C.V.

fintan.nagle.10@ucl.ac.uk        

Education

  • PhD Experimental Psychology, UCL, December 2015. I worked with Prof. Alan Johnston on the modelling and psychophysics of dynamic visual search in natural scenes.
  • MRes Modelling Biological Complexity, UCL CoMPLEX, 2011. Merit (68%).         
  • MSc Natural Computation, Dept. of Computer Science, University of York, 2010. Merit (72%).
  • BSc (Honours) First Class in Computer Science, University of York, 2009 (averaging 78% in  final year).
  • French Baccalaureat (scientific section), Lycée Jean-Baptiste Darnet, St-Yrieix-la-Perche, France; 2006. Overall grade 77.5%, roughly the equivalent of 3 A’s at A level.

Employment

  • Convenor in Psychology, New College of the Humanities, 2014 - present. Module design (psychology and cognitive science), lecturing, and conference organisation.
  • Software engineer in machine learning, Unii Ltd, May 2015 - present. Neural network design for text filtering and image classification.
  • Various consulting work in software engineering and web design
  • Various student jobs in retail and charity volunteering

Teaching

  • Module Leader, Introduction to Research and Statistics on the Postgraduate Certificate in Clinical Ophthalmic Practice, UCL Institute of Ophthalmology. A challenging course involving 30 mature students. December 2014 - present
  • Developed and taught short course, Introduction to Programming, at UCL CoMPLEX (2013 and 2014)
  • 400+ hours private tuition in programming, biology and neuroscience
  • Taught two week-long courses on high-performance scientific computing using MATLAB.
  • Departmental tours for undergraduate applicants at York and UCL; demonstrator at Royal Society exhibition on face recognition (3 days).

Research interests

  • Object recognition, temporal visual search and features
  • The neurological basis of visual perception: encodings and functional substrates
  • Bio-inspired and non-standard computation
  • Knowledge representation for automated model comparison

Languages and techniques

  • EEG: theory, SSVEPs, ERPs, multivoxel analysis, source separation, practical recording experience.
  • MRI: theory, searchlight method, DTI
  • Python, C, C++, Matlab, Scheme, Haskell, Church.
  • Machine learning: optimisation, regression, backpropagation, convolutional networks, deep learning
  • Bayesian theory: model comparison, Markov chain Monte Carlo
  • High-performance computing: experience in parallel Matlab on a 128-core HPC cluster. Expert with the Amazon Compute Cloud (spin up unlimited compute resources on demand)
  • Wide experience in genetic, evolutionary, neural and bio-inspired algorithms.
  • Gaussian processes, PCA/ICA.
  • Statistical testing and automated high-throughput data processing.

Practical skills

  • Psychophysics: have designed and run over 15 full studies using my own automated analysis framework. Expert knowledge of specialist hardware including CRS. Competent in adaptive sampling, interactive experiments, reaction time measurements and online experimentation.
  • Experience in confocal and electron microscopy, patch clamping.

Publications

  • Apthorp, D., Nagle, F., & Palmisano, S. (2014). Chaos in balance: Non-linear measures of postural control predict individual variations in visual illusions of motion. PloS one, 9(12), e113897.
  • Nagle, F and Hickinbotham, S, Embodied reaction logic in a simulated chemical computer, ECAL 2011.
  • Nagle, F et al: Techniques for Mimicry and Identity Blending Using Morph Space PCA, Lecture Notes in Computer Science, 2013

Invited talks

  • The College of Richard Collyer, Horsham, Sussex, 2012
  • Rank Prize Symposium on Perception of Faces, 2014

Conferences

  • NCH Mind and Brain Conference 2016: chair and programme committe member
  • UCL ID2 bioinformatics conference 2012: co-organiser
  • VSS 2014: Temporal visual search in faces and fire
  • ECVP 2013: Perception of the dynamic form of flames in hearth fire
  • ACCV 2012: Techniques for Mimicry and Identity Blending Using Morph Space PCA
  • ECVP 2012: Recognition from facial motion in dynamic average avatars

References

        Available on request