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Dr. Anthony Quinn

Associate Professor (Electronic & Elect. Engineering)

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.
  BAYESIAN INFERENCE   Bayesian signal and system identification   Bayesian signal processing in medicine and communications   Communication theory   Digital signal processing   Estimation   Image segmentation   Information theory   Probability theory   Signal Processing   Variational / stochastic approximations Bayesian inference
 Collaboration with Univ. Paris V
 Visit S.U.N.Y.
 Computer-Aided Diagnosis
 High-Performance Computing Scholarship
 ProDaCTool (Phase I)

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Reviewer for international journals/publishers: . Very Active reviewer for many of the best journals in his field, such as the IEEE Transactions on Signal Processing, Image Processing, Information Theory, and Signal Processing Letters; IEE Proceedings on Vision, Image and Signal Processing; and Automatica. . Served on Editorial Board of Elsevier journal, "Signal Processing" (2011-12). . Was chosen as academic reviewer of the major textbook by MacMillan (2002), "Digital Signal Processing", Mulgrew, Grant and Thompson. 1993-date
Chair of the Irish Signals and Systems Conference, Trinity College Dublin, 2011: . Assembled an outstanding set of four international plenary speakers (Purdue University, University of Edinburgh, Aalborg University, MIT). . Single-handedly raised €10,200 from Irish technology companies to support the conference and see it safely into the future. . Inaugurated the Best Paper award and a Best Maths Paper award to encourage standards and interest in mathematical methods in Irish signals and systems research. . Organized innovative opening session on circuit design and implementation with aim of attracting delegates from the Irish Microelectronics Industry Design Association (MIDAS). The session was heavily attended by delegates from industry, with a very positive interchange between them and the industry and academic delegations. 2011
Technical/Scientific Programme Committee membership and service: . EURASIP European Signal Processing Conference (2012-15). . International Conference on New Computational Methods for Inverse Problems, Cachan, France (2011-17). . Irish Signals and Systems Conference (ISSC) Steering Committee (1998-2017). Extensive reviewing work for the IEEE International Conference on Image Processing (regularly since 2002). 1998-date
Review work/invitations for International Funding Agencies: . Czech National Funding Agency (GAČR) (2009, 2012, 2015, 2016) . French Research Agency (CNRS) (2012) . Vienna Science and Technology Fund (2007) . European Commission ERC scheme (2016) 2009-2016
External PhD Examiner, in the French language, at the École Normale Supérieure (Supélec), Paris, in both 2010 and 2012. 2010, 2012
Language Skill Reading Skill Writing Skill Speaking
Czech Basic Basic Basic
French Fluent Fluent Fluent
Irish Fluent Fluent Fluent
Latin Fluent Fluent Fluent
Details Date From Date To
Member of the IEEE 1991 2013
Member of the International Society for Bayesian Analysis (ISBA) 2008 2015
Fulbright Alumni Association 2016 present
A. Quinn, M. Kárny and T.V. Guy, Optimal Design of Priors Constrained by External Predictors, International Jour. Approximate Reasoning, 84, 2017, p150 - 158, Journal Article, PUBLISHED  DOI
L. Jirsa, F. Varga and A. Quinn, Identification of Thyroid Gland Activity in Radioiodine Therapy, Informatics in Medicine Unlocked, 7, 2017, p23 - 33, Journal Article, PUBLISHED
S. Azizi and A. Quinn, Hierarchical Fully Probabilistic Design for Deliberator-Based Merging in Multiple Participant Systems, IEEE Trans. Systems, Man, Cybernetics: Systems, (99), 2016, p1-9 , Journal Article, PUBLISHED
A. Quinn, M. Kárny and T.V. Guy, Fully probabilistic design of hierarchical Bayesian models, Information Sciences, 369, 2016, p532 - 547, Notes: [Export Date: 27 September 2016], Journal Article, PUBLISHED  DOI  URL
Azizi S, Quinn A, Approximate Bayesian filtering using stabilized forgetting, 2015 23rd European Signal Processing Conference, EUSIPCO 2015, 2015, 2015, pp2711 - 2715, Notes: [Export Date: 27 September 2016], Conference Paper, PUBLISHED  DOI  URL
Azizi S, Quinn A, A data-driven forgetting factor for stabilized forgetting in approximate Bayesian filtering, 2015 26th Irish Signals and Systems Conference, ISSC 2015, 2015, 2015, Notes: [Export Date: 27 September 2016], Conference Paper, PUBLISHED  DOI  URL
Quinn, A. and Jackson, A., E3: the Engineering, Energy and Environment Institute of Trinity College Dublin, Trinity College Dublin, June, 2012, 123 pp., Notes: [], Report, PUBLISHED
Arijit Das and Anthony 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, Conference Paper, PUBLISHED  TARA - Full Text  URL
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, Conference Paper, PUBLISHED
A. Quinn, J.P. Barbot, P. Larzabal, The Bayesian Inference of Phase, Proc. IEEE Int. Conf. Acoust., Speech, Sig. Process. (ICASSP), Prague, 2011, Conference Paper, PUBLISHED

