Dr. Anthony Quinn
Associate Professor, Electronic & Elect. Engineering
Biography
Anthony Quinn is a senior academic (associate professor) in electronic engineering at Trinity College Dublin. He has had tenure there for twenty four years, and is a Fellow of the College since 2008. He has specialized for nearly thirty years in the development of theory and methods in statistical signal processing. He has an international reputation in Bayesian methodology, i.e. fully probabilistic methods for coping with uncertainty, inspiring optimal design and algorithm flows. He co-authored the first, and highly cited, book (Springer, 2006) on Bayesian methods for the design of signal processing algorithms that can be implemented efficiently (i.e. low energy and computational costs) on computers and specialized hardware. His algorithms have been applied in several practical contexts, notably in signal and image analysis for medical diagnosis and advisory system design. More recently, he has collaborated with his long-time partners in the Czech Academy of Sciences on important problems in Bayesian decision-making and design, with applications in distributed knowledge processing for sensor networks. This work has led to what is probably the first formal Bayesian definition of the transfer learning problem, a key concern in machine learning. He is developing new results in this area currently, as a one-year Fulbright visiting scholar at the renowned Michael Jordan group in Statistics at the University of California, Berkeley. He has recently secured an appointment as research scientist in the Department of Adaptive Systems at the Czech Academy of Sciences (2017-20). Professor Quinn is a committed educator in electrical engineering. He has originated and developed innovative courses in probability (which has been approved for the entire Junior Sophister engineering student cohort in his School) and in statistical signal processing (a module which he developed for the MAI Fifth Year programme in Electronic Engineering, and which was central to the success of the programme in professional accreditation). He has served in many senior roles professionally in his discipline and in his College. He named, lead-authored and developed the E3 and its Strategy in 2012, which received strong endorsement internationally. It has since been adopted as a priority infrastructure project by College. He serves recurrently, and for many years, on committees of several of the best international conferences and funding agencies. He was general chair of the Irish Signals and Systems Conference in 2011, and has acted as external examiner in Ireland and France.
Publications and Further Research Outputs
Peer-Reviewed Publications
M. Papez and A. Quinn, Transferring model structure in Bayesian transfer learning for Gaussian process regression, Elsevier Knowledge-Based Systems, 2022, p13
L. Pavelková, L. Jirsa and A. Quinn, Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances, Elsevier Knowledge-Based Systems, 238, (107879), 2022, p16
S. Murray and A. Quinn, Bayesian selective transfer learning for patient-specific inference in thyroid radiotherapy, 32nd IEEE Irish Signals and Systems Conference (ISSC), Athlone, Ireland, June 2021, IEEE, 2021, pp6
A. Barber and A. Quinn, Robust Bayesian transfer learning between autoregressive inference tasks, 32nd IEEE Irish Signals and Systems Conference (ISSC), Athlone, Ireland, June 2021, IEEE, 2021, pp6
Bayesian Transfer Learning between Uniformly Modelled Bayesian Filters in, Informatics in Control, Automation and Robotics: Lecture Notes in Electrical Engineering, Springer Lecture Notes in Electrical Engineering, Springer, 2021, pp21 , [L. Pavelková, L. Jirsa and A. Quinn]
M. Papez and A. Quinn, Hierarchical Bayesian transfer learning between a pair of Kalman filter, 32nd IEEE Irish Signals and Systems Conference (ISSC), Athlone, Ireland, June 2021, IEEE, 2021, pp6
