Dr Ciprian Doru Giurcaneanu

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Senior Lecturer


CD Giurcaneanu received the Ph.D. degree (with commendation) from Tampere University of Technology (TUT), Finland, in 2001. From 1993 to 1997, he was a Junior Assistant at "Politehnica" University of Bucharest. In 1997 he joined TUT where he spent more than 14 years as a Researcher, Senior Researcher and Academy Research Fellow in the Department of Signal Processing. From January 2012 to June 2012 he was with Helsinki Institute for Information Technology (HIIT), and in July 2012 he joined the Department of Statistics, University of Auckland, where he is currently a Senior Lecturer. His research is mainly focused on stochastic complexity and its applications.

Research | Current

My list of publications can be found here.

Teaching | Current

2012: STATS150, STATS726

2013: STATS150, STATS390, STATS726

2014: STATS210, STATS726

2015: STATS210, STATS726

Postgraduate supervision

2013: F.A. Abdul Saip (BScHons) - On the use of sequentially normalized maximum likelihood for selecting the order of autoregressions when the model parameters are estimated by forgetting factor least-squares algorithms.

2014: S. Li (Research Msc) - Periodogram smoothing via cepstral analysis

2014: K. Furushima (MSc) - Methods for selecting the model when parametric complexity is infinite

2015 (anticipated): R. Vasundara (BScHons) - Performance analysis for a chaos-based CDMA system in wide-band channel

2016 (anticipated): C. Li (Research Msc, Part-Time) - Information theoretic criteria for least-squares trees


Postgraduate Adviser

Areas of expertise

Time series, model selection, data compression, clustering.

Selected publications and creative works (Research Outputs)

  • Alavi-Shoshtari, M., Salmond, J. A., Giurcăneanu CD, Miskell, G., Weissert, L., & Williams, D. E. (2018). Automated data scanning for dense networks of low-cost air quality instruments: Detection and differentiation of instrumental error and local to regional scale environmental abnormalities. Environmental Modelling and Software, 101, 34-50. 10.1016/j.envsoft.2017.12.002
    Other University of Auckland co-authors: David Williams, Jennifer Salmond
  • Maanan, S., Dumitrescu, B., & Giurcaneanu, C. D. (2017). Conditional independence graphs for multivariate autoregressive models by convex optimization: Efficient algorithms. SIGNAL PROCESSING, 133, 122-134. 10.1016/j.sigpro.2016.10.023
    URL: http://hdl.handle.net/2292/31528
    Other University of Auckland co-authors: Said Maanan
  • Maanan, S., Dumitrescu, B., & Giurcaneanu, C. D. (2016). Renormalized maximum likelihood for multivariate autoregressive models. Proceedings of 24th European Signal Processing Conference (EUSIPCO), 150-154. Budapest, Hungary: The European Association for Signal Processing (EURASIP). 10.1109/EUSIPCO.2016.7760228
    URL: http://hdl.handle.net/2292/31494
    Other University of Auckland co-authors: Said Maanan
  • Giurcaneanu, C. D. (2015). Information theoretic criteria for least-squares trees. In C.-H. Chen, H. P. Chan (Eds.) IASC-ARS 2015 Book of Abstracts, 41-41. Singapore: IASC-ARS. Related URL.
    URL: http://hdl.handle.net/2292/28032
  • Giurcaneanu, C. D., Abeywickrama, R. V., & Berber, S. (2015). Performance analysis for a chaos-based code-division multiple access system in wide-band channel. JOURNAL OF ENGINEERING-JOE10.1049/joe.2015.0117
    URL: http://hdl.handle.net/2292/26915
    Other University of Auckland co-authors: Stevan Berber
  • Giurcaneanu, C. D., & Saip, F. A. A. (2014). New insights on AR order selection with information theoretic criteria based on localized estimators. DIGITAL SIGNAL PROCESSING, 32, 37-47. 10.1016/j.dsp.2014.06.005
    URL: http://hdl.handle.net/2292/22942
  • Giurcaneanu, C. D., & Razavi, S. A. (2013). Analysis of an information theoretic criterion for cepstral nulling. 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings. 10.1109/ChinaSIP.2013.6625285
  • Razavi, S. A., & Giurcaneanu, C. D. (2013). Application of optimally distinguishable distributions to the detection of subspace signals in Gaussian noise of unknown level. DIGITAL SIGNAL PROCESSING, 23 (4), 1094-1102. 10.1016/Idsp.2013.01.014


Contact details

Primary location

New Zealand

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