The Convergence Lab is a group of researchers committed to applying artificial intelligence, machine learning, and other forms of unconventional computation to a variety of disciplines.
The team is always interested in learning new domains and is always open to collaborating opportunities.
Computer Science + Neuroscience
The Human Brain is a Nonlinear Computational System
Current Projects Include:
- Using Symbolic Regression to find Nonlinear Models of Functional and Effective Connectivity within fMRI data
- Discovering Generative Functions Describing Anatomical Connectivity
- Generating synthetic fMRI data
- Aligning fMRI data based on functional space
Computer Science + Epidemiology & Logistics
Vaccinating a Population is a Programming Problem
Current Projects Include:
- Using AI to find effective vaccination strategies based on a community’s social contact network (graph topology)
- Evolving social contact networks to match the records of COVID-19 spread
- http://epidem.ai/
Computer Science + Art
Can AI Be Truly Creative?
Current Projects Include:
- Wandering Artist
- SmartDrone
- Generative Music Based On Real Time Data
- Audio Source Separation
Computer Science + Earth Science
Automating Important Tasks with AI
Current Projects Include:
- Improving the Automation of Fossil Zone Alignments to Build a Global Record of Species Diversity
- Increasing the resolution of remote sensing data
Computer Science + Human Kinetics
How Can AI Monitor Gait to Prevent Falls? Identify Parkinson’s Disease?
Current Projects Include:
- Devices detect users based on gait for security purposes
- Task identification using real time data and metastable states
- Investigating human gait from a network perspective
- Identify Parkinson’s disease severity based on gait
Computer Science + Ethics
The Buck Stops Here
Current Projects Include:
- How can AI practitioners be ethical when they have no control over their data, or when there is no control over how the new technology is used?
Computer Science + Finance
Fresh Take On An Old System
Current Projects Include:
- Model arbitrage: Improved regression analysis for more options pricing
Computer Science + Biology
Harnessing AI and High Performance Computing
Current Projects Include:
- DNA Fragment Assembly
- Error correction in DNA
Computer Science + Healthcare
Improve Healthcare Outcomes for All
Current Projects Include:
- Noninvasive strategy for monitoring intracranial pressure
- Updated Resting Energy Expenditure models to more accurately inform clinical decision making
List of Publications
Peer Reviewed Conference Papers
Rodrigo Vega Jimenez, Michael Dube, Sheridan Houghten, and James Alexander Hughes. “Weighting on the World to Change… an Epidemic.” 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021 (Accepted).
James Alexander Hughes, William Hannah, Peter Kikkert, Barry MacKenzie, Wendy Ashlock, Sheridan Houghten, Daniel Ashlock, Matthew Stoodley, Michael Dube, Rachel Brown, and Amanda Saunders. “We Are Not Pontius Pilate: Acknowledging Ethics and Policy.” 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI): IEEE Symposium on the Ethical, Social and Legal Implications of Artificial Intelligence (IEEE ETHAI). IEEE, 2020. (Nominated for Best Paper Award)
James Alexander Hughes, Michael Dube, Sheridan Houghten, and Daniel Ashlock. “Vaccinating a Population is a Programming Problem.” 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2020.
Michael Dube, Sheridan Houghten, Daniel Ashlock, and James Hughes. “Evolving the Curve.” 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2020.
James Alexander Hughes, Ryan E. R. Reid, Sheridan Houghten, and Ross E. Andersen. “Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patents.” 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2020.
Travis MacDonald, Barry MacKenzie, and James A. Hughes. “SmartDrone: An Aurally Interactive Harmonic Drone.” New Interfaces in Musical Expression 2020 (NIME), 2020.
James Alexander Hughes, Sheridan Houghten, and Joseph Alexander Brown. “Gait Model Analysis of Parkinson’s Disease Patients under Cognitive Load.” 2020 IEEE World Congress on Computational Intelligence (WCCI). IEEE, 2020.
James Alexander Hughes, Sheridan Houghten, and Joseph Alexander Brown. “Descriptive Symbolic Models of Parkinson’s Disease Patient Gait.” 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2019. (Nominated for Best Paper Award)
James Alexander Hughes, and Mark Daley. “Generating Nonlinear Models of Functional Connectivity from Functional Magnetic Resonance Imaging Data with Genetic Programming.” 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019.
James Alexander Hughes, Joseph Alexander Brown, Adil Mehmood Khan, Asad Masood Khattak, and Mark Daley. “User and Task Identification of Smartwatch Data with an Ensemble of Nonlinear Symbolic Models.” 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019.
Ethan Jackson, James Hughes and Mark Daley. “On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data.” 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2018
Sheridan Houghten, Tyler K. Collins, James Alexander Hughes and Joseph Alexander Brown. “Edit Metric Decoding: Return of the Side Effect Machines.” 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2018 (Best Paper Award)
James Alexander Hughes, Joseph Alexander Brown, Adil Mehmood Khan, Asad Masood Khattak, and Mark Daley. “Analysis of Symbolic Models of Biometric Data and their use for User and Task Identification.” 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2018 (Nominated for Best Paper Award)
James Alexander Hughes, Ethan C. Jackson, Mark Daley. “Modelling Intracranial Pressure with Noninvasive Physiological Measures.” Proceedings of the 2017 Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2017. (Nominated for Best Paper Award)
Ethan C. Jackson, James Alexander Hughes, Mark Daley. “An Algebraic Generalization for Graph and Tensor-Based Neural Networks.” Proceedings of the 2017 Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2017.
