Research areas

  • Bayesian Networks, Design Theory, Geostatistics, Machine Learning, Minimum Message Length Inference, Bayesian Inference, Support Vector Machines (SVM)

Biography

David Albrecht started in the Faculty of Information Technology in 1994. He has done research in several areas including Machine Learning, Bayesian Networks, User Modelling, Geostatistics, Logic, Functional Operator Theory, and Statistics. He is currently jointly supervising three Ph.D. students and two Masters students. He has two honours degrees and a Ph.D in Mathematics from Monash (1994). During his time at Monash David has run an extension program for Primary School students, the Faculty Extension program for Secondary School students, worked with the teachers at John Monash Science School in developing several subjects, developed a unit which has become the basis of a new VCE unit, and was involved in the Thailand-Australia Science and Engineering Assistance Project (TASEAP) to improve the quality of undergraduate science and engineering in Thai universities. He has always been at the forefront of applying and developing better learning and teaching methods, most recently the introduction of Peer Instruction and the development of resources on Alexandria in the Faculty of Information Technology. In 2015 he was nominated by the Faculty to be the Faculty Foundation Fellow of the Monash Education Academy. David is an experienced Coordinator and manager of courses, and is currently the chair of Faculty Academic Progress Committee.

Monash teaching commitment

Dr David Albrecht has experience as the Chief Examiner for the following units in the Faculty of IT:

  • FIT1008 Introduction to computer science
  • FIT1029 Algorithmic problem solving
  • FIT1045 Introduction to algorithms and programming
  • FIT3144 Advanced computer science project
  • FIT4008 Reading unit
  • Minor Thesis, Masters Thesis and Honours Thesis units

David has experience as the Lecturer for the following units in the Faculty of IT:

  • FIT1008 Introduction to computer science
  • FIT1029 Algorithmic problem solving
  • FIT1040 Programming fundamentals
  • FIT1045 Introduction to algorithms and programming
  • FIT2032 Industry-based learning
  • FIT3045 Industry-based learning
  • FIT3144 Advanced computer science project
  • FIT4009 Advanced topics in intelligent systems

Research output

  1. LeSiNN: Detecting anomalies by identifying Least Similar Nearest Neighbours

    Research output: Research - peer-reviewConference Paper

  2. Efficient anomaly detection by isolation using nearest neighbour ensemble

    Research output: Research - peer-reviewConference Paper

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Activities

  1. Chair - Academic Progress Committee - Faculty of Information Technology, Monash University

    Activity: External academic engagementCommittees and working groups

  2. Secretary - Australasian Bayesian Network Modelling Society

    Activity: External academic engagementProfessional association or peak discipline body

  3. Fellow - Monash Education Academy - Monash University

    Activity: External academic engagementProfessional association or peak discipline body

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