Dr. Anthony Quinn

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

I am an expert in Bayesian methods (i.e. knowledge quantification and manipulation via probability) for signal processing, system analysis & machine learning for over thirty years. I work at the interface between electrical engineering, probability & statistics, being among the few internationally with grounding jointly in electrical/information systems, and in advanced statistical methodology. My one-year Fulbright research scholar position ('16-'17) at the renowned Michael Jordan group in the Statistics Department, UC Berkeley, reflects this, as do my invited lectures (UC Berkeley, UC Santa Cruz and UW Madison), and at Université de Bordeaux (2018). I focus on Bayesian answers to fundamental problems, seeking (a) a fuller understanding of the assumptions underlying classical & empirical solutions (what I call the `auditing property' of Bayesian methods), and (b) new designs arising out of Bayesian analysis (what I call the `prescriptive property'). Major contexts include: (i) Bayesian transfer learning: recent papers with Czech collaborators address machine learning and decision making problems, processing knowledge specifications broader than the crisp data sets of signal processing. 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 trade-offs between cost and accuracy. This is the central focus of the (widely cited) 2006 Springer monograph. (iii) Objective assessment of parametric model complexity, leading to optimal parsimony-prediction trade-offs. (iv) Nonparametric Bayesian methods for flexible model design and model-robust inference, obviating finite degrees-of-freedom constraints. Published applications: our recent Bayesian transfer learning mechanism allows external data from a patient population to be exploited, improving design of radio-iodine therapy for individual thyroid cancer patients in that population. 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. Key research achievements: . My international reputation is recognized by award of a one-year Fulbright scholarship in research, 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 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 and Communications Laboratory, which subsequently became a leading laboratory in this area. In recent years, these same Bayesian methods are central to the modern machine learning agenda. . I have contributed important results on performance bounds of Bayesian estimators, contrasting these with classical techniques that have dominated the signal processing state-of-the-art. . My Bayesian research has led to 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. . My research has led to important selective applications, particularly in medicine, image analysis and telecommunications.