Simon Mak

Conference Proceedings of the Aaai Conference on Artificial Intelligence · January 1, 2026 Hierarchical representations provide powerful and principled approaches for analyzing many musical genres. Such representations have been broadly studied in music theory, for instance via Schenkerian analysis (SchA). Hierarchical music analyses, however, a ... Full text Cite

Local-Transfer Gaussian Process (LTGP) Learning for Multi-fuel Capable Engines

Conference AIAA Science and Technology Forum and Exposition AIAA Scitech Forum 2025 · January 1, 2025 Data-driven engine surrogate models have been widely used to emulate in-cylinder trends of pressure and heat release rate for a wide variety of applications. For example, engines using multi-fuels, e.g., varying fuel cetane number (CN) or different sustain ... Full text Cite

SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization

Conference Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · August 24, 2024 Music composition and analysis is an inherently creative task, involving a combination of heart and mind. However, the vast majority of algorithmic music models completely ignore the "heart"component of music, resulting in output that often lacks the rich ... Full text Cite

Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression

Conference Proceedings of Machine Learning Research · January 1, 2024 Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression. In particular, Quadrature Fourier Features (QFF) derived from Gaussian quadrature rules have gained popularity in recent years du ... Cite

MaLT: Machine-Learning-Guided Test Case Design and Fault Localization of Complex Software Systems

Conference Proceedings 2024 22nd ACM IEEE International Symposium on Formal Methods and Models for System Design Memocode 2024 · January 1, 2024 Software testing is essential for the reliable and robust development of complex software systems. This is particularly critical for cyber-physical systems (CPS), which require rigorous testing prior to deployment. The complexity of these systems limits th ... Full text Cite

An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation

Conference Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · August 4, 2023 The fast-growing demand for algorithmic music generation is found throughout entertainment, art, education, etc. Unfortunately, most recent models are practically impossible to interpret or musically fine-tune, as they use deep neural networks with thousan ... Full text Cite

Physics-integrated Segmented Gaussian Process (SegGP) learning for cost-efficient training of diesel engine control system with low cetane numbers

Conference AIAA Scitech Forum and Exposition 2023 · January 1, 2023 Control model training is an essential step towards the development of an engine controls system. A robust controls strategy is required for engines to perform reliably and optimally under challenging conditions, such as using low cetane number fuels (vita ... Full text Cite

BayesFLo: Bayesian Fault Localization for Software Testing

Conference Proceedings 2023 IEEE 23rd International Conference on Software Quality Reliability and Security Companion Qrs C 2023 · January 1, 2023 Fault localization is a software testing activity that is critical when software failures occur. We propose a novel Bayesian fault localization method, yielding a principled and probabilistic ranking of suspicious input combinations for identifying the roo ... Full text Cite

Uncertainty quantification for inferring Hawkes networks

Conference Advances in Neural Information Processing Systems · January 1, 2020 Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications. However, it remains a challenge to extract reliable inference from complex datasets with uncertainty quantification. Aiming towards t ... Cite

Maximum Entropy Low-Rank Matrix Recovery

Conference IEEE International Symposium on Information Theory Proceedings · August 15, 2018 We propose a novel, information-theoretic mask construction method, called MaxEnt, for efficient data acquisition for low-rank matrix recovery. Fundamental to this design approach is the maximum entropy principle, which states that the measurement masks wh ... Full text Cite

Uncertainty quantification of flame transfer function under a bayesian framework

Conference AIAA Aerospace Sciences Meeting 2018 · January 1, 2018 Combustion instability identification techniques have received increasing attentions in the design and development of modern propulsion systems, and one of the most popular approaches is the flame transfer function (FTF). This work proposes a novel method ... Full text Cite

A two-stage transfer function identification methodology and its applications to bi-swirl injectors

Conference 53rd AIAA SAE ASEE Joint Propulsion Conference 2017 · January 1, 2017 Thermo-acoustic instability identification techniques have received increasing attentions in modern propulsion systems, and one of the most popular approaches is the flame transfer function. Despite the prominent role it plays in instability analysis, the ... Full text Cite