Paraskevas (Paris) Yiapanis

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Mikel LujanDepartment of Computer Science, University of ManchesterVerified email at manchester.ac.uk
Gavin BrownProfessor of Machine Learning, Dept of Computer Science, University of ManchesterVerified email at manchester.ac.uk
Adam PocockResearcher at Oracle Labs Machine Learning Research GroupVerified email at oracle.com
Goran NenadicDepartment of Computer Science, University of ManchesterVerified email at manchester.ac.uk
Demian Rosas-HamPhD in Computer Science, University of ManchesterVerified email at cs.man.ac.uk
Marcelo CintraHead of Scientific Computing at CAESAR and Honorary Professor at University of EdinburghVerified email at caesar.de
Nikolas IoannouGoogleVerified email at google.com
Martyn AmosProfessor of Computer and Information Sciences, Northumbria UniversityVerified email at northumbria.ac.uk
Joshua PeakePhD Student, Centre for Advanced Computational Science, Manchester Metropolitan UniversityVerified email at mmu.ac.uk
Huw LloydSenior Lecturer, Computing and Mathematics, Manchester Metropolitan UniversityVerified email at mmu.ac.uk
Polychronis XekalakisNvidiaVerified email at nvidia.com
Salman KhanSolarflare CommunicationsVerified email at solarflare.com
Richard AllmendingerProfessor of Applied AI, The University of Manchester, UKVerified email at manchester.ac.uk
David J. HaglinChief Technology Officer, Trovares Inc.Verified email at trovares.com
Julia HandlProfessor in Decision Sciences, The University of ManchesterVerified email at manchester.ac.uk
Mohaimen MansurAssociate Professor, University of DhakaVerified email at isrt.ac.bd
Azar ShahgholianSenior Lecturer in Digital MarketingVerified email at ljmu.ac.uk
Yu-wang ChenThe University of ManchesterVerified email at manchester.ac.uk
Isuru HerathPhD Student in Computer Science, University of ManchesterVerified email at cs.man.ac.uk

Paraskevas (Paris) Yiapanis

University of Manchester

Verified email at cs.man.ac.uk

Machine Learning Engineering Data Science MLOps Data Engineering


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A Parallel Distributed Weka Framework for Big Data Mining using Spark AK Koliopoulos, P Yiapanis, F Tekiner, G Nenadic, J Keane IEEE International Congress on Big Data (BigData Congress), 9-16, 2015	80	2015
Fundamental nano-patterns to characterize and classify java methods J Singer, G Brown, M Luján, A Pocock, P Yiapanis Electronic Notes in Theoretical Computer Science 253 (7), 191-204, 2010	49	2010
Optimizing software runtime systems for speculative parallelization P Yiapanis, D Rosas-Ham, G Brown, M Luján ACM Transactions on Architecture and Code Optimization (TACO) 9 (4), 1-27, 2013	43	2013
Online non-stationary boosting A Pocock, P Yiapanis, J Singer, M Luján, G Brown Multiple Classifier Systems, 205-214, 2010	42	2010
Toward a more accurate understanding of the limits of the TLS execution paradigm N Ioannou, J Singer, S Khan, P Xekalakis, P Yiapanis, A Pocock, G Brown, ... IEEE International Symposium on Workload Characterization (IISWC), 1-12, 2010	24	2010
Scaling Techniques for Parallel Ant Colony Optimization on Large Problem Instances J Peake, M Amos, P Yiapanis, H Lloyd Genetic and Evolutionary Computation Conference (GECCO), 2019	17	2019
Towards Automatic Memory Tuning for In-Memory Big Data Analytics in Clusters A Koliopoulos, P Yiapanis, T Tekiner, G Nenadic, J Keane IEEE International Congress On Big Data (BigData Congress), 2016	16	2016
Compiler-Driven Software Speculation for Thread-Level Parallelism P Yiapanis, G Brown, M Luján ACM Transactions on Programming Languages and Systems (TOPLAS) 38 (2), 5, 2016	15	2016
Vectorized candidate set selection for parallel ant colony optimization J Peake, M Amos, P Yiapanis, H Lloyd Genetic and Evolutionary Computation Conference (GECCO) Companion, 1300-1306, 2018	12	2018
Static java program features for intelligent squash prediction J Singer, P Yiapanis, A Pocock, M Lujan, G Brown, N Ioannou, M Cintra Statistical and Machine learning approaches to ARchitecture and compilaTion …, 2010	9	2010
Current and future applications of artificial intelligence in surgery: implications for clinical practice and research MX Morris, D Fiocco, T Caneva, P Yiapanis, DP Orgill Frontiers in Surgery 11, 1393898, 2024	8	2024
Variable-grain and dynamic work generation for Minimal Unique Itemset mining P Yiapanis, DJ Haglin, AM Manning, K Mayes, J Keane IEEE International Conference on Cluster Computing (CLUSTER), 33-41, 2008	7	2008
Parallel Mining of Minimal Sample Unique Itemsets P Yiapanis University of Manchester, 2007	5	2007
A clustering-based patient grouper for burn care CN Onah, R Allmendinger, J Handl, P Yiapanis, KW Dunn Intelligent Data Engineering and Automated Learning–IDEAL 2019: 20th …, 2019	4	2019
Leveraging data mining techniques to understand drivers of obesity R Salehnejad, R Allmendinger, YW Chen, M Ali, A Shahgholian, ... IEEE Conference on Computational Intelligence in Bioinformatics and …, 2017	2	2017
High Performance Optimizations in Runtime Speculative Parallelization for Multicore Architectures P Yiapanis The University of Manchester (United Kingdom), 2013	2	2013
Architectural Support for Exploiting Fine Grain Parallelism D Rosas-Ham, I Herath, P Yiapanis, M Luján, I Watson IEEE International Conference on High Performance Computing and …, 2012	1	2012
Using Static Code Features for Intelligent Squash Prediction J Singer, A Pocock, P Yiapanis, S Wilkinson, M Luján, G Brown		2009
Mining Static Features for Squash Prediction in Thread Level Speculation P Yiapanis, J Singer, A Pocock, M Luján, G Brown Advanced Computer Architecture and Compilation for High-Performance and …, 2009		2009

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