Mário A. T. Figueiredo
Born (1962-01-05) January 5, 1962
Luanda, Angola
NationalityPortuguese
Occupation(s)Academic and researcher in electrical and computer engineering
AwardsFellow, International Association for Pattern Recognition (IAPR)
Fellow, Institute of Electrical and Electronics Engineers (IEEE)
Fellow, European Association for Signal Processing (EURASIP)
Pierre Devijver Award, IAPR
Technical Achievement Award, EURASIP
W. R. J. Baker Award, IEEE
Academic background
EducationM.Sc., Electrical and Computer Engineering, IST, 1990
Ph.D., Electrical and Computer Engineering, IST, 1994
Agregação (habilitation), Electrical and Computer Engineering, IST, 2004
Alma materInstituto Superior Técnico (IST), University of Lisbon
Academic work
InstitutionsIST, University of Lisbon

Mário A. T. Figueiredo (born January 5, 1962) is a Portuguese engineer, academic, and researcher. He is an IST Distinguished Professor and holds the Feedzai chair of machine learning at IST, University of Lisbon.[1]

Figueiredo's research interests include signal and image processing, focusing on image inverse problems, along with machine learning, with a focus on statistical approaches. Additionally, he has worked on applications to medical imaging and remote sensing, and mathematical optimization applied to imaging inverse problems and machine learning.[2]

Figueiredo is a Fellow of EURASIP (European Association for Signal Processing),[3] IEEE (Institute of Electrical and Electronics Engineers)[4] and of IAPR (International Association of Pattern Recognition).[5] He is a Senior Editor at the IEEE Signal Processing Magazine[6] and at IEEE Transactions on Computational Imaging[7] and he was also an Associate Editor at SIAM Journal on Imaging Science, and many other journals.[8] He is an ELLIS Fellow,[9] and the head of LUMLIS, the Lisbon ELLIS unit.[10]

Education

In 1990, Figueiredo received his master's degree from the Instituto Superior Técnico (IST) at the University of Lisbon, Portugal in Electrical and Computer Engineering. He also earned his Ph.D. and his habilitation (Agregação) in Electrical and Computer Engineering from the same institute in 1994 and 2004 respectively.[11]

Career

Figueiredo began his academic career in 1994 as an assistant professor of the Department of Electrical and Computer Engineering at IST, University of Lisbon. From 2004 to 2010, he became an associate professor there.[12] Since then, he has been promoted to full professor and in 2019, an IST Distinguished Professor for the same department. He is also the Feedzai Professor of machine learning at the same university since 2019.[13]

Figueiredo has been a visiting scholar of the Department of Computer Science and Engineering at the Michigan State University in 1998[14] and also of the Department of Electrical and Computer Engineering at the University of Wisconsin in 2005 and 2014.

Research

Figueiredo has authored over 360 publications. He has focused his research on machine learning,[15] signal processing and image processing, with particular attention on imaging inverse problems, probabilistic/statistical approaches, and applications to medical imaging and remote sensing.[16]

Signal processing and image processing

Figueiredo proposed gradient projection (GP) algorithms, and discussed their application in terms of the bound-constrained quadratic programming (BCQP) formulation of compressed sensing and other inverse problems.[17] He also provided sparse approximate solutions to large underdetermined linear systems of equations and regarded them a common problem in signal/image processing and statistics.[18] In 2010, he co-authored pioneering work on the usage of ADMM (alternating direction method of multipliers) for imaging inverse problems, namely image deblurring.[19] Furthermore, he described first application of the ADMM regarding the restoration of images corrupted with Poisson noise,[20] and to solve the problem of hyperspectral unmixing, a central problem in hyperspectral imaging, widely used in remote sensing.[21]

In 2003, he proposed the first efficient algorithm for wavelet-based image restoration.[22]

Machine learning

In his paper published in 2003, Figueiredo described a hierarchical Bayesian approach to adaptive sparse regularization in supervised learning.[23] He also focused his study on mixture models, and published a paper which seamlessly integrates model estimation and selection in an unsupervised algorithm.[24] He highlighted the concept of feature saliency and introduced an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering.[25] In 2005, he proposed the first fast algorithm for sparse logistic regression. He also explored a new family of nonextensive mutual information kernels, which includes the Boolean, Jensen-Shannon, and linear kernels as particular cases.[26]

Awards and honors

  • 1995 - Portuguese IBM Scientific Prize
  • 2009 - Fellow, International Association for Pattern Recognition (IAPR)[5]
  • 2010 - Fellow, Institute of Electrical and Electronics Engineers (IEEE)[4]
  • 2011 - IEEE Signal Processing Society Best Paper Award
  • 2014 - W. R. G. Baker Award, IEEE
  • 2016 - Individual Technical Achievement Award, EURASIP[27]
  • 2016 - Pierre Devijver Award, IAPR[28]
  • 2019 - Member, the Portuguese Academy of Engineering[29]
  • 2019 - Corresponding Member, the Lisbon Academy of Sciences[30]
  • 2019 - Holder of the endowed chair “Feedzai Professor of Machine Learning” at IST.
  • 2020 - Fellow, EURASIP[3]
  • 2023 - Effective Member, the Lisbon Academy of Sciences[31]

Bibliography

  • Figueiredo, M. A., Nowak, R. D., & Wright, S. J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of selected topics in signal processing, 1(4), 586–597.
  • Figueiredo, M. A. T., & Jain, A. K. (2002). Unsupervised learning of finite mixture models. IEEE Transactions on pattern analysis and machine intelligence, 24(3), 381–396.
  • Wright, S. J., Nowak, R. D., & Figueiredo, M. A. (2009). Sparse reconstruction by separable approximation. IEEE Transactions on signal processing, 57(7), 2479–2493.
  • Bioucas-Dias, J. M., & Figueiredo, M. A. (2007). A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Transactions on Image processing, 16(12), 2992–3004.
  • Figueiredo, M. A., & Nowak, R. D. (2003). An EM algorithm for wavelet-based image restoration. IEEE Transactions on Image Processing, 12(8), 906–916.

References

  1. ↑ "Mário Alexandre Teles de Figueiredo".
  2. ↑ "Prof. Mário Figueiredo".
  3. 1 2 "EURASIP Fellows".
  4. 1 2 "IEEE Fellows Directory".
  5. 1 2 "IAPR Fellows".
  6. ↑ "Editorial Board". 25 February 2016.
  7. ↑ "Technical Liaison Committee/Editorial Board". 2 March 2016.
  8. ↑ "Editorial Board".
  9. ↑ "EELIS Fellows".
  10. ↑ "LUMLIS".
  11. ↑ "Today's world is extremely visual, we are immersed in images".
  12. ↑ "Professor Mário Figueiredo nominated Fellow of the European Association for Signal Processing".
  13. ↑ "Mario A. T. Figueiredo".
  14. ↑ "All ECE Events".
  15. ↑ "ARTIFICIAL INTELLIGENCE - APPLICATIONS (THE GOOD AND THE BAD)".
  16. ↑ "Mario A. T. Figueiredo".
  17. ↑ "Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems" (PDF).
  18. ↑ "Sparse Reconstruction by Separable Approximation" (PDF).
  19. ↑ Afonso, Manya V.; Bioucas-Dias, José M.; Figueiredo, Mário A T. (2010). "Fast Image Recovery Using Variable Splitting and Constrained Optimization". IEEE Transactions on Image Processing. 19 (9): 2345–2356. arXiv:0910.4887. Bibcode:2010ITIP...19.2345A. doi:10.1109/TIP.2010.2047910. PMID 20378469. S2CID 8426429.
  20. ↑ Figueiredo, M A T.; Bioucas-Dias, J. M. (2010). "Restoration of Poissonian Images Using Alternating Direction Optimization". IEEE Transactions on Image Processing. 19 (12): 3133–3145. arXiv:1001.2244. Bibcode:2010ITIP...19.3133F. doi:10.1109/TIP.2010.2053941. PMID 20833604. S2CID 6611971.
  21. ↑ Bioucas-Dias, Jose M.; Figueiredo, Mario A. T. (2010). "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing". 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. pp. 1–4. doi:10.1109/WHISPERS.2010.5594963. ISBN 978-1-4244-8906-0. S2CID 11927662.
  22. ↑ Figueiredo, M.A.T.; Nowak, R.D. (2003). "An EM algorithm for wavelet-based image restoration". IEEE Transactions on Image Processing. 12 (8): 906–916. Bibcode:2003ITIP...12..906F. doi:10.1109/TIP.2003.814255. PMID 18237964. S2CID 10428338.
  23. ↑ Figueiredo, M.A.T. (2003). "Adaptive sparseness for supervised learning". IEEE Transactions on Pattern Analysis and Machine Intelligence. 25 (9): 1150–1159. doi:10.1109/TPAMI.2003.1227989. S2CID 13358226.
  24. ↑ Figueiredo, M.A.T.; Jain, A.K. (2002). "Unsupervised learning of finite mixture models". IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (3): 381–396. doi:10.1109/34.990138.
  25. ↑ Law, M.H.C.; Figueiredo, M.A.T.; Jain, A.K. (2004). "Simultaneous feature selection and clustering using mixture models". IEEE Transactions on Pattern Analysis and Machine Intelligence. 26 (9): 1154–1166. doi:10.1109/TPAMI.2004.71. PMID 15742891. S2CID 7218153.
  26. ↑ "Nonextensive Information Theoretic Kernels on Measures" (PDF).
  27. ↑ "EURASIP Awards: Announcement 2016 Society Awards".
  28. ↑ "THE INTERNATIONAL ASSOCIATION FOR PATTERN RECOGNITION" (PDF).
  29. ↑ "Academia de Engenharia, Membros".
  30. ↑ "Academia das Ciências de Lisboa, Correspondentes Nacionais".
  31. ↑ "Classe de Ciências – Academia das Ciências de Lisboa" (in European Portuguese). Retrieved 2023-07-18.
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