CV

General Information

Full Name Chang-Le (Charles) Chen
Position Postdoctoral Researcher, UC Berkeley
Location Berkeley, CA, USA
Languages Mandarin (native), English (fluent)

Education

  • 2021–2026
    PhD in Bioengineering
    University of Pittsburgh, PA, USA
    • Developing harmonization methods for neuroimaging data to ensure quality consistency across multi-site datasets.
    • Developing diffusion-derived indices to reflect cerebral fluid dynamics as a marker for dementia.
    • Developing multimodal brain age prediction models and normative frameworks to assess neurodegeneration risk.
    • Developing representation learning approaches to learn spatiotemporal patterns of the aging brain.
    • Instructing students in neuroimaging and statistical analysis and mentoring undergraduates in research methods.
    • GPA- 4.0/4.0
  • 2015–2017
    MSc in Brain and Mind Sciences
    National Taiwan University, Taipei, Taiwan
    • Performed diffusion and structural MRI analyses for pattern recognition of brain aging.
    • Conducted neuroimaging research for neuroanatomical alterations induced by mental training.
    • Implemented machine learning methods for medical image analysis.
    • GPA- 4.04/4.30
  • 2010–2014
    BSc in Biomedical Imaging & Radiological Sciences
    National Yang Ming Chiao Tung University, Taipei, Taiwan
    • Biomedical engineering, physics, and medicine.
    • GPA- 3.77/4.30

Experience

  • 2026–Present
    Postdoctoral Researcher
    University of California, Berkeley, CA, USA
  • 2021–2026
    Graduate Researcher
    University of Pittsburgh, PA, USA
    • Developing imaging-derived measures for reflecting biological processes.
    • Conducting neuroimaging research for investigating multiple neurodegenerative diseases and psychiatric disorders
    • Studying deep learning approaches for clinical research.
  • 2017–2021
    Research Engineer
    Molecular Imaging Center, National Taiwan University, Taipei, Taiwan
    • Conducted neuroimaging analyses for diffusion MRI, structural MRI, fMRI, etc.
    • Developed analytic frameworks of imaging processing and automatic pipelines for brain age estimation.
    • Performed statistical and data analyses, including regression, multivariate analysis, machine learning, etc.
    • Designed and executed clinical experiments.
    • Gave lectures on neuroimaging analysis and assisted in supervising graduate students.
  • 2018
    Trainee
    Taiwan Artificial Intelligence Academy, Taipei, Taiwan
    • Learned theory and implementation of modern machine learning and deep learning.
    • Performed instance segmentation by using mask R-CNN for individual cell tracing.
  • 2014-2015
    Second Lieutenant
    Medical Affairs Bureau, Ministry of National Defense, Taiwan
    • 11 months of active duty enlisted military service in the Army branch of the Taiwan Armed Forces.

Awards

  • 2022
    Magna Cum Laude Award
    International Society for Magnetic Resonance in Medicine (ISMRM)
    • Virtual presentation at 2022 Joint Annual Meeting ISMRM-ESMRMB (London, UK)
    • Topic- “Microstructural Deviation in Parkinson’s Disease and Multiple System Atrophy Detected by Spatial Normative Models”
  • 2020
    Magna Cum Laude Award
    International Society for Magnetic Resonance in Medicine (ISMRM)
    • Oral presentation at 2020 ISMRM Annual Meeting (Virtual meeting)
    • Topic- “Multiple Brain Age Metrics Reveal Premature Brain Aging Network and Association with Clinical Factors in Schizophrenia”
    • This study was also awarded the Best Paper Award by the ISMRM Psychiatric MR Spectroscopy & Imaging Study Group.
  • 2019
    Summa Cum Laude Award
    International Society for Magnetic Resonance in Medicine (ISMRM)
    • Oral presentation at 2019 ISMRM Annual Meeting (Canada)
    • Topic- “Advanced Brain Aging in Patients with Right Mesial Temporal Lobe Epilepsy- A Machine Learning Approach Based on White Matter Tract Integrity”

Technical Skills

  • Neuroscience
    • Neuroimaging Analysis (including voxel-based and surface-based morphometries for structural MRI data, tractography and tract-specific analysis for diffusion MRI data, fMRI connectivity analysis, etc.)
    • Clinical Experiment Design and Execution (executed more than eight research projects)
  • Deep Learning
    • Computer Vision (CNN, GAN, VAE, ViT, self-supervised learning, etc.)
    • Natural Language Processing (Embedding, attention-based model, LLM, etc.)
    • Reinforcement Learning (deep q learning, etc.)
    • Time Series Analysis (RNN, LSTM, transformer, etc.)
  • Data Science
    • Medical Image Analysis (tumor segmentation, diffusion MRI image denoising, head motion correction, etc.)
    • Machine Learning (supervised classification and regression, unsupervised methods, optimization, etc.)
    • Data Visualization (neuroimaging visualization, statistical visualization, and customized digital artworks)
  • Statistics
    • Multivariate Analysis (principal component analysis, canonical correlation, multivariate regression, etc.)
    • Structural Equation Modeling (factor analysis, path analysis, and model evaluation)
    • Survival Analysis (Kaplan-Meier method and Cox proportional hazard regression)
    • Longitudinal Analysis (linear mixed effect model, latent growth curve modeling, etc.)
    • Bayesian Statistics (empirical Bayes estimation, etc.)
  • Computer Programming
    • Python (Numpy, Pandas, Scipy, SKlearn, Keras, Matplotlib, PyTorch, Tensorflow, etc.)
    • Matlab (statistics & machine learning, visualization, graphical user interface design, exploratory data analysis, etc.)
    • R (statistical modeling, lavaan, ggplot2, etc.)

Academic Interests

  • Aging of the human brain
  • Pathophysiological mechanisms of neurodegenerative diseases and psychiatric disorders
  • Digital twins

Other Interests

  • Hobbies- cooking, travel, music.