Yixiang Gao

I am a Ph.D candidate from University of Missouri - Columbia under the supervision of Dr. Gui DeSouza. My research is about detect, quantify and mitigate confounding factors in machine learning models with a specific focus on the applications of voice pathology. I am familiar with the topics such as computer vision, robotics and of the development of large lange models and vision-language models.

Bios (CV)

2017 - Current

Ph.D. Candidate Electrical and Computer Engineering

Vision-Guided and Intelligent Robotics Labotory

University of Missouri - Columbia

Supervisor: Dr. Gui N. DeSouza

Research: AI, Machine Learning, Computer Vision, Robotics, Voice Pathology

Thesis: Confounded Predictions in Machine Learning - Detect, quantify and mitigate confounding bias in machine learning applications. From medical predictive models to general deep learning models.

2014 - 2017

B.S. Computer Engineering & B.S. Electrical Engineering

University of Missouri - Columbia

Publications (Google Scholar)

• COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism

  The IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024

  R. Farag, P. Upadhyay, Y. Gao, J. Demby’s, K. G. Montoya, S. M. Ali Tousi, G. Omotara, G. N. DeSouza

  [Accepted]

• Voice sEMG classification of sentences for vocal fatigue detection using GA-SVM for confounder

  13th International Conference on Voice Physiology and Biomechanics (ICVPB), 2024

  Y. Gao, G. N. DeSouza, M. Berardi, and M. Dietrich

  [Accepted]

• Removal of Confounding Factors using GA-SVM Feature Adaptation: Application on Detection of Vocal Fatigue thru sEMG Classification

  IEEE Congress on Evolutionary Computation (CEC), 2023

  Y. Gao, M. Berardi, M. Dietrich, and G. N. DeSouza

• Classification of vocal fatigue using neck semg with leave-one-subject-out testing

  The 14th Advances in Quantitative Laryngology, Voice and Speech Research (AQL), 2021

  Y. Gao, M. Dietrich, and G. N. DeSouza

• Explore voice production variability through neck semg clustering - challenge for accurate labeling of vocal fatigue

  The 14th Advances in Quantitative Laryngology, Voice and Speech Research (AQL), 2021

  Y. Gao, M. Dietrich, and G. N. DeSouza

• Classification of vocal fatigue using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

  Applied Sciences, 2021

  Y. Gao, M. Dietrich, and G. N. DeSouza

• Object Detection and Pose Estimation using CNN in Embedded Hardware for Assistive Technology

  IEEE Symposium Series on Computational Intelligence (SSCI), 2019

  J. Demby’s, Y. Gao, A. Shafiekhani, and G. N. DeSouza

• A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network

  IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019

  J. Demby’s, Y. Gao and G. N. DeSouza

• Classification of vocal gestures extracted from quasi-daily sentences to detect vocal fatigue

  The 13th Advances in Quantitative Laryngology, Voice and Speech Research (AQL), 2019

  Y. Gao, M. Dietrich, M. Pfeiffier, A. Walker, and G. N. DeSouza

• Classification of sEMG Signals for the Detection of Vocal Fatigue based on VFI Scores

  International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018

  Y. Gao, M. Dietrich, M. Pfeiffer and G. N. DeSouza

• Classification of neck surface emg signals for the early detection of vocal dysfunction

  The 12th Advances in Quantitative Laryngology, Voice and Speech Research (AQL), 2017

  Y. Gao, M. Pfeiffier, M. Dietrich, and G. N. DeSouza