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Posts
Digital Trends report our Brain-NET research
Published:
A.I. can tell if you’re a good surgeon just by scanning your brain
Rensselaer News report our Brain-NET research
Published:
Brain-NET Accurately Predicts Surgeon Certification Scores Based on Neuroimaging Data
Certified in SQL language
Published:
Certified in SQL language by IBM
Presentation at UB med by Jessica
Published:
Jessica presented the poster by Yuanyuan Gao in UB med student poster session
Dr Xavier Intes presented the poster by Yuanyuan Gao in SPIE
Published:
Dr Xavier Intes presented the poster by Yuanyuan Gao in SPIE
portfolio
Project 1 (PhD thesis): Evaluating brain activation changes during learning via functional near- infrared spectroscopy (fNIRS)
Published:
Oxy-hemoglobin level before and after brain stimulation
Project 2: Predicting surgical skills from fNIRS via deep learning models
Published:
Predicting surgical skills from fNIRS via deep learning models
Project 3: Predicting learning curve characteristics via machine learning approaches
Published:
Predicting learning curve characteristics via machine learning approaches
Project 4: Literature review on neuroimaging studies
Published:
Literature review on neuroimaging studies
Project 5: Removing motion artifacts in fNIRS data via deep learning approach
Published:
Removing motion artifacts
Project 6 (master stage): 90kg Palletizing Robot Program
Published:
- Mechanical structure design and motion simulation analysis of wrist, big arm, rear arm and waist part using Solidworks software; All engineering drawings by CAXA software;
- Debug the system using PMAC motion control card;
- Responsible for motor, rail, belt wheels calculation, selection and procurement.
Project 7 (master stage): Automatic Pharmacy Storage Systems program
Published:
Optimization
publications
A Machine Learning approach to predict surgical learning curves
Published in Surgery, 2019
This paper is about the learning curve prediction.
Recommended citation: Gao, Y., Kruger, U., Schwaitzberg, S., Intes, X., De, S. (2019). "A Machine Learning approach to predict surgical learning curves." Surgery. 167(2), 321-327.DOI: https://doi.org/10.1016/j.surg.2019.10.008. https://www.sciencedirect.com/science/article/abs/pii/S0039606019307299
Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task
Published in IEEE TBME, 2020
This paper is about predicting surgical skill by fNIRS data via deep learning.
Recommended citation: Gao, Y., Yan, P., Kruger, U.,Cavuoto, L., Schwaitzberg, S., De, S., Intes, X. (2020). "Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task" IEEE TBME. Early Access. DOI: 10.1109/TBME.2020.3014299.
A comprehensive review of experimental neuroimaging studies of the effect of transcranial electrical stimulation on human motor skills
Published in Frontiers in Neuroscience, 2020
This paper is review paper of experimental neuroimaging studies of the effect of transcranial electrical stimulation on human motor skills.
Recommended citation: Gao, Y., Cavuoto, L., Schwaitzberg, S., Norfleet, J., Intes, X., De, S. (2020). "The effects of transcranial electrical stimulation on human motor functions: A comprehensive review of functional neuroimaging studies" Frontiers in Neuroscience. 14:744 (2020). DOI: https://doi.org/10.3389/fnins.2020.00744.
Deep Learning-based Motion Artifact Removal in Functional Near-Infrared Spectroscopy (fNIRS)
Published in NeuroImage, 2020
This paper is about removing motion artifacts in fNIRS data via denoising autoencoder.
Recommended citation: Gao, Y., Chao, H., Cavuoto, L., Yan, P., Kruger, U., Norfleet, J., Makled, B., Schwaitzberg, S., De, S., Intes, X. (2020). "Deep Learning-based Motion Artifact Removal in Functional Near-Infrared Spectroscopy (fNIRS)" Neuroimage. Under review.
talks
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Published:
teaching
Thermal and Fluid Engineering II (MANE 4010)
Teaching Assistant, Rensselear Polytechnic Institute, Department of Mechanical Aerospace, and Nuclear Engineering, 2015
I delivered TA sections to classes up to 50 students using traditional lecture notes and Power Point presentations.
Modeling and Analysis of Uncertainty (ENGR 2600)
Teaching Assistant, Rensselear Polytechnic Institute, School of Engineering, 2016
I delivered TA sections to classes up to 50 students using traditional lecture notes and Power Point presentations.Download TA session slides here
Modeling and Analysis of Uncertainty (ENGR 2600)
Teaching Assistant, Rensselear Polytechnic Institute, School of Engineering, 2017
I delivered TA sections to classes up to 50 students using traditional lecture notes and Power Point presentations.
Undergraduate Research Program (URP)
Mentoring, Rensselear Polytechnic Institute, Department of Mechanical Aerospace, and Nuclear Engineering, 2018
I was an undergraduate mentor in Undergraduate Research Program (URP) at RPI for one spring semester and the following summer semester. I mentored four undergraduate students from sophomores to juniors. I designed the independent research topic for each student and led weekly group meetings as well as one-on-one meetings. I guided them to learn literature review, defining the specific aims and hypothesis, setting up models, coding, scientific report writing and presentation.
Modeling of Biomedical Systems (BMED 4200)
Guest Lecture, Rensselear Polytechnic Institute, Department of Biomedical Engineering, 2018
At RPI I have continued to hone my teaching skills by giving guest lectures in Modeling of Biomedical Systems (BMED 4200). These invited lectures afforded me the opportunity to teach classes by introducing real lab research problems into the classroom and present how they were solved by the theories and models learned from the class.Download slides here
Thermal and Fluid Engineering I (ENGR 2250)
Teaching Assistant, Rensselear Polytechnic Institute, School of Engineering, 2018
I delivered TA sections to classes up to 50 students using traditional lecture notes and Power Point presentations.