Approximate Bayesian Prediction Using a State Space Model with Uniform Noise in, Informatics in Control, Automation and Robotics: Lecture Notes in Electrical Engineering, Springer Lecture Notes in Electrical Engineering, Springer, 2020, pp552 - 568, [L. Jirsa, L. Pavelková and A. Quinn]
M. Papez and A. Quinn, Bayesian transfer learning between Student-t filters, Elsevier Signal Processing Journal, 175, (107624), 2020, p1-36
L. Pavelková, L. Jirsa and A. Quinn, Bayesian filtering for states uniformly distributed on a parallelotopic support, 19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, UAE, December, 2019, edited by IEEE , 2019, pp1-6
L. Jirsa, L. Pavelková and A. Quinn, Knowledge transfer in a pair of uniformly modelled Bayesian filters, Proc. 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Prague, Czechia, July 2019, 2019, pp1-8
M. Papez and A. Quinn, Bayesian transfer learning between Gaussian process regression tasks, 19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, UAE, December 2019, edited by IEEE , 2019, pp1-6
M. Papez and A. Quinn, Robust Bayesian transfer learning between Kalman filters, 29th IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP), Pittsburg, US, October 2019, edited by IEEE , 2019, pp1-6
C. Foley and A. Quinn, Fully Probabilistic Design for Knowledge Transfer in a Pair of Kalman Filters, IEEE Signal Processing Letters, 25, (4), 2018, p487 - 490
M. Papez and A. Quinn, Dynamic Bayesian Knowledge Transfer between a Pair of Kalman Filters , IEEE Int. Workshop on Machine Learning for Signal Processing, Aalborg, Denmark, September 2018, IEEE, 2018, pp6
S. Azizi and A. Quinn, Hierarchical fully probabilistic design for deliberator-based merging in multiple participant systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, (4), 2018, p565 - 573
A. Quinn, M. Kárny and T.V. Guy, Optimal Design of Priors Constrained by External Predictors, International Jour. Approximate Reasoning, 84, 2017, p150 - 158
L. Jirsa, F. Varga and A. Quinn, Identification of Thyroid Gland Activity in Radioiodine Therapy, Informatics in Medicine Unlocked, 7, 2017, p23 - 33
A. Quinn, M. Kárny and T.V. Guy, Fully probabilistic design of hierarchical Bayesian models, Information Sciences, 369, 2016, p532 - 547
S. Azizi and A. Quinn, Approximate Bayesian filtering using stabilized forgetting, 23rd European Signal Processing Conference, EUSIPCO 2015, Nice, France, 2015, pp2711 - 2715
S. Azizi and A. Quinn, A data-driven forgetting factor for stabilized forgetting in approximate Bayesian filtering, 26th Irish Signals and Systems Conference, ISSC 2015, Carlow, Ireland, 2015, IEEE, 2015, pp6
A. Quinn and A. Jackson, E3: the Engineering, Energy and Environment Institute of Trinity College Dublin, Trinity College Dublin, June, 2012, 123 pp.
Tran, V.H. and Quinn, A., The transformed variational Bayes approximation, (5947288), 2011, pp4236-4239
A. Quinn, J.P. Barbot and P. Larzabal, The Bayesian Inference of Phase, Proc. IEEE Int. Conf. Acoust., Speech, Sig. Process. (ICASSP), Prague, 2011, pp4
V.H. Tran and A. Quinn, The Transformed Variational Bayes Approximation, Proc. IEEE Int. Conf. Acoust., Speech, Sig. Process. (ICASSP), Prague, 2011
A. Das and A. Quinn, A Variational Bayes Approach to Decoding in a Phase-Uncertain Digital Receiver, IET Irish Signals and Systems Conference, Trinity College Dublin, 23-24 June 2011, 2011
A. Quinn, Recursive Inference for Inverse Problems using Variational Bayes Methodology, Proc. 5th Int. ICST Conf. on Performance Evaluation Methodologies and Tools (VALUETOOLS), Cachan, France, 2011
V.H. Tran and A. Quinn, Variational Bayes Variants of the Viterbi Algorithm, Proc. 22nd IET Irish Signals and Systems Conference (ISSC), Dublin, 2011
V.H. Tran and A. Quinn, Online Bayesian Inference for a Mixture of Known Components, Proc. Irish Signals and Systems Conf. (ISSC), Cork, 2010
