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Personal profile

Biography

Christoph Bergmeir is a Senior Lecturer in Data Science and Artificial Intelligence, and a 2019 ARC DECRA Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His fellowship is on the development of “efficient and effective analytics for real-world time series forecasting”. He works as a Data Scientist in a variety of projects with external partners in diverse sectors, e.g. in healthcare or infrastructure maintenance. Christoph holds a PhD in Computer Science from the University of Granada, Spain, and an M.Sc. degree in Computer Science from the University of Ulm, Germany. He has published on time series prediction using Machine Learning methods, recurrent neural networks and long short-term memory neural networks (LSTM), time series predictor evaluation, as well as on medical applications and software packages in the R programming language, in journals such as IEEE Transactions on Neural Networks and Learning Systems, Journal of Statistical Software, Computational Statistics and Data Analysis, and Information Sciences.

Christoph Bergmeir's personal website

 

Monash teaching commitment

Dr Christoph Bergmeir has taught the following units at Monash University in the past:

  • 2018 FIT3163/FIT3164: Data Science project 1 and 2
  • 2017 FIT5120AA: Industry Experience Studio Project (Advanced Analytics Stream)
  • 2016, 2017 FIT5145: Introduction to Data Science both on campus within the Master of Data     Science and online within the Graduate Diploma in Data Science (GDDS)

 

Education/Academic qualification

Computer Science, Doctor of Philosophy (Doctor por la Universidad de Granada), Universidad de Granada (University of Granada)

Award Date: 21 Mar 2013

Computer Science, Master Universitario en Soft Computing b Sistemas Intelligentes, Universidad de Granada (University of Granada)

Award Date: 20 Jan 2010

Computer Science, Diplom Informatiker, Universitat Ulm (University of Ulm)

Award Date: 16 Jan 2008

Research area keywords

  • Machine Learning
  • Time series forecasting
  • R programming

Network

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