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Award Date
Fulbright Scholar and Visiting Scholar at the Statistics Department, University of California, Berkeley 2016-17
Fellow of Trinity College Dublin (FTCD) 2008
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 2007
Research expertise: Bayesian statistical theory & methods; Bayesian nonparametrics for robustness & flexible design; optimal decision making & estimation; signal processing; transfer machine learning; distributed knowledge processing in sensor networks; information-theoretic criteria; Bayesian inferential approximation; design of recursive algorithms for real-time signal processing & digital receiver design; image analysis (texture modelling & segmentation); computer-aided diagnosis for medicine; advisory system design. Research summary: Prof. Quinn has specialized in Bayesian methods for signal processing, system analysis & machine learning for twenty nine years. He works at the interface between electrical engineering, probability & statistics, and is among the few internationally with grounding jointly in electrical/information systems analysis & design, and in advanced statistical methodology. His current position as one-year Fulbright visiting scholar in the renowned Michael Jordan group in the Statistics Department at UC Berkeley reflects this, as do his invited lectures (UC Berkeley, UC Santa Cruz and UW Madison to date) in 2017. Bayesian methods focus on fully probabilistic settings for information-bearing processes/systems. Prof. Quinn focusses on Bayesian answers to fundamental problems. Rather than applications as primary goal, he applies his methods selectively. Typical outputs are therefore (a) fuller understanding of the assumptions underlying classical & empirical solutions (what he calls the `auditing property' of Bayesian methods), and (b) new designs arising out of Bayesian analysis (what he calls the `prescriptive property'). The following is a selective list of the `fundamental problems' above: (i) Objective assessment of parametric model complexity, leading to optimal trade-offs between parsimony and prediction; many publications in model order inference, signal detection, threshold-free parameter estimation, and automatic rank determination (ARD) in matrix decomposition. (ii) Design of recursive computational algorithms for flexible classes of observation models. These Bayesian algorithms - which can be hosted by computing hardware - provide attractive trade-offs between cost and accuracy. This is the central focus of Professor Quinn's (widely cited) 2006 Springer monograph with a former student, and related papers. (iii) Nonparametric Bayesian solutions for flexible model design and model-robust inference. Here, the model itself is inferred, without finite degrees-of-freedom constraints. (iv) Randomized design for knowledge processing and transfer learning. A number of major recent papers with his Czech collaborators address machine learning and decision making problems, processing knowledge specifications broader than the crisp data sets of signal processing. Designs are randomized, thereby allowing exploration, and circumventing the need for optimization. These contexts have been explored in selective applications. Respectively: (i) ARD led to improved reconstruction of noisy functional scintigraphic images of the human kidney; (ii) a novel variational Bayes filter-bank provided excellent smoothing of impulsively corrupted speech; in telecommunications, an iterative symbol detector for a digital receiver yielded improved performance over state-of-the-art Viterbi algorithm; (iii) a nonparametric stopping rule for sampling from unknown distributions revealed a new non-uniform quantizer design; and (iv) an application in distributed knowledge processing for sensor networks yielded a new way of pooling distributions, equipping it with error bounds and providing improved prediction; a new Bayesian transfer learning mechanism allowed external data from a patient population to be exploited, improving design of radio-iodine therapy for individual thyroid cancer patients.