Cogan, B. and de Paor, A.M. and Quinn, A., PI control of first-order lag plus time-delay plants: Root locus design for optimal stability, Transactions of the Institute of Measurement & Control, 31, (5), 2009, p365-379
B. Cogan, A. de Paor and A. Quinn, PI Control of First-Order Lag plus Time-Delay Plants: Root Locus Design for Optimal Stability, Trans. Institute of Measurement and Control, 31, (5), 2009, p365 - 379
V. Smídl and A. Quinn, Variational Bayesian Filtering, IEEE Trans. Sig. Processing, 56, (10), 2008, p5020 - 5030
Identification of Thyroid Gland Activity in Radiotherapy in, Bayesian Statistics VIII, Oxford University Press, 2007, pp613 - 618, [L. Jirsa, A. Quinn, F, Varga]
V. Smídl and A. Quinn, Accelerated Particle Filtering using the Variational Bayes Approximation, Proc. IEEE Int. Conf. on Acoust., Speech and Sig. Process. (ICASSP), Hawaii, 2007
A. Quinn and M. Kárný, Learning for Nonstationary Dirichlet Processes, Jour. Adaptive Control and Signal Processing, 21, (10), 2007, p1 - 29
V. Smídl and A. Quinn, On Bayesian Principal Component Analysis, Jour. of Computational Stats. and Data Analysis, 51, (9), 2007, p4101 - 4123
V. Smídl and A. Quinn , The Variational Bayes Method in Signal Processing , Berlin, Heidelberg, Springer-Verlag, 2006, 247 pagespp
V. SmÍdl and A. Quinn, The Restricted Variational Bayes Approximation in Bayesian Filtering, Proc. IEEE Statist. Sig. Process. Workshop, Cambridge, (83), 2006, pp1 - 4
Å mÃdl, V. and Quinn, A., The restricted variational bayes approximation in Bayesian filtering, (4378860), 2006, pp224-227
V. SmÍdl and A. Quinn, The Variational Bayes Approximation in Bayesian Filtering, Proc. IEEE Int. Conf. on Acoust., Speech and Sig. Process. (ICASSP), Toulouse, 3, 2006, pp137 - 140
V. Smídl and A. Quinn , The Variational EM Algorithm for On-line Identification of Extended AR Models, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, U.S.A, 2005
V. Smídl, A. Quinn, M. Karný and T. V. Guy, Robust Estimation of Autoregressive Processes using a Mixture Based Filter Bank, Sys. And Cont. Letters, 54, (4), 2005, p315 - 323
Å mÃdl, V. and Quinn, A., The variational em algorithm for on-line identification of extended AR models, IV, (1415959), 2005, ppIV117-IV120
Senan Doyle, Anthony Quinn and Petr Gebousky, Bayesian Inference of Optimal Lymphoscintigraphic Sampling Times, IFMBE Proceedings EMBEC'05, 3rd European Medical and Biological Engineering Conference, Prague, Czech Republic, November 20-25, edited by Peter Kneppo and Jiri Hozman , 11, (1), International Federation for Medical and Biological Engineering, 2005, pp3255 - 3259
V. Smídl and A. Quinn , Mixture-Based Extension of the AR Model and its Recursive Bayesian Identification, IEEE Trans. Sig. Process, 53, (9), 2005, p3530 - 3542
V. Smídl and A. Quinn, Bayesian Estimation of Non-stationary AR Model Parameters via an Unknown Forgetting Factor, Proceedings of the IEEE Workshop on Digital Signal Processing, New Mexico, U.S.A., 2004
V. Smídl, A. Quinn and Y. Maniouloux, Fully Probabilistic Model for Functional Analysis of Medical Image Data, Proceedings of the Irish Signals and Systems Conference, Belfast, 2004
A. Quinn, P. Ettler, L. Jirsa, I. Nagy and P. Nedoma, Probabilistic Advisory Systems for Data-Intensive Applications, Int. J. Adapt. Control Signal Process, 17, 2003, p133 - 148
V. Smídl and A. Quinn , The Extended AR Model and its Bayesian Identification, Proceedings of the Irish Signals and Systems Conference, Limerick, 2003
V. Smídl and A. Quinn, Fast Variational PCA for Functional Analysis of Dynamic Image Sequences, Proceedings of the IEEE Workshop on Image and Signal Processing and its Applications, Rome, 2003
E. Ranguelova, A. Quinn, 3-D Methods for Difference Estimation in Volumetric Data, Proceedings of the IEEE Workshop on Image and Signal Processing and its Applications, Rome, 2003
Lymphoscintigraphy of Upper Limbs: a Bayesian Framework in, Valencia VII, Oxford University Press, 2003, pp543 - 552, [P. Gebouský, M. Karný and A. Quinn ]
