIT, Harvard, and Broad Institute.
I started my ML journey by developing biologically inspired ML-like spiking neural networks and liquid state machines. Later, I moved to develop ML solutions to model the Spatio-temporal patterns (EEG/fMRI/MRI/DTI) of the brain in addition to leveraging pattern recognition for disease diagnosis. Motivated by the challenges of tackling brain disorders (complexity+missing links), I worked on linking brain patterns to genomics data through multimodal machine learning to fill missing gaps. I recently moved to R&D in the industry to work on drug discovery using machine learning. However, I'm still collaborating with MIT/Harvard to link brain (DTI/fMRI/EEG) and genomics using ML/DL.
I love to learn new advancements in AI/ML, and I'm always looking for new ways to improve my skills and connect with people.
Areas of interest: Geometric Deep Learning, Protein Design using AI, Generative Models, Transformers, Learning from a few shots, Brain-Cognitive modeling.
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Expertise

Machine Learning and Deep Learning

DL and ML including graph neural network, transformers, attention mechanisims, single shot learning.

Artificial Intelligence

Heuristic methods, pathfinding, and game theory for understanding human behaviors.

Cloud Computing

Some of my wrok aims at implementing large-scale pipelines using high-performance computing and cloud computing like AWS.

Scientific Software Engineering

Long-term experience in building, designing and programming algorithms for scientific purposes especially for biomedical domain.

Drug Design and Discovery

Protein Design, Target Discovery, Activity Modeling, Efficacy & Safety, Drug Modeling, Gene Editing Modeling

Computational Genetics

I'm working on linking Genetics with Brain Imaging data like fMRI.

Signal Processing

Signal processing methods for understanding and analyzing human-subject data.

Brain & Neurosceince

My interest is focused on understanding the mechanisms behind EEG and BOLD signal in the brain. I have long-term experience in analyzing resting-state EEG-fMRI data using data-driven methods.

Skills

Python
90%
R
90%
Matlab
95%
Tensorflow/Keras
95%
AFNI/FSL/FreeSurfer/Nilearn (fMRI/MRI)
90%
EEGLAB/MNE(EEG)
90%
C++
45%

Work Experience

2021 - Present

Sr. Data Scientist

Omega Therapeutics

Cambridge, USA

2020 - Present

Research Associate

Broad Institute

Boston, USA

2021 - Present

Research Associate

Harvard Medical School

Boston, USA

2020 - 2021

Research Fellow

Harvard Medical School

Boston, USA

2020 - 2021

Research Associate

MIT - Computer Science and Artificial Intelligence Laboratory

Boston, USA

2020 - 2021

Postdoc Fellow

McLean Hospital - Neurogenomics and Translational Bioinformatics Laboratory

Boston, USA

2019 - 2020

Post-doc associate

Laureate Institute for Brain Research

OK, USA

2019 - 2020

Instructor of Advanced Machine Learning

The University of Oklahoma

OK, USA

2016 - 2019

Research Assistant

Laureate Institute for Brain Research

OK, USA

2013 - 2015

Research Assistant

American University of Beirut

Beirut, Lebanon

Education

2016-2019

Ph.D. in Electrical and Computer Engineering

The University of Oklahoma

Norman, US

2013-2016

Master of Electrical and Computer Engineering

American University of Beirut

Beirut, Lebanon

Scientific Contributions

Open Source Software

  • An Automatic Pipeline for EEG Artifact Reduction (APPEAR) [link].
  • Evolving Spiking Neural Networks (eSNNs) and Hierarchical Fusion Hierarchical Fusion eSNN (HFeSNN) [link] .
  • Other open soource codes are available through [link to Github].

    • Reviewer

  • IEEE Transactions on Neural Networks and Learning Systems.
  • Journal of Artificial Intelligence and Soft Computing Research.
  • IEEE Transactions on Biomedical Engineering.
  • IEEE Transactions on Neural Systems & Rehabilitation Engineering.
  • IEEE Journal of Biomedical and Health.
  • Scientific Reports.
  • Neuroimage.
  • Frontiers in Neuroscience.

    • Selected Publications

    [Singal Processing + Neuroimaging ]
    Mayeli, A.*, Al Zoubi, O.*, White, E.J. et al. Parieto-occipital ERP indicators of gut mechanosensation in humans. Nat Commun 14, 3398 (2023) [* co-first authors]

    [ML+Neuroimaging]
    Al Zoubi, Obada, et al. Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets Brain Connectivity 12.4 (2022): 348-361

    [ML+Neuroimaging]
    Al Zoubi, Obada, et al. "Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal." Journal of Neural Engineering 18.6 (2022): 066051.

    [ML+Neuroimaging]
    Al Zoubi, O., Ki Wong, C., Kuplicki, R. T., Yeh, H. W., Mayeli, A., Refai, H., ... & Bodurka, J. (2018). Predicting age from brain EEG signals–a machine learning approach. Frontiers in aging neuroscience, 10, 184.

    [ML]
    Al Zoubi, Obada, et al. "Hierarchical fusion evolving spiking neural network for adaptive learning." 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). IEEE, 2018.

    [AI]
    Al Zoubi, Obada, and Mariette Awad. "Dynamic Area Search with Shared Memory: A Meta-Framework to Improve Pathfinding Algorithms." 2018 International Conference on Innovations in Information Technology (IIT). IEEE, 2018.[Best Paper Award]

    [ML]
    Al Zoubi, O., Awad, M., & Kasabov, N. K. (2018). Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework. Artificial intelligence in medicine, 86, 1-8.

    [ML]
    Al Zoubi, Obada, and Mariette Awad. "Toward a continuous authentication system using a biologically inspired machine learning approach: a case study." Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019.[Best Poster Award]

    [Neuroimaging + Open source Software Development]
    Mayeli, A.*,Al Zoubi, O. *, et al. (2021). Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI. Journal of neural engineering, 18(4), 0460b4.[* co-first authors]

    [Congnitive Neuroscience]
    Al Zoubi, Obada, et al. "Taking the body off the mind: Decreased functional connectivity between somatomotor and default‐mode networks following Floatation‐REST." Human brain mapping 42.10 (2021): 3216-3227.

    [ML+Biology]
    Dalvie, S., Chatzinakos, C., Al Zoubi, O., Georgiadis, F., Lancashire, L., & Daskalakis, N. P. (2021). From genetics to systems biology of stress-related mental disorders. Neurobiology of Stress, 15, 100393.

    [Singal Processing+Congnitive Neuroscience]
    Al Zoubi, O., Mayeli, A., Tsuchiyagaito, A., Misaki, M., Zotev, V., Refai, H., ... & Bodurka, J. (2019). EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects. Frontiers in human neuroscience, 13, 56.

    [Singal Processing+Congnitive Neuroscience]
    Misaki, M., Tsuchiyagaito, A., Zoubi, O. A., Paulus, M., Bodurka, J., & Tulsa 1000 Investigators. (2020). Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention. NeuroImage: Clinical, 102244.

    [Singal Processing+Congnitive Neuroscience]
    Mayeli, A., Zoubi, O. A., Misaki, M., Stewart, J. L., Zotev, V., Luo, Q., ... & Refai, H. (2019). Integration of Simultaneous Resting-State EEG, fMRI, and Eye Tracker Methods to Determine and Verify EEG Vigilance Measure. arXiv preprint arXiv:1912.04975.

    Email
    obada.y.alzoubi@gmail.com