Engineering Basics for Mathematicians

Time:2023-10-12 ~ 2024-01-25 Thu 14:20-16:55

Venue:Venue: A3-1-103 Zoom: 388 528 9728 (PW: BIMSA)

Speaker:Xiaoming Zhang (张晓明, Professor)


This course is intended for graduate students and researchers with mathematics background. The purpose is to help the attendees to have a quick grasp of engineering basics, a quick entry to future engineering related research projects, and a quick mastery of communication language with professional engineers.

Lecturer Intro

Dr. Zhang Xiaoming received his bachelor's, master's, and doctor's degrees from Zhejiang University, Peking University, and Massachusetts Institute of Technology. He is currently a research fellow at the Beijing Institute of Mathematical Sciences and Applications, responsible for the artificial intelligence and big data research team. Dr. Zhang has long been engaged in the research, development, and application of artificial intelligence technologies to big data prediction and resource optimization and allocations problems. He presided over the development of digital intelligence service platform "printing and dyeing brain", which was well recognized in the industry. At present, his work focuses on the research and development of digital twins and process twins in the industrial field.

DATEOctober 12, 2023
Related News
    • 0

      Financial Engineering and Derivatives Markets II

      Prerequisiteprobability theory, calculus,AbstractWith the development of the financial market, traditional financial products can not meet the increasing financial demand, and the derivatives market (such as futures and options) has developed very rapidly in China. Mathematical tools have played a huge role in the design of new financial products and the development of new facilities, and deriv...

    • 1

      Probabilistic machine learning

      IntroductionProbabilistic approach in machine and deep learning leads to principled solutions. It provides explainable decisions and new ways for improving of existing approaches. Bayesian machine learning consists of probabilistic approaches that rely on Bayes formula. It can help in numerous applications and has beautiful mathematical concepts behind. In this course, I will describe the found...