Doctoral student and amateur drummer, interested in why humans enjoy musical rhythms, and the dynamics behind synchronisation in social interactions.
PhD candidate at Queen Mary University of London (QMUL), working in the Music Cognition Lab and CRISP groups. Respectively, these are part of the Cognitive Science (CogSci) and Advanced Robotics (ARQ) Research Groups. Studentship supported by the Media and Arts Technology (MAT) Centre.
- Programmer at Hudson River Trading (2016-18), Fidelity International (2015-16)
- BEng Computing at Imperial College London (2012-15)
- What computers still can’t do, a blend of artificial intelligence, philosophy of mind, cognitive science etc.
- Computational models of (musical) rhythm perception and production (e.g. oscillators, Bayesian techniques).
Demonstrator in the School of Electronic Engineering & Computer Science (EECS), QMUL:
- 2019/20 - cognitive robotics.
- 2019/20 - workshops for final year UG project students.
- 2019/20 - algorithms and data structures.
See full list: here on Google Scholar.
- Thomas Kaplan, Elaine Chew (presenter): Detecting Low Frequency Oscillations in Cardiovascular Signals Using Gradient Frequency Neural Networks. CinC 2019. Read here: cinc.org/archives/2019/pdf/CinC2019-405.pdf
- 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)
- James Arram, Moritz Pflanzer, Thomas Kaplan, Wayne Luk: FPGA acceleration of reference-based compression for genomic data. FPT 2015: 9-16
- Thomas Kaplan, Optimising Bisulfite Sequencing Analysis, 2015. Read here: doc.ic.ac.uk/teaching/distinguished-projects/2015/t.kaplan.pdf
✉️ Email: thomas•m•kaplan✉gmail•com
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