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publications
Plasma conditions at Europa’s orbit
Published in Icarus, 2015
With attention turned to Europa as a target for exploration, we focus on the space environment in which Europa is embedded. We review remote and in situ observations of plasma properties at Europa’s orbit, between Io’s dense, UV-emitting plasma torus and Jupiter’s dynamic plasma sheet. Where observations are limited (e.g. in plasma composition), we supplement our analysis with models of the neutral and plasma populations from Io to Europa. We evaluate variations and uncertainties in plasma properties with radial distance, latitude, longitude and time.
Recommended citation: Bagenal, F., Sidrow, E., Wilson, R., Cassidy, T., Dols, V., Crary, F., Steffl, A., Delamere, P., Kurth, W., and Paterson, W. (2015). Plasma conditions at Europa's orbit. Icarus 261, 1-13.
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Modelling multi-scale, state-switching functional data with hidden Markov models
Published in The Canadian Journal of Statistics (Winner of CJS Best Paper Award for 2022), 2021
Data sets composed of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods in functional data analysis. We detail a hierarchical approach that treats the curves as observations from a hidden Markov model. The distribution of each curve is then defined by another fine-scale model that may involve autoregression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movements of a northern resident killer whale (Orcinus orca) off the western coast of Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods.
Recommended citation: Sidrow, E., Heckman, N., Fortune, S.M.E., Trites, A.W., Murphy, I., and Auger-Méthé, M. (2022). Modelling multi-scale, state-switching functional data with hidden Markov models. Canadian Journal of Statistics 50(1), 327-356.
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Characterization of respirable dust generated from full scale cutting tests in limestone with conical picks at three stages of wear
Published in Minerals, 2022
Respirable rock dust poses serious long-term health complications to workers in environments where mechanical rock excavation is utilized. The purpose of this study is to characterize respirable dust generated by cutting limestone with new, partially worn, and fully worn conical pick wears. Characterizing limestone respirable dust can aid in decision making for respirable dust suppression levels and exposures throughout the lifetime of a pick in underground mining and engineering activities. The methods include full scale cutting of a limestone sample in the laboratory with three conical picks at different stages of wear. Dust samples were collected during cutting with various instruments connected to pumps and subsequently analyzed to determine the concentrations, mineralogy, particle shapes, and particle size distributions. The results show that the worn pick generated the highest concentration of dust, all picks generated dust containing quartz, all three picks generated dust particles of similar shapes, and all three picks generated various particle size distributions. In conclusion, a preliminary suite of respirable dust characteristics is available and with further future additional studies, results could be used for the evaluation of possible strategies and methods of dust suppression and exposures during mining, tunneling, or drilling activities.
Recommended citation: Slouka, S., Brune, J., Rostami, J., Tsai, C., and Sidrow, E. (2022). Characterization of respirable dust generated from full scale cutting tests in limestone with conical picks at three stages of wear. Minerals 12(8), 930.
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Killer whale respiration rates
Published in PLOS One, 2024
Measuring breathing rates is a means by which oxygen intake and metabolic rates can be estimated to determine food requirements and energy expenditure of killer whales (Orcinus orca) and other cetaceans. This simple measure allows the energetic consequences of environmental stressors to cetaceans to be understood but requires knowing respiration rates while they are engaged in different behaviours such as resting, travelling and foraging. We calculated respiration rates for different behavioural states of southern and northern resident killer whales using video from UAV drones and concurrent biologging data from animal-borne tags. Behavioural states of dive tracks were predicted using hierarchical hidden Markov models (HHMMs) parameterized with time-depth data and with labeled tracks of drone-identified behavioural states. Dive tracks were sequences of dives and surface intervals lasting ≥ 10 minutes cumulative duration. We calculated respiration rates and estimated oxygen consumption rates for the predicted behavioural states of the tracks. We found that juvenile killer whales breathed at a higher rate when travelling (1.6 breaths min-1) compared to resting (1.2) and foraging (1.5)—and that adult males breathed at a higher rate when travelling (1.8) compared to both foraging (1.7) and resting (1.3). The juveniles in our study were estimated to consume 2.5–18.3 L O2 min-1 compared with 14.3–59.8 L O2 min-1 for adult males across all behaviours based on estimates of mass-specific tidal volume and oxygen extraction. Our findings confirm that killer whales take single breaths between dives and indicate that energy expenditure derived from respirations requires using sex, age, and behavioural-specific respiration rates. These findings can be applied to bioenergetics models on a behavioural-specific basis, and contribute towards obtaining better predictions of dive behaviours, energy expenditure and the food requirements of apex predators.
Recommended citation: McRae, T.M., Volpov, B.L., Sidrow, E., Fortune, S.M.E., Auger-Méthé, M., Heckman, N., and Trites, A.W. (2024). Killer whale respiration rates. PLOS One 19(5), 1-26.
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Variance-reduced stochastic optimization for efficient inference of hidden Markov models
Published in The Journal of Computational and Graphical Statistics, 2024
Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large datasets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying dataset for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire dataset. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs, with less computation time, and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series datasets more efficiently than existing baselines. Supplemental materials are available online.
Recommended citation: Sidrow, E., Heckman, N., Bouchard-Côté, A., Fortune, S.M.E., Trites, A.W., and Auger-Méthé, M. (2024). Variance-reduced stochastic optimization for efficient inference of hidden Markov models. Journal of Computational and Graphical Statistics, 1-39
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talks
Modelling functional data with hierarchical hidden Markov models: Applications to animal movement
Published:
Modern biologging sensors can record sequences of curves at very high frequencies, allowing researchers to observe biological processes such as animal movement at extremely fine scales. High-frequency data sets can exhibit state-switching, multi-scale dependence structures that are difficult to model with standard methods in functional data analysis. Inspired by data collected from a northern resident killer whale (Orcinus orca), we describe a hierarchical framework that treats curves as observations from a hidden Markov model. