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Dr. Anthony Quinn

Associate Professor (Electronic & Elect. Engineering)
ARAS AN PHIARSAIGH
      
Profile Photo

Dr. Anthony Quinn

Associate Professor (Electronic & Elect. Engineering)
ARAS AN PHIARSAIGH

 Anthony.Quinn@tcd.ie

 3531896 1863


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
Project Title
 Optimal Distributional Design for External Stochastic Knowledge Processing
From
1st January 2018
To
31st December 2020
Summary
Funding Agency
Czech Science Foundation (GACR)
Project Type
Principal Investigator
Project Title
 Robust Signal and Information Processing Using Bayesian Nonparametrics
From
26th September 2016
To
25th September 2017
Summary
Funding Agency
Fulbright Commission
Programme
Fulbright Scholarship
Project Title
 Bayesian Nonparametrics in Signal Processing
From
1st October 2010
To
30th September 2014
Summary
Funding Agency
SFI
Programme
Research Frontiers Programme
Project Title
 Knowledge Processing in Distributed Software Systems
From
1st September 2011
To
31st March 2017
Summary
Funding Agency
SFI
Programme
CSET-2
Project Title
 Variational Bayes Methods for Iterative Telcommunications Receiver Design
From
1st September 2008
To
31st August 2012
Summary
Funding Agency
SFI
Programme
Research Frontiers Programme

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Details Date
Active reviewer for many top-flight international journals and publishers 1993-date
Appointment as the University Observer for the Leaving Certificate Technology examinations (Department of Education) 2017-2022
Chair of the Irish Signals and Systems Conference, Trinity College Dublin, 2011 2011
Extensive international technical/scientific programme committee membership and service 1998-date
Invited review work for international research funding Agencies 2009-2016
External PhD Examinerships, in the French language, at the École Normale Supérieure (Supélec), Paris 2010, 2012
Language Skill Reading Skill Writing Skill Speaking
Czech Basic Basic Basic
French Fluent Fluent Fluent
Irish Fluent Fluent Fluent
Italian Fluent Medium Medium
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 2022
Irish Fulbright Alumni Association 2016 2020
Langbridge, A. and Quinn, A. and Shorten, R., Optimal Transport for Fairness: Archival Data Repair using Small Research Data Sets, 2024, pp237-245 , Notes: [cited By 0], Conference Paper, PUBLISHED  DOI
Kharman, A.M. and Ferraro, P. and Quinn, A. and Shorten, R., Robust Decentralised Proof-of-Position Algorithms for Smart City Applications, 2023, pp112-119 , Notes: [cited By 0], Conference Paper, PUBLISHED  DOI
Gallagher, S. and Quinn, A., A New Non-Separable Kernel for Spatio-Temporal Gaussian Process Regression, 2023, Notes: [cited By 0], Conference Paper, PUBLISHED  DOI
M. Papez and A. Quinn, Transferring model structure in Bayesian transfer learning for Gaussian process regression, Elsevier Knowledge-Based Systems, 2022, p13 , Journal Article, ACCEPTED
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 , Journal Article, PUBLISHED
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], Book Chapter, PUBLISHED
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 , Conference Paper, PUBLISHED
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 , Conference Paper, PUBLISHED
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 , Conference Paper, PUBLISHED
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], Book Chapter, PUBLISHED
  

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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, Report, PUBLISHED
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, Report, PUBLISHED
M. Papez and A. Quinn, Hierarchical Bayesian transfer learning between a pair of Kalman filters, IEEE Signal Processing Letters, 2019, p1 - 4, Notes: [under review], Journal Article, SUBMITTED
A. Quinn, Randomized Design of Bayesian Conditionals, 2018 Workshop on Bayesian Nonparametrics for Signal and Image Processing (BNPSI), Bordeaux, France, 2018, Université de Bordeaux, Invited Talk, PRESENTED

  


Award Date
Fulbright (Senior) Research 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
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.