About Me

AI Researcher, Centre for Clinical Pharmacology and Precision Medicine at the William Harvery Research Institute (WHRI), Queen Mary University of London (QMUL). PhD in probabilistic machine learning, applied to how the brain processes musical rhythm, using computational cognitive models and probabilistic machine learning; studied at QMUL, supervised by Marcus Pearce and Lorenzo Jamone, in the Music Cognition Lab and CRISP groups (CogSci and ARQ groups); studentship supported by MAT.

Experience

• AI Researcher - William Harvey Research Institute (2024-)
• Software Developer - Mytos (2023)
• Research Placement - Bela (2019)
• Software Developer - Hudson River Trading (2016-18)

Previously: Fidelity Intl. (2015), BAML (2014) & Splunk (2013)

Education

• PhD in Computer Science, Queen Mary University of London (2018-23)
• BEng in Computing, Imperial College London (2012-15)

Publications

Google Scholar.

1. Thomas Kaplan. Probabilistic Models of Rhythmic Expectation & Synchronisation. PhD Thesis, Queen Mary University of London, School of Electronic Engineering and Computer Science. (2024) {url}.
2. Ana Clemente, Thomas Kaplan, Marcus Pearce. Perceptual representations mediate effects of stimulus properties on liking for music. Annals of the New York Academy of Sciences. (2024) {doi,url}.
3. Jonathan Cannon, Thomas Kaplan. Oscillators can perform inference in Bayesian models of rhythm perception. Preprint. (2024) {doi}.
4. Thomas Kaplan, Lorenzo Jamone, Marcus Pearce. Probabilistic modelling of microtiming perception. Cognition. 239, e105532. (2023) {doi,url}.
5. Thomas Kaplan, Jonathan Cannon, Lorenzo Jamone, Marcus Pearce. Modelling enculturated bias in entrainment to rhythmic patterns. PLoS Comput. Biol. 18(9): e1010579.(2022) {url, doi, pdf}.
6. Thomas Kaplan, Elaine Chew. Detecting low frequency oscillations in cardiovascular signals using Gradient Frequency Neural Networks. IEEE Computing in Cardiology (CinC) (2019) {doi, url}.
7. James Arram, Thomas Kaplan, Wayne Luk, Peiyong Jiang: Leveraging FPGAs for Accelerating Short Read Alignment. IEEE/ACM Trans. Comput. Biology Bioinform. 14(3): 668-677 (2017) {url, doi}
8. James Arram, Moritz Pflanzer, Thomas Kaplan, Wayne Luk: FPGA acceleration of reference-based compression for genomic data. {url, doi}
9. Thomas Kaplan, Optimising Bisulfite Sequencing Analysis, 2015. {pdf}