James Alexander Hughes, and Mark Daley. “Searching for Nonlinear Relationships in fMRI Data with Symbolic Regression.” Proceedings of the 2017 on Genetic and Evolutionary Computation Conference. ACM, 2017. (Nominated for Best Paper Award)
James Alexander Hughes, and Mark Daley. “Finding Nonlinear Relationships in fMRI Time Series with Symbolic Regression.” Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, 2016.
James Alexander Hughes, Joseph Alexander Brown, and Adil Mehmood Khan. “Smartphone gait fingerprinting models via genetic programming.” Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, 2016.
James Alexander Hughes, Sheridan Houghten, Guillermo M. Mallén-Fullerton, and Daniel Ashlock. “Recentering and Restarting Genetic Algorithm Variations for DNA Fragment Assembly.” 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2014. (Best Student Paper Award)
James Alexander Hughes, Sheridan Houghten, and Daniel Ashlock. “Recentering, Reanchoring & Restarting an Evolutionary Algorithm.” 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2013.
James Alexander Hughes, Joseph Alexander Brown, Sheridan Houghten, and Daniel Ashlock. “Edit metric decoding: Representation strikes back.” 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013.
Journal Papers
Pritika Dasgupta, James Alexander Hughes, Ervin Sejdic, Mark Daley. “Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming.” Computer Methods and Programs in Biomedicine (2021): 106104.
Hughes, James, Sheridan Houghten, and Joseph Alexander Brown. “Models of Parkinson’s Disease Patient Gait.” IEEE Journal of Biomedical and Health Informatics (2019).
James Alexander Hughes, Sheridan Houghten, and Daniel Ashlock. “Restarting and Recentering Genetic Algorithm Variations for DNA Fragment Assembly: The Necessity of a Multi-Strategy Approach.” Biosystems 150 (2016): 35-45.
James Alexander Hughes, Sheridan Houghten, and Daniel Ashlock. “Recentering and Restarting a Genetic Algorithm Using a Generative Representation for an Ordered Gene Problem.” International Journal of Hybrid Intelligent Systems 11.4 (2014): 257-271.
Guillermo M. Mallén-Fullerton, James Alexander Hughes, Sheridan Houghten, and Guillermo Fernández-Anaya. “Benchmark Datasets for the DNA Fragment Assembly Problem.” International Journal of Bio-Inspired Computation 5.6 (2013): 384-394.
Book Chapters
James Alexander Hughes, Sheridan Houghten, and Daniel Ashlock. “Permutation Problems, Genetic Algorithms, and Dynamic Representations.” Nature-Inspired Computing and Optimization. Springer International Publishing, 2017. 123-149.
Posters
Travis MacDonald, Barry MacKenzie, and James A. Hughes. “SmartDrone: An Aurally Interactive Harmonic Drone.” At: Manchester UK, Conference: New Interfaces in Musical Expression 2020 (NIME), 2020.
James Alexander Hughes, Ethan C. Jackson, Mark Daley. “Modelling Intracranial Pressure with Noninvasive Measures and Genetic Programming.” At: London ON, Conference: London Health Research Day (2018): LHRD 2018, DOI: 10.13140/RG.2.2.20645.81127
James Alexander Hughes and Mark Daley. “On the Generalizability of Nonlinear Models of fMRI Data and the True Model Selection Problem.” At: Vancouver, Conference: 12th Annual Canadian Neuroscience Meeting (2018): CAN 2018, DOI: 10.13140/RG.2.2.36020.14721
James Alexander Hughes and Mark Daley. “Nonlinear Model of a Nonlinear System: An Alternative view of fMRI Modelling.” At: Montreal, Conference: 11th Annual Canadian Neuroscience Meeting (2017): CAN 2017, DOI: 10.13140/RG.2.2.17508.58246
James Alexander Hughes and Mark Daley. “Finding Nonlinear Relationships in fMRI Time Series.” At: Denver Colorado, Conference: GECCO 2016, DOI: 10.13140/RG.2.1.4489.1128
Software
James Alexander Hughes, Michael Dube, Sheridan Houghten, and Daniel Ashlock. “COVID-19 Modelling and Vaccination Strategies”, Software for modelling/simulate the spread of a disease such as COVID-19 over a given graph/network representing a specific social contact network. Source Code Available on GitHub. 2020. https://github.com/convergencelab/eCov-GP
Travis MacDonald and James Alexander Hughes. “PianoView”, Smartphone Application Demo and Source Code for a piano view for Android applications with customizable look and feel, Google Play Store, 2020. https://play.google.com/store/apps/details?id=com.convergencelabstfx.pianoviewexample&hl=en_CA. Source Code Available on GitHub. 2020 https://github.com/convergencelab/PianoView
Travis MacDonald and James Alexander Hughes. “SmartDrone”, Smartphone Application, Google Play Store, 2019. https://play.google.com/store/apps/details?id=com.convergencelabstfx.smartdrone
Travis MacDonald and James Alexander Hughes. “Key Companion”, Smartphone Application, Google Play Store, 2019.
https://play.google.com/store/apps/details?id=com.convergencelabstfx.keycompanion
James Alexander Hughes and Mark Daley. “jGPv9”, Genetic Programming system specialized for Symbolic Regression. Source Code Available on GitHub. 2017. https://github.com/convergencelab/jGP
CONVERGENCE LAB 