Weitao Tang

Weitao Tang has a diverse work experience in the fields of computer vision, machine learning, and metal casting. Weitao started their career in 2015 as a Graduate Research Assistant at the New Jersey Institute of Technology, where they developed Matlab tools for simulating optical and dielectric properties of nanomaterials. Weitao also performed photolithography and utilized SEM for characterization.

In 2017, Weitao joined Purdue University as a Graduate Research Assistant. Weitao'sresponsibilities included analyzing data to enhance the yield rate of Chrysler Die Casting Foundry using machine learning techniques. Weitao also conducted research on metal casting defects and utilized computer simulations to decrease the defect rate of Enkei America's products.

In 2021, Weitao returned to AiFi Inc. as a Computer Vision Research Engineer, focusing on developing algorithms for automatic product recognition using machine learning methods and camera projection.

Overall, Weitao Tang has a strong background in computer vision, machine learning, and experimental research in metal casting. Weitao has experience in algorithm development, data analysis, and simulation techniques.

Weitao Tang has an extensive education history. Weitao attended Purdue University from 2017 to 2022, where they pursued a Doctor of Philosophy (Ph.D.) degree in Computer Information Technology. Prior to that, from 2015 to 2017, they obtained a Master's degree in Materials Science and Engineering from the New Jersey Institute of Technology. Before their master's degree, they completed their Bachelor's degree in Materials Chemistry from Tianjin University, graduating in the year 2014.

In addition to their formal education, Weitao Tang also obtained several certifications. In 2019, they completed courses on "Convolutional Neural Networks," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Neural Networks and Deep Learning," and "Structuring Machine Learning Projects." These certifications were obtained through Coursera.