Jacky Li Adjunct Lecturer & M.S.E.E. Candidate

Edge AI · Embedded Systems · Software Engineer

Jacky Li

Adjunct Lecturer & M.S.E.E. Candidate at Cal Poly Pomona. I build on-device ML, embedded firmware, real-time control, and AI-enabled user-facing applications — with a focus on neuromorphic vision and edge-efficient inference.

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Open to research collaborations and industry partnerships in edge AI, embedded systems, and applied ML. Reach me at jackydli95@gmail.com.

Interactive Console

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Featured Projects

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MemoryQC-Edge: Label-Efficient On-Device QC Agent for MRI Segmentation

Research (first author) · MICCAI 2026 MedAgent workshop, accepted

Under domain shift a segmenter can miss a tumor confidently, so QC built on uncertainty inherits a blind spot. This agent learns a failure detector over a compact 29-dimensional signature of the segmenter's output, flags likely failures for clinician review, and recalibrates online from the cases that come back reviewed, without touching the segmenter's weights. Accepted at the MedAgent workshop (MICCAI 2026).

Medical Imaging Edge AI Runtime QC Domain Shift

EvCorrupt-Bench: Runtime Corruption Detection for Event Cameras

Research (first author)

A benchmark and monitor study for detecting sensor corruption in event-camera streams at runtime: six physically modeled failure modes (leak-event hot pixels, mains flicker, activity bursts, dead regions, readout congestion, polarity faults) injected as timed episodes into 42 recordings across four sensor families, with leakage-safe injection and false-alarm-budgeted evaluation. Under revision for resubmission.

PyTorch Event Cameras SNN Benchmarking

Recent Experience

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Adjunct Lecturer, ECE 3301 / ECE 3301L Introduction to Microcontrollers

California State Polytechnic University, Pomona | Jan 2026 - Present

Teach upper-division lecture and lab courses on PIC18F microcontrollers, embedded C/assembly, and hardware interfacing.

Course Support, Machine Learning Foundations

Break Through Tech at Cornell Tech | May 2026 - Present

Instructional support for a machine learning foundations program: live labs, office hours, grading, and capstone mentorship.