Publication
ICML 2026 ML optimization paper
Co-first author on work analyzing failure modes in machine learning models under challenging training regimes and designing regime-specific optimization strategies.
Read arXivML Researcher & Engineer
Xiaokun (Ken) Zhong
I work on machine learning systems, scalable model evaluation, and reliable training methods, with research experience in scientific AI and optimization.
Research Engineer in ML Systems & Scientific AI.
Research with Dartmouth and Berkeley collaborators.
BSc Mathematics, Computer Science Track @ HKUST.
Highlights
Selected research signals
A compact view of the work most relevant to research collaborators and ML hiring teams.
ML Optimization
Reliable training under difficult regimes
Built second-order optimization pipelines in PyTorch to improve model accuracy and training stability across benchmark tasks.
ML Systems
Scalable experiment infrastructure
Built Slurm-based workflows for large ablation studies, reproducible evaluation, and model behavior analysis on HPC clusters.
News
Recent updates
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Our scientific ML paper was accepted to ICML 2026.
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Conducted scientific machine learning research with Dartmouth and Berkeley collaborators.
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Graduated from HKUST with a BSc in Mathematics, Computer Science Track.
Experience
Research and education
Research Engineer, ML Systems & Scientific AI
Dartmouth College ยท UC Berkeley collaborators
Feb 2025 - Present
Berkeley, CA
- Investigated failure modes and training instability in scientific AI systems under stiff PDE and other challenging regimes.
- Implemented second-order optimization pipelines in PyTorch and scaled reproducible experiments on Slurm-based HPC clusters.
Research Engineer, Medical Imaging AI
HKUST Undergraduate Research Program
Feb 2023 - May 2025
Hong Kong
- Developed deep learning workflows for MRI reconstruction and 3D volumetric modeling from sparse medical data.
- Improved Python and C++ data processing pipelines for faster experimentation on large imaging datasets.
BSc in Mathematics, Computer Science Track
Hong Kong University of Science & Technology
Sep 2022 - Jun 2026
Hong Kong
- Coursework includes differential equations, artificial intelligence, and probability theory.
- Visiting student in Computer Science at UC Berkeley from Dec 2025 to May 2026.
Selected Projects
Applied research and systems work
Regime-specific Optimization for Scientific ML
Optimization and evaluation pipelines for PINNs and related scientific AI systems under difficult training regimes.
PyTorchNewton-CGPINNsSlurm
Medical Imaging AI
Deep learning models and data pipelines for MRI reconstruction and 3D volumetric modeling from sparse medical data.
PythonC++Medical imagingDeep learning
Secure Network Tunneling & Transport Optimization
Cross-region networking system using TLS-based transport and system-level tuning for reliability and latency analysis.
LinuxTLSNetworkingSystems
Autonomous Robotics Vision System
Real-time computer vision and embedded control work for target acquisition in RoboMaster robotics.
OpenCVPythonC++STM32
Contact
Research collaborations and ML roles
I am interested in reliable AI systems, scalable model evaluation, and research engineering work, with applications in scientific machine learning and model behavior analysis.