Adjunct Professor

Bio

Dr. Giulia De Masi is Adjunct Professor in the College of Natural and Health Sciences of Zayed University, Dubai Campus. Prior to ZU, she taught at American University in Dubai and Canadian University in Dubai. She has very long experience both in Research and Industry, in the field of Statistical Physics of Complex Systems, Computational Physics, Data Science and Machine Learning. Her main research interests are Data Science applications to Physics as well as multidisciplinary applications.

 EMPLOYMENT HISTORY:

• Adjunct Professor, College of Natural and Life Science, Zayed University, Dubai, UAE, present

• Visiting Associate Professor, School of Engineering, Canadian University in Dubai, UAE

• Adjunct Professor, School of Engineering, American University in Dubai, UAE

• Scientist, Snamprogetti center of Excellence, Italy

• Visiting Researcher, ATR, Hitachi lab, Nara, Japan

• Post-doctoral Researcher, Polytechnic University of Marche, Ancona, Italy

Office

Dubai Academic City, C-L1-033

Phone:

+971 4 402 1367

Email:

Teaching Areas

Statistical Physics, Computational Physics, Data Science, Machine Learning, Complex Networks

Research and Professional Activities

• Data Science and Machine Learning, with applications to Physics as well as multidisciplinary applications.

• Statistical Physics.

• Statistical analysis of extreme events.

• Cyclone modellng.

MEMBERSHIP OF SCIENTIFIC AND PROFESSIONAL SOCIETIES:

• IEEE, Institure of Electrical and Electronics Engineers

• IAHR, International Association for Hydro-Environment Engineering and Research

• EDAW, Emirates Digital Association of Women

SELECTED PUBLICATIONS:

1. The best-of-n problem with dynamic site qualities: Achieving adaptability with stubborn individuals, J. Prasetyo, G. De Masi, P. Ranjan, E. Ferrante, ANTS2018, Lectures Notes in Computer Science, Vol.11172, Springer, 2018

2. The Impact of Topology on Internet of Things: a Multidisciplinary Review, Proceedings of ASET Conference First Multi Conference on Advances in Science and Engineering Technology (ASET 2018), “IoT, Mechatronics and Applications”, 2018

3. The Network of European Outward Foreign Direct Investment, Giulia De Masi and Giorgio Ricchiuti, chapter contribution to book “Networks of International Trade and Investment: Understanding globalisation through the lens of network analysis”, Vernon Press, edited by Alessia Amighini, Sara Gorgoni, Matthew Smith, 2018

4. Multiscale processing of loss of metal: a machine learning approach, G. De Masi, M. Gentile, R. Vichi, R. Bruschi, A. Bennardo, G. Gabetta, Journal of Physics Conference Series 07/2017; 869(1):012023, 2017

5. Corrosion prediction by hierarchical neural networks, G. De Masi, M. Gentile, R. Vichi, R. Bruschi, G. Gabetta, COMPIT2016 Proceedings, Lecce 2016

6. Artificial neural network - multiscale processing of loss of metal, G. De Masi, M. Gentile, R. Vichi, R. Bruschi, A. Bennardo, G. Gabetta, M. Conti, NACE2016 Proceedings, 2016

7. Statistical method for Cyclone probabilistic assessment, G. De Masi, M. Mattioli, M. Drago, Oceans2015 Proceedings, IEEExplore, 2015

8. Synthetic metocean time series generation for offshore design and operability based on multivariate Markov model, G. De Masi, M. Drago, R. Bruschi, Oceans2015 Proceedings, IEEExplore, 2015

9. Machine Learning approach to corrosion assessment in subsea pipelines, G. De Masi, M. Gentile, R. Vichi, R. Bruschi, G. Gabetta, Oceans2015 Proceedings, IEEExplore, 2015

10. Pipeline Internal Damage Prediction by Deterministic Models and Neural Networks, G. Gabetta, G. De Masi, M. Gentile, R. Vichi, M. Scapin, SPE 171919, 2014

11. Analysis of opportunity windows for weather sensitive operations, Y.P. Foo, K. Gan, D. Giudice, G. De Masi, Oil&Gas Facilities, SPE-171553-PA, 2014

12. A Neural Network Predictive Model of Pipeline Internal Corrosion Profile, G. De Masi, R. Vichi, M. Gentile, R. Bruschi, G. Gabetta, SIMS2014 Proceedings, IEEExplore, 2014

13. Optimization of critical wave forecasting by Artificial Intelligence, G. De Masi, F. Gianfelici, Y.P. Foo, OCEANS2013, IEEE Proceedings, 2013