V. Smídl and A. Quinn , Variational Methods in Dimensionality Reduction, Workshop on Advances in Information and Control Theory, Slovenia, 2002
E. Ranguelova, A. Quinn , Difference Estimation and Compensation via Entropy Minimization in 3-D Image Segmentation, Proceedings of the 2nd International Workshop on Spectral Methods and Multirate Signal Processing, Toulouse, France, 2002
E. Ranguelova, A. Quinn, Difference Field Estimation for Enhanced 3-D Texture Segmentation, Proceedings of the British Machine Vision Conference, Cardiff, Wales, 2002
E. Ranguelova, A. Quinn, Registration Preprocessing for Enhanced 3-D Segmentation, Proceedings of the Irish Signals and Systems Conference, Maynooth, 2001
Bayesian Identification of Non-Linear Parameters in Signal and System Models in, Attractors, Signals and Synergetics, Berlin, Pabst Science Publishers, 2001, pp284 - 294, [A. Quinn]
V. Smídl, M. Kárný and A. Quinn , On Prior Information in Principal Component Analysis, Proceedings of the Irish Signals and Systems Conference, Maynooth, 2001
L. Jirsa and A. Quinn, Mixture Analysis of Nuclear Medicine Data: Medical Decision Support, Proceedings of the Irish Signals and Systems Conference, Maynooth, 2001
L. Tesar and A. Quinn , Method for Artefact Detection and Suppression Using Alpha-Stable Distributions, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA), Prague, Czech Republic, 2001
L. Tesar and A. Quinn , Detection and Removal of Outliers from Multidimensional AR Processes, Proceedings of the Irish Signals and Systems Conference, Maynooth, 2001
L. Tesar and A. Quinn , A Survey of Techniques for Pre-Processing in High-Dimensional Data Clustering, PhD Workshop on Cybernetics and Informatics, Marianska, Czech Republic, 2000
E. Ranguelova, A. Quinn , Disparity-Compensated Segmentation of 3-D Images, PhD Workshop on Cybernetics and Informatics, Marianska, Czech Republic, 2000
E. Ranguelova, A. Quinn, Analysis and Synthesis of Three-Dimensional Gaussian Markov Random Fields, Proceedings of the IEEE International Conference on Image Processing (ICIP), Kobe, Japan, 1999
Clark, E. and Quinn, A., Data-driven Bayesian sampling scheme for unsupervised image segmentation, 6, 1999, pp3497-3500
E. Clark and A. Quinn , A Data-Driven Bayesian Sampling Scheme for Unsupervised Image Segmentation, 6, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, U.S.A., 2307, 1999
Reichel, J. and Quinn, A., Fast and fully unsupervised scheme for model-based image segmentation, 3459, 1998, pp82-90
Regularized Signal Identification using Bayesian Techniques in, Attractors, Signals and Synergetics, Boston, Birkhäuser , 1998, pp151 - 162, [A. Quinn]
J. Reichel and A. Quinn, A Fast and Fully Unsupervised Scheme for Model-Based Image Segmentation, Proceedings of the SPIE Conference on Bayesian Inference for Inverse Problems, San Diego, U.S.A., 1998, pp82 - 90
A. Quinn, Regularization Strategies for Signal Identification, The European Association for Signal Processing (EURASIP), Proceedings of the First European Conference on Signal Analysis and Prediction, Prague, Czech Republic, 1997, pp145 - 148
A. Quinn, Novel Parameter Priors for Bayesian Signal Identification, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Munich, Germany, 5, (3909), 3912, 1997
O. Schwartz and A. Quinn , New Approaches to Markov Random Field-Based Image Segmentation, Proceedings of the Irish DSP and Control Conference, Dublin, Ireland, 1996, pp323 - 330
O. Schwartz and A. Quinn , Fast and Accurate Texture-Based Image Segmentation, Proceedings of the IEEE International Conference on Image Processing, Lausanne, Switzerland, 3, 1996, pp121 - 124
A. Quinn, A Mathematical Framework for Signal Identification, Proceedings of the Eighth URSI Symposium on Radio Science, paper No. 7, Royal Irish Academy, Dublin, Ireland, 1996
A. Quinn, A General Complexity Measure for Signal Identification using Bayesian Inference, Proceedings of the IEEE Workshop on Nonlinear Signal and Image Processing, Halkidiki, Greece, 2, 1995, pp843 - 846