Conferences & Seminars

• (Upcoming) Ana Clemente (presenter), Thomas Kaplan, Marcus Pearce: Perceptual representations mediate effects of stimulus properties on liking for music, ESCOM12 Conference, 07/2024.
• Fleur Bouwer (presenter), Thomas Kaplan, Atser Damsma, Edward Large, Marcus Pearce: Modelling temporal expectations: A probabilistic model and an entrainment model explain distinct aspects of rhythmic behavior, 19th NVP Dutch Society for Brain and Cognition Winter Conference, 12/2023.
• Jonathan Cannon (presenter), Thomas Kaplan: Beat perception as dynamic inference. “Modelling rhythm perception beyond the beat” symposium, ICMPC17-ASCOM7 Conference, 08/2023.
• Atser Damsma (presenter), Pierre-Louis Bazin, Thomas Kaplan, Henkjan Honing, Fleur Bouwer: Modelling flexible rhythm prediction in continuous time. “Modelling rhythm perception beyond the beat” symposium, ICMPC17-ASCOM7 Conference, 08/2023.
• Tomas Matthews (Presenter), Jonathan Cannon, Victor Pando-Naude, Jan Stupacher, Isaac Romkey, Thomas Kaplan, Gunvor Bertelsen, Alexandre Celma Miralles, Virginia Penhune and Peter Vuust: Predictive processes shape the relation between groove and syncopation. ICMPC17-ASCOM7 Conference, 08/2023.
• Fleur Bouwer (presenter), Thomas Kaplan, Atser Damsma, Ed Large, Marcus Pearce: Modelling prediction of non- isochronous rhythmic patterns with probabilistic and entrainment models. Rhythm Perception and Production Workshop (RPPW) 2023, 21/06/2023.
• Tomas Matthews, Thomas Kaplan, Yassaman Ommi, Jonathan Cannon (presenter): A minimal Bayesian model of groovy prediction error. Groove Workshop (2023). 27/01/2023.
• Thomas Kaplan, Lorenzo Jamone, Marcus Pearce: Modelling individual differences in perception of microtiming patterns for an archetypal drum beat. Groove Workshop (2023). 27/01/2023.
• Thomas Kaplan: Modelling microtiming perception for a popular drum rhythm. Perception, Cognition, Aesthetics (PCA) Theme Meeting, Centre for Digital Music (C4DM), Queen Mary University of London. 06/12/2022.
• Thomas Kaplan: Probabilistic modelling of auditory rhythm processing in music (and towards speech). Cognitive Science (CogSci) Seminar, Queen Mary University of London. 26/10/2022.
• Ana Clemente (presenter), Thomas Kaplan, Marcus Pearce: Individual perceptual mediation of the influence of stimulus properties on liking for music. 2022 Biennial Congress of the International Association of Empirical Aesthetics, 31/08/2022.
• Thomas Kaplan: A multi-scale model of rhythmic expectation and synchronisation. Music Cognition Lab 10th Anniversary Workshop, QMUL, 01/07/2022.
• Fleur Bouwer (presenter), Thomas Kaplan, Atser Damsma, Olivier Bais, Kai Schüren, Marcus Pearce: Modelling prediction of non-isochronous rhythmic patterns as a probabilistic process. 18th NVP Dutch Society for Brain and Cognition Winter Conference, 28/04/2022.
• Thomas Kaplan, Lorenzo Jamone, Marcus Pearce: Modelling the Influence of Rhythm Production on Meter Perception. SysMus21 Conference, 04/11/2021.
• Thomas Kaplan: Modelling enculturated bias in entrainment to rhythmic patterns. Music Cognition Lab (MCL) Meeting, Queen Mary University of London. 15/10/2021.
• Thomas Kaplan: Multi-scale model of rhythmic expectation and synchronisation. COGNITIVESCIENCEFEST #1, QMUL Cognitive Science department, 06/10/2021.
• Thomas Kaplan, Jonathan Cannon, Lorenzo Jamone, Marcus Pearce: Simulating Iterated Reproduction of Musical Rhythms using a Hybrid Probabilistic Model (Poster). ICMPC16-ESCOM11 Conference, 30/07/2021. {jpg, video}
• Thomas Kaplan, Lorenzo Jamone, Marcus Pearce: Modelling the Influence of Rhythm Production on Meter Perception. (Best paper award candidate) ICMPC16-ESCOM11 Conference, 29/07/2021. {video}
• Thomas Kaplan, Jonathan Cannon, Lorenzo Jamone, Marcus Pearce: Modelling Entrainment as Continuous Bayesian Inference using Learned Rhythmic Prototypes. Rhythm Perception and Production Workshop (RPPW) 2021, 24/06/2021. {video}
• Thomas Kaplan: Modelling Entrainment as Continuous Bayesian Inference using Learned Rhythmic Prototypes. Music Cognition Lab (MCL) Meeting, Queen Mary University of London. 19/02/2021.
• Thomas Kaplan: Is microtiming an essential aspect of groove? Perception, Cognition, Aesthetics (PCA) Theme Meeting, Centre for Digital Music (C4DM), Queen Mary University of London. 02/02/2021.
• Thomas Kaplan: Predictive Processing Models of Rhythm Perception and Production. Online Event, Max Planck Institute of Empirical Aesthetics. 16/09/2020.
• Thomas Kaplan: Predictive Processing Models of Rhythm Perception and Production. Music Cognition Lab (MCL) Meeting, Queen Mary University of London. 04/09/2020.
• Thomas Kaplan, Elaine Chew (presenter): Detecting Low Frequency Oscillations in Cardiovascular Signals Using Gradient Frequency Neural Networks. Computing in Cardiology 2019. {url}

Teaching

Demonstrator in the School of Electronic Engineering & Computer Science (EECS), QMUL:

• 2019-21 - cognitive robotics.
• 2019-21 - workshops for final year UG project students.
• 2019-20 - algorithms and data structures.

Contact

✉️ Email: thomas•m•kaplan✉gmail•com

Social:

• Twitter: twitter.com/TomKappers
• LinkedIn: linkedin.com/in/thomasmkaplan/
• Github: github.com/Kappers

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