David teaches ATMC NZ’s undergraduate and postgraduate level programmes in software development and IT security management.
He holds a Bachelors in Software Engineering and a Masters in Information Systems both from AUT University, and a Graduate Diploma in Teaching from the University of Auckland. He will complete his PhD in 2020.
After graduating, he entered the industry and worked as a website developer and in data analysis, before starting his teaching career. David is a problem solver, who likes to use computational thinking in everything.
His favorite pass time activities include gardening, designing furniture, playing tennis and hacking. He loves Marvel and Disney studios.
Research areas of interest:
David’s doctoral studies involve Educational Datamining (EDM) for Personalised Educational Paradigm.
After five years of undertaking simulation studies in designing Personalised Information systems for patients, he is currently interested in ways of maximising the benefits and enabling the wider use of the personalisation technique including business and education domains and is also investigating how the qualitative outcomes of EDM can be utilised for assessing and supporting learners through a Mobile Devices in order to augment Bring Your Own Technology (BYOT) initiative.
His current research projects include:
- The Usage of Machine Learning Techniques for Malware Analysis
- Malware classification and detection using artificial neural network
- The impact of blockchain technology in business domain, health sector and education field.
- Internet of Things (IoT) for smart life
List of Research
Nandigam, D., Tirumala, S (2020) Evaluation of Feature and Signature based training approaches for Malware Classification using Autoencoders. 12th International Conference on COMmunication Systems & NETworkS (COMSNETS 2020) January 7 – 11, Bengaluru, India
Nandigam, D., Dacey, S (2020) Identifying Significant Features for Student Performance through Education Datamining. Conference: EDUCON2020 – IEEE Global Engineering Education Conference, University of Coimbra, University of Porto and Polytechnic of Porto, Portugal. April 28-30th, 2020.
Baghaei, N., Nandigam, D., Casey, J (2016) Designing and Evaluating Mobile Games for Diabetes Education. Games for Health Journal.
Baghaei, N., Nandigam, D., Casey, J., Direitob, A and Maddison, R (2015) Evaluating Mobile Games for Diabetes Education. Ogata, H. et al. (Eds.). Proceedings of the 23rd International Conference on Computers in Education. China: Asia-Pacific Society for Computers in Education
Nandigam, D., Baghaei, N. and Hai-Ning Liang (2015). Mobile Devices as Support Systems for Health Behaviour. IEEE International Conference on Computing and Technology Innovation (CTI 2015), 27-28 May, Luton, United Kingdom, 2015.
Nandigam, D.; Tirumala, S.S.; Baghaei, N., (2014) Personalized learning: Current status and potential,” in e-Learning, e-Management and e-Services (IC3e), 2014 IEEE Conference. pp.111-116, 10-12 Dec.
Nandigam, D., Sremath Tirumala, S., & Baghaei, N. (2014). Teaching with Mobile Devices. IEEE Computer.
Tirumala, S. S., Chen, G., Nandigam, D., Maruno, & Yuki and Li, Z. (2014). Performance analysis of QCCEA vs CCEA for Maze problem. The Tenth International Conference on Simulated Evolution And Learning. Dunedin, New Zealand.