Gaotian Mi
Baltimore,MD
Summary
Accomplished Imagery Analyst with a proven track record at Johns Hopkins University, adept in MATLAB and Python. Spearheaded advanced rsfMRI data preprocessing and analysis, enhancing understanding of neural mechanisms in health and disease. Demonstrated exceptional analytical skills and teamwork, achieving a 90% accuracy in ECG signal classification. Recognized for innovative research with a National Third Prize.
Overview
4
4
years of professional experience
Work History
Imagery Analyst
Johns Hopkins University
Baltimore, MD
09.2024 - Current
- Conducted comprehensive preprocessing of rsfMRI data using the CONN Toolbox, including realignment, slice-timing correction, normalization, smoothing, and denoising, ensuring data quality for accurate analysis.
- Applied Independent Component Analysis (ICA) to extract resting-state networks, correlating the results with standard network templates to identify key neural networks and their functional connectivity patterns.
- Generated brain ICA spatial maps, correlation matrices, and dice coefficient matrices to visually represent neural network organization, connectivity changes, and functional connectivity patterns.
- Demonstrated that after the treatment in hydrocephalus patients, the network connectivity improves in the Default Mode Network, Dorsal Attention Network, and Language Network.
Image Analyst
University of Dundee
07.2022 - 07.2023
- Utilized the PRISMA framework to systematically search and select literature on SPECT imaging related to cerebral blood flow abnormalities in depression patients.
- Assessed the quality of selected studies, extracted key data, and conducted a meta-analysis using GingerALE to identify significant brain regions associated with depressive disorders, enhancing the understanding of the neural mechanisms underlying depression.
- Reported the most consistent and significant regions in depressive patients: the insula, the anterior cingulate cortex, and the thalamus.
Leader
Northeastern University (China)
11.2020 - 09.2022
- Developed a flexible ECG sensor utilizing liquid metal electrodes to enhance signal acquisition quality.
- Designed a CNN deep learning model with seven convolutional layers and four pooling layers, leveraging temporal and spatial convolution, batch normalization, and Dropout to classify ECG signals into five categories, with an accuracy of 90%.
- Designed an application that receives ECG signals to calculate heart rate and determine if the individual has cardiovascular diseases.
- Listed as the second co-author of the review paper, "Intelligent Wearable Devices Based on Nanomaterials and Nanostructures for Healthcare" (accepted by Nanoscale, IF: 8.307), citation: 47.
- Won the National Third Prize in the Jianlong Steel Computer Design Competition.
Project Intern
Neusoft Corporation
Shenyang, China
06.2021 - 07.2021
- Assisted in establishing a medical data display system, including searching for medical data on carbon dioxide, simulating in Qt, and transmitting the data via serial communication.
Education
M.S.E. - Biomedical Engineering
Johns Hopkins University, Whiting School of Engineering
Baltimore, MD05.2025
Bachelor's Degree - Biomedical Engineering (Chinese-Foreign Cooperation)
Northeastern University (NEU)
Shenyang, China06.2023
Skills
- MATLAB
- Python
- C
- SolidWorks
- 3D Slicer
- GingerALE
Languages
English
Full Professional
Spanish
Elementary
Timeline
Imagery Analyst
Johns Hopkins University
09.2024 - Current
Image Analyst
University of Dundee
07.2022 - 07.2023
Project Intern
Neusoft Corporation
06.2021 - 07.2021
Leader
Northeastern University (China)
11.2020 - 09.2022
M.S.E. - Biomedical Engineering
Johns Hopkins University, Whiting School of Engineering
Bachelor's Degree - Biomedical Engineering (Chinese-Foreign Cooperation)
Northeastern University (NEU)
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