A. Quinn, The Modelling of Signals and Systems amid Uncertainty: a Bayesian Perspective, Proceedings of the EURACO Workshop on Recent Results in Robust and Adaptive Control, Florence, Italy, 1995, pp514 - 531
M. C. Musgrave and A. Quinn , Texture Analysis of Terrain Images using a Markov Random Field Model, Proceedings of the Fifth IEE International Conference on Image Processing and its Applications (IPA), Edinburgh, Scotland, 1995, pp781 - 785
Quinn, A., A new objective measure of signal complexity using Bayesian inference, (572444), 1994, pp79-82
The Objective Admission of Ockham's Razor by Marginalization in Bayesian Model-Based Inference in, Mathematics in Signal Processing III, Oxford University Press, 1994, pp339 - 357, [A. Quinn]
A. Quinn, A New Objective Measure of Signal Complexity using Bayesian Inference, Proceedings of the Seventh IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP), Quebec City, Canada, 1994
A. Quinn, Prediction of the Asymptotic and Threshold Behaviour of Bayesian Signal Parameter Estimators, Signal Processing VII: Proceedings of the Seventh European Signal Processing Conference (EUSIPCO), The European Association for Signal Processing (EURASIP). Edinburgh, Scotland, 3, 1994, pp1839 - 1842
A. Quinn, New Lower Bounds to the Variance of Signal Parameter Estimators using Bayesian Inference, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Adelaide, Australia, 4, 1994, pp493 - 496
A. Quinn, A consistent, numerically efficient Bayesian framework for combining the Selection, Detection and Estimation tasks in Model-Based Signal Processing, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Minneapolis, U.S.A., 4, 1993, pp65 - 68
A. Quinn, Censored Marginal a Posteriori Bayesian Inference for Signal Models, Proceedings of the IEEE Winter Workshop on Nonlinear Digital Signal Processing, Section 6.3, Tampere, Finland, 1993, pp5.1 - 5.6
A. Quinn, Threshold-Free Bayesian Estimation using Censored Marginal Inference, Signal Processing VI: Proceedings of the Sixth European Signal Processing Conference (EUSIPCO), The European Association for Signal Processing (EURASIP), Brussels, Belgium, 2, 1992, pp677 - 680
A. Quinn, A Unified Approach to Model-Based Signal Processing using Bayesian Marginal Inference, Proceedings of the Sixth IEEE Signal processing Workshop on Statistical Signal and Array Processing (SSAP), Victoria, B.C., Canada, 1992
Quinn, A.P., The performance of Bayesian estimators in the superresolution of signal parameters, 5, (226624), 1992, pp297-300
Quinn, A.P., A unified approach to model-based signal processing using Bayesian marginal inference, (246857), 1992, pp98-101
A. Quinn, The Performance of Bayesian Estimators in the Superresolution of Signal Parameters, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), San Francisco, U.S.A., 5, 1992, pp297 - 300
A. M. dePaor, A. Quinn and A. J. Murphy, The Sinusoidal Instantaneous Frequency Extractor: A New Instrument for use in Speech Therapy, Innovation et Technologie en Biologie et Médecine, 13, (6), 1992, p635 - 640
A. Quinn and M. D. Macleod, A Study of Joint and Marginal Bayesian Estimators for the Parameters of Periodic Signals in Gaussian White Noise, Proceedings of the IEEE International Conference on Communication Systems (ICCS), Section 27, Singapore, 1990, pp1.1 - 1.5
A. Quinn, J. Murphy and A.M. de Paor, 'The Sinusoidal Instantaneous Frequency Extractor for Speech Therapy', Irish Patents Office , 1988
Non-Peer-Reviewed Publications
Á. Hoffmann and A. Quinn, Ockham's Razor from a fully probabilistic design perspective, 2391, Czech Academy of Sciences, Institute of Information Theory and Automation, January , 2022, 9
S. Nugent and A. Quinn, Transferring improved local kernel design in multi-source Bayesian transfer learning, with an application in air pollution monitoring in India, 2392, Czech Academy of Sciences, Institute of Information Theory and Automation, January, 2022, 23
