Alexandros Iosifidis, Ph.D.


Contact Information

Education

Ph.D. (with honors) - Computer Science
Aristotle University of Thessaloniki, Greece (2014)

M.Eng. (with honors) - Electrical and Computer Engineering
Democritus University of Thrace (2010)

Diploma (5-year studies) - Electrical and Computer Engineering
Democritus University of Thrace (2008)

Biography

Alexandros Iosifidis is a Docent of Machine Learning in the Laboratory of Signal Processing at Tampere University and an Associate Professor of Machine Learning at Aarhus University, Denmark. He has held Postdoctoral Researcher positions at Tampere University of Technology, Finland and Aristotle University of Thessaloniki, Greece. He received prestigious awards, including the Academy of Finland Postdoc Fellowship and the H.C. Oersted  Forskerspirer Prize for research excellence at a young age. He has contributed in more than ten R&D projects financed by EU, Greek, Finnish, and Danish funding agencies and companies. He has co-authored 50 articles in international journals and 74 papers in international conferences proposing novel Machine Learning techniques and their application in a variety of problems. His work has received 1500+ citations with h-index 20+ (Google Scholar). He has co-organized Special Issues/Sessions in international journals/conferences focusing on topics of Machine Learning and Big Data Analysis.

Dr. Iosifidis is a Senior Member of IEEE and he served as an Officer of the Finnish IEEE Signal Processing-Circuits and Systems Chapter (20162018). He is currently serving as an Associate Editor in IEEE Access and Neurocomputing journals and as an Area Editor in Signal Processing: Image Communication journal, he was an Area Chair in IEEE ICIP (2018,2019) and Technical Program Chair in IEEE ICASSP 2019.

Other CVDI Projects Involved With

Publications

  • M. Waris, A. Iosifidis and M. Gabbouj, “CNN-based Edge Filtering for Object Proposals”, Neurocomputing, accepted May 2017
  • A. Iosifidis and M. Gabbouj, “Class-Specific Kernel Discriminant Analysis revisited: further analysis and extensions”, IEEE Transactions on Cybernetics, accepted September 2016 pdf
  • S. Kaveh, A. Iosifidis and M. Gabbouj, “On the comparison of random and Hebbian weights for the training of single-hidden layer feedforward neural networks”, Expert Systems with Applications, vol. 83, pp. 177-186, 2017 pdf
  • S. Kiranyaz, T. Ince, A. Iosifidis and M. Gabbouj, “Progressive Operational Preceptrons”, Neurocomputing, vol. 224, pp. 142-154, 2017 pdf
  • J. Arje, S. Karkkainen, K. Meissner, A. Iosifidis, T. Ince, M. Gabbouj and S. Kiranyaz, “The effect of automated taxa identification errors on biological indices”, Expert Systems with Applications, vol. 72, pp. 108-120, 2017 pdf
  • C. Aytekin, A. Iosifidis, S. Kiranyaz and M. Gabbouj, “Learning Graph Affinities for Spectral Graphbased Salient Object Detection”, Pattern Recognition, vol. 64, pp. 159-167, 2017 pdf
  • A. Iosifidis, A. Tefas and I. Pitas, “Approximate Kernel Extreme Learning Machine for Large-Scale Data Classification”, Neurocomputing, vol. 219, pp. 210-220, 2017 pdf
  • A. Iosifidis, V. Mygdalis, A. Tefas and I. Pitas, “One-Class Classification based on Extreme Learning and Geometric Class Information”, Neural Processing Letters, vol. 45, no. 2, pp. 577-592, 2017 pdf
  • A. Iosifidis and M. Gabbouj, “Scaling up Class-Specific Kernel Discriminant Analysis for large-scale Face Verification”, IEEE Transactions on Information Forensics and Security, vol. 11, no. 11, pp. 2453-2465, 2016 pdf
  • V. Mygdalis, A. Iosifidis, A. Tefas and I. Pitas, “Graph Embedded One-Class Classifiers for media data classification”, Pattern Recognition, vol. 60, pp. 585-595, 2016 pdf
  • A. Iosifidis and M. Gabbouj, “Nyström-based Approximate Kernel Subspace Learning”, Pattern Recognition, vol. 57, pp. 190-197, 2016 pdf
  • F. Patrona, A. Iosifidis, A. Tefas, N. Nikolaidis and I. Pitas, “Visual Voice Activity Detection in the Wild”, IEEE Transactions on Multimedia, vol. 18, no. 6, pp. 967-977, 2016 pdf
  • A. Iosifidis and M. Gabbouj, “Multi-class Support Vector Machine Classifiers using Intrinsic and Penalty Graphs”, Pattern Recognition, vol. 55, pp. 231-246, 2016 pdf
  • A. Iosifidis, A. Tefas and I. Pitas, “Graph Embedded Extreme Learning Machine”, IEEE Transactions on Cybernetics, vol. 46, no. 1, pp. 311-324, 2016 pdf
  • C. Aytekin, S. Kiranyaz, A. Iosifidis and M. Gabbouj, “Recent Advances in Salient Object Detection – Towards Object Recognition in Big Media Data”, Futura – Big Data, vol. 35, no. 2, pp. 80-92, 2016 pdf
  • A. Iosifidis and M. Gabbouj, “On the kernel Extreme Learning Machine speedup”, Pattern Recognition Letters, vol. 68, pp. 205-210, 2015 pdf
  • A. Iosifidis, “Extreme Learning Machine based Supervised Subspace Learning”, Neurocomputing, vol. 167, pp. 158-164, 2015 pdf
  • A. Iosifidis, A. Tefas and I. Pitas, “Class-specific Reference Discriminant Analysis with application in Human Behavior Analysis”, IEEE Transactions on Human-Machine Systems, vol. 45, no. 3, 315-326, 2015 pdf
  • A. Iosifidis, A. Tefas and I. Pitas, “Sparse Extreme Learning Machine classifier exploiting Intrinsic Graphs”, Pattern Recognition Letters, vol. 65, pp. 192-196, 2015 pdf