Dr Marcello Trovati

Position: Senior Lecturer in Mathematics

College: College of Engineering and Technology

Department: Computing and Mathematics

Subject area: Mathematics

Research Centre: Distributed and Intelligent Systems Centre for Research and Technology Transfer

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I am a Senior Lecturer at the Department of Computing and Mathematics at the University of Derby.

My research interests include mathematical modelling, the science of Big Data, including data and text mining, and their applications to multi-disciplinary topics.

Teaching responsibilities

I am the programme leader for the MSc in Big Data Analytic

Please contact me if interested.


I am also the module leader for the following undergraduate modules:

  • Analysis and Algebra
  • Further Topics in Mathematics
  • Maths Group Project

Research interests

  • Theory and Applications of Big Data
  • Text and Data Mining
  • Information Extraction
  • Computational Mathematics

Membership of professional bodies



University of Aberdeen: MA Single Honours in Mathematics, 2002

University of Exeter: PhD in Mathematics, 2007

Coventry University: PG Cert in Teaching in Higher Education, 2012

Recent publications

Journal Articles

  • Trovati M., Bessis N., Palmieri F., and Hill R. Extraction and Conceptualisation of Probabilistic Information: A Big Data Approach, Under Review IEEE Systems, 2015, Impact Factor 1.746
  • Trovati M. and Bessis N. An Influence Assessment Method based on Co-Occurrence for Topologically Reduced Big Data Sets, Soft Computing, DOI 10.1007/s00500-015-1621-9, 2015, Impact Factor 1.304
  • Trovati M. Reduced Topologically Real-World Networks: a Big-Data Approach, International Journal of Distributed Systems and Technologies (IJDST), 2015, Impact Factor 0.64 as for 2011
  • Behadada O, Trovati M, Chikh M A and Bessis N. Big Data Based Extraction of Fuzzy Partition Rules for Heart Arrhythmia Detection: a Semi-Automated Approach. Concurrency and Computation: Practice and Experience, DOI: 10.1002/cpe.3428, 2014, Impact Factor 0.784
  • Trovati M, Ashwin P and Byott N. Packings induced by piecewise isometries cannot contain the Arbelos. Discrete and Continuous Dynamical Systems, Volume 22:3, 2008, Impact Factor 0.923
  • Trovati M and Ashwin P. Tangency properties of a pentagonal tiling generated by a piecewise isometry, Chaos: An Interdisciplinary Journal of Nonlinear Science, 17:4, 2007, Impact Factor 2.188

Book Chapters

  • Trovati M. Mining Social Media: Architecture, Tools and Approaches to Detect Criminal Activity, Application of Big Data for National Security, 1st Edition, 2015

Selected Conference and Workshop Articles

  • Trovati M, Asimakopoulou E, Bessis N. Topology Reduction and Probabilistic Information Extraction for Large Data-Sets: A Disaster Management Case Study, Under Review ICT-DM’2015, 2015
  • Trovati M, Larcombe P, Bessis N, Some Novel Mathematical Algorithms for Concepts and Relations Extraction from Big Data, IMA Conference on the Mathematical Challenges of Big Data, 2014
  • Voorhis D, Trovati M, Self R, Designing Big Data Analytics Undergraduate and Postgraduate Programmes for Employability, IBM Big Data and Analytics Educational Conference 2014
  • Trovati M, Bessis N and Asimakopoulou E. An Analytical Tool to Map Big Data to Networks with Reduced Topologies. Proceedings of INCoS 2014, pp: 411 – 414, 2014.
  • Trovati M, Bessis N, Huber A, Zelenkauskaite A and Asimakopoulou E. Extraction, Identification, and Ranking of Network Structures from Data Sets. Proceedings of the Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pages 331 – 337, 2014.
  • Trovati M and Brady J. Towards an Automated Approach to Extract and Compare Fictional Networks: An Initial Evaluation. Proceedings of DEXA’14 Workshops, 2014
  • Trovati M and Bagdasar O. Influence Discovery in Semantic Networks: An Initial Approach UKSIM ’14 Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pages 154-158, 2014


  • McCloskey D J, Trovati M, and Zimmer C, Using a Dynamically-Generated Content-Level Newsworthiness Rating to Provide Content Recommendations. US 8,386,457 B2, 2013.

Experience in industry

IBM Research

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