M. Papez and A. Quinn, Hierarchical Bayesian transfer learning between a pair of Kalman filters, IEEE Signal Processing Letters, 2019, p1 - 4
A. Quinn, Randomized Design of Bayesian Conditionals, 2018 Workshop on Bayesian Nonparametrics for Signal and Image Processing (BNPSI), Bordeaux, France, 2018, Université de Bordeaux
Research Expertise
Description
I am an expert in Bayesian methods (i.e. knowledge representation & inference via probability) for signal processing & machine learning, for 34 years. I work at the interface between electrical engineering (EE), probability & statistics, being among the few internationally with formal grounding jointly in EE, and advanced statistical methodology. My 1-year Fulbright research scholar position ('16-'17) at the renowned Michael Jordan group in the Stats Department, UC Berkeley, reflects this, as do invited lectures (UC Berkeley, UC Santa Cruz and UW Madison), at Université de Bordeaux (2018), Linköping (2008), SUNY NY (1997), etc., as well as international appointments in Prague and Paris (Section 3.2). I seek (a) fuller understanding of the assumptions underlying classical & empirical statistical signal processing solutions (what I call the `auditing property' of Bayesian methods), and (b) new designs arising out of Bayesian analysis (the `prescriptive property'). Major contexts include: (i) Bayesian transfer learning: major recent papers address machine learning and decision making problems, dynamically processing broad knowledge specifications. Designs are randomized, conferring model robustness and replacing selection with exploration strategies. A major context is probabilistic knowledge processing in distributed sensor networks. (ii) Design of recursive computational algorithms. These can be hosted by mobile computing hardware, providing attractive cost-accuracy trade-offs, a central focus of my (widely cited) 2006 Springer monograph, the first in the topic. (iii) Objective assessment of parametric model complexity, leading to optimal parsimony-prediction trade-offs. (iv) Nonparametric Bayesian methods for flexible model-robust inference, obviating finite degrees-of-freedom constraints. Published applications: Bayesian transfer learning allows external data from a patient population to improve the design of radio-iodine therapies for individual thyroid cancer patients. I have also published applications in reconstruction of noisy functional scintigraphic images of the human kidney; a variational Bayes (VB) method for smoothing impulsively corrupted speech; a high-performing VB variant of the state-of-the-art Viterbi algorithm for iterative symbol detection in a mobile digital receiver; and, recently, improved atmospheric pollutant (PM2.5) prediction in India via Bayesian knowledge transfer between nonparametrically modelled sensors. Key research achievements: . My international reputation is recognized by award of a 1-year Fulbright senior research fellowship, hosted by Michael Jordan at UC Berkeley. As part of my Fulbright sabbatical, I delivered a series of invited lectures across America in 2017. . I co-wrote the first book-length treatment (Springer, 2006) of the variational Bayes (VB) approximation in signal processing, which continues to be highly cited. . I was pioneering in migrating Bayesian methods into the arena of signal processing and system analysis, writing the first PhD thesis on the subject (1992) in the Cambridge Signal Processing Laboratory. In recent years, these same Bayesian methods are being widely uptaken within the deep learning toolset. . Important results on performance bounds of Bayesian estimators, contrasting these with classical techniques that have dominated the signal processing state-of-the-art. . New computational flows, implementable on digital computers, for recursive and adaptive signal processing, free from many of the restrictive (linear, Gaussian) assumptions of the implemented state-of-the-art. . I have a long-term (4.5-year) funded appointment as a research scientist in the Czech Academy of Sciences (Section 3.2), employing three postdocs, plus 4 interns from TCD, working on topics ranging from radio-iodine metabolism in thyroid cancer treatment, to pollution prediction in India.Projects
- Title
- Optimal Distributional Design for External Stochastic Knowledge Processing
- Funding Agency
- Czech Science Foundation (GACR)
- Date From
- 1st January 2018
- Date To
- 31st December 2020
- Title
- Robust Signal and Information Processing Using Bayesian Nonparametrics
- Funding Agency
- Fulbright Commission
- Date From
- 26th September 2016
- Date To
- 25th September 2017
- Title
- Bayesian Nonparametrics in Signal Processing
- Funding Agency
- SFI
- Date From
- 1st October 2010
- Date To
- 30th September 2014
- Title
- Knowledge Processing in Distributed Software Systems
- Funding Agency
- SFI
- Date From
- 1st September 2011
- Date To
- 31st March 2017
- Title
- Variational Bayes Methods for Iterative Telcommunications Receiver Design
- Funding Agency
- SFI
- Date From
- 1st September 2008
- Date To
- 31st August 2012
- Title
- Bayesian Design for Iterative Turbo-Receivers
- Funding Agency
- École Normale Supérieure de Cachan, France
- Date From
- 1st September 2011
- Date To
- 30th September 2011
- Title
- Variational Bayesian Signal Processing
- Funding Agency
- École Normale Supérieure de Cachan, France
- Date From
- 1st September 2009
- Date To
- 30th September 2009
- Title
- Hierarchical Fully Probabilistic Design for Decision Making and Machine Learning
- Funding Agency
- Czech Academy of Sciences
- Date From
- 1st July 2014
- Date To
- 31st December 2016
- Title
- Rationality and Deliberation
- Funding Agency
- Czech National Funding Agency (GACR)
- Date From
- 1st January 2016
- Date To
- 31st December 2018
- Title
- ICT 2013- Vilnius
- Funding Agency
- Enterprise Ireland
- Date From
- 31st October 2013
- Date To
- 30th April 2014
- Title
- Collaboration with Univ. Paris V
- Funding Agency
- Forbairt/CNRS
- Date From
- 1995
- Date To
- 1998
- Title
- Visit S.U.N.Y.
- Funding Agency
- Forbairt
- Date From
- 1997
- Title
- Computer-Aided Diagnosis
- Funding Agency
- Forbairt
- Date From
- 1998
- Date To
- 1998
- Title
- High-Performance Computing Scholarship
- Funding Agency
- T.C.D./Queen's
- Date From
- 1998
- Date To
- 1998
- Title
- ProDaCTool (Phase I)
- Funding Agency
- ESPRIT (E.U.)
- Date From
- 1998
- Date To
- 1998
- Title
- Medical Imaging
- Funding Agency
- Provost's Fund
- Date From
- 1999
- Date To
- 1999
- Title
- ProDaCTool (Phase II)
- Summary
- Funding Agency
- ESPRIT (E.U. Fifth Framework)
- Date From
- 2000
- Date To
- 2002
- Title
- Research Incentive Grant
- Funding Agency
- T.C.D.
- Date From
- 2001
- Date To
- date
Recognition
Representations
Active reviewer for many top-flight international journals and publishers
Appointment as the University Observer for the Leaving Certificate Technology examinations (Department of Education)
Chair of the Irish Signals and Systems Conference, Trinity College Dublin, 2011
Extensive international technical/scientific programme committee membership and service
Invited review work for international research funding Agencies
External PhD Examinerships, in the French language, at the École Normale Supérieure (Supélec), Paris
Awards and Honours
Fulbright (Senior) Research Scholar, and Visiting Scholar at the Statistics Department, University of California, Berkeley
Fellow of Trinity College Dublin (FTCD)
First Prize for the best publication in the Theory of Information and Automation, Czech Academy of Sciences, for the monograph: 'The Variational Bayes Method in Signal Processing', Smidl, V. and Quinn, A., Springer 2006
Memberships
Member of the IEEE
Member of the International Society for Bayesian Analysis (ISBA)
Irish Fulbright Alumni Association