Jae Moon, Ph.D.

Computational Neuroscientist, Biomedical Data Scientist, AI/ML Research Scientist

About

With a background in Life Sciences & Neurosciences from Queen’s University and a Ph.D. in Biomedical Engineering from the University of Toronto, I delve into the fascinating intersection of artificial and human intelligence.

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Research EThos

< Applied AI research Tangible + proximal impact. >

My research ethos centers on applied work with immediate, tangible impacts. I strive to create solutions that resonate in real-world scenarios.

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Highlights

Data & Algorithm Quality and Integrity Evangelist

< Accountable data practices Reliable outcomes for AI >
| Human-guided parameterization < Data labelling policies |

At HealthTech Connex (HTC+NeuroCatch), I enhanced Health Canada + FDA-approved neurotech devices, creating innovative and patent-pending algorithms for electrophysiological time-series data analysis.

> Of note, I spearheaded a patient-oriented healthcare research initiative targeting the amelioration of detection rate bottlenecks by advocating for accountable data management practices for optimal patient outcomes.
> Developed a variational inference framework for the data to guide decision making, proving more conducive to noise-robustness, generalizability, and reliability.

Neuro-engineering Research Scientist

At the University of Toronto’s PRISM Lab, mentored by Dr. Tom Chau, I developed an innovative brain-computer interface for decoding speech-related thoughts in real-time. This work honed my skills in signal processing, machine learning, AI, and statistics, culminating in notable awards, publications, and a distinguished thesis defense.

Data Research Scientist

• Zero/few-shot anomaly detection and segmentation algorithm for heterogeneous time-series and image data.
• Reducing burden for edge-computing and real-time operations

Expanding upon this, I have been curently researching similar methods for radiological, biomedical time-series datasets, and other unstructured data, with an objective of guiding/grounding AI development.

More detailed portfolio page coming soon!

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AWARDS & gRANTS

• JAMES F. CROTHERS FELLOWSHIP for Peripheral Nerve Damage
• TORONTO REHABILITATION INSTITUTE SCHOLARSHIP
• WHIPPER-WATSON SCHOLARSHIP (Holland Bloorview)
• INSTITUTE FOR TECHNOLOGY IN HEALTHCARE THESIS AWARD
• HANNAH FARKAS-HIMSLEY AND ALEXANDER HIMSELY MEMORIAL PRIZE
• KITE-TD FELLOWSHIP
• MARGARET & HOWARD GAMBLE GRANT for Cerebral Palsy
• LEE KYUNGSUK SCHOLARSHIP
• DAAD RISE RESEARCH SCHOLARSHIP
• QUEEN’S UNIVERSITY ACADEMIC EXCELLENCE AWARD

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Google Scholar

Highlighted Publications

^{Real-time machine learning for thought-decoding using cross-modal linguistic representations}

^{Connectivity-based approaches of understanding language Networks in the brain}

^{Machine learning approaches for achieving comparative understanding of cross-modal linguistic cognition}

^{Bone-conduction reduces VR sickness}

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• LinkedIn

_{Feel free to connect with me on LinkedIn—just mention that you came across this bio.}