Dr Paraskevas Yiapanis

Who am I?

I am a Senior Lecturer (Associate Prof.) in Data Science at Manchester Metropolitan University.

My research spans from extracting hidden patterns and forming predictions on data from a variety of domains to engineering and optimising systems software for delivering maximum performance and efficiency.

Recently I have been working on implementing and optimising Big Data systems as well as applying data analytic techniques in the healthcare domain while working in close collaboration with industrial partners and the public sector.

For an up-to-date list of publications, visit my google scholar profile.

Academic and professional qualifications

• PhD in Computer Science, University of Manchester, 2013
• MSc (Distinction) in Software Engineering, University of Manchester, 2007
• BSc (Summa Cum Laude) in Computer Science, Radford University, 2005

Previous employment

• Research Lead, MDSAS (Medical Data Solutions and Services)
• Postdoctoral Research Associate, University of Manchester
• Research Assistant, Oracle Inc.
• Software Engineer, Broadridge Financial Solutions
• Teaching Assistant, University of Manchester
• Teaching Assistant, Radford University

Research expertise

• Data Analytics applied to healthcare data
• Data Analytics applied to systems software
• Engineering Big Data systems
• Performance optimisation in systems software

Academic collaborations

• University of Manchester
• University of Edinburgh

Publications

• Featured Works

  P. Yiapanis, G. Brown, M. Lujan (2016). Compiler-driven software speculation for thread-level parallelism. ACM Transactions on Programming Languages and Systems. 38(2),

  A-K. Koliopoulos, P. Yiapanis, F. Tekiner, G. Nenadic, JA. Keane (2015). A Parallel Distributed Weka Framework for Big Data Mining Using Spark. In: BigData Congress. 27/6/2015. pp.9-16.

  P. Yiapanis, D. Rosas-Ham, G. Brown, M. Luján (2013). Optimizing software runtime systems for speculative parallelization. ACM Transactions on Architecture and Code Optimization. 9(4),

• Journal articles

  P. Yiapanis, G. Brown, M. Lujan (2016). Compiler-driven software speculation for thread-level parallelism. ACM Transactions on Programming Languages and Systems. 38(2),

  P. Yiapanis, D. Rosas-Ham, G. Brown, M. Luján (2013). Optimizing software runtime systems for speculative parallelization. ACM Transactions on Architecture and Code Optimization. 9(4),

  J. Singer, G. Brown, M. Luján, AC. Pocock, P. Yiapanis (2009). Fundamental Nano-Patterns to Characterize and Classify Java Methods. LDTA. 253(7), pp.191-204.

• Conferences

  R. Salehnejad, R. Allmendinger, Y-W. Chen, M. Ali, A. Shahgholian, et al. P. Yiapanis, M. Mansur. (2017). Leveraging data mining techniques to understand drivers of obesity. In: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 23/8/2017.

  A-K. Koliopoulos, P. Yiapanis, F. Tekiner, G. Nenadic, JA. Keane (2015). A Parallel Distributed Weka Framework for Big Data Mining Using Spark. In: BigData Congress. 27/6/2015. pp.9-16.

  D. Rosas-Ham, I. Herath, P. Yiapanis, M. Luján, I. Watson (2012). Architectural Support for Exploiting Fine Grain Parallelism. In: HPCC-ICESS. Liverpool, ENGLAND, 25/6/2012. pp.61-70.

  N. Ioannou, J. Singer, S. Khan, P. Xekalakis, P. Yiapanis, et al. AC. Pocock, G. Brown, M. Luján, I. Watson, M. Cintra. (2010). Toward a more accurate understanding of the limits of the TLS execution paradigm. In: IISWC. 2/12/2010. pp.1-12.

  A. Pocock, P. Yiapanis, J. Singer, M. Luján, G. Brown (2010). Online Non-stationary Boosting. In: MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS. Cairo, EGYPT, 7/4/2010. pp.205-214.

  AC. Pocock, P. Yiapanis, J. Singer, M. Luján, G. Brown (2010). Online Non-stationary Boosting. In: MCS. pp.205-214.

  P. Yiapanis, DJ. Haglin, AM. Manning, K. Mayes, JA. Keane (2008). Variable-grain and dynamic work generation for Minimal Unique Itemset mining. In: CLUSTER. Tsukuba, JAPAN, 29/9/2008. pp.33-41.

Projects and initiatives

Current project involvement:

• Evidence-based Decision Support for Haemophilia Treatment, EPSRC IAA

Past project involvement:

• SAMS (Software Architecture for Mental health Self management), EPSRC
• Teraflux (Exploiting Dataflow Parallelism in Teradevice Computing), EU FP7
• iTLS (Machine Learning for Thread Level Speculation on Multicore Architectures), EPSRC

Membership of professional associations

• Member, Association for Computing Machinery (ACM)
• Member, European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC)
• Fellow of the Higher Education Academy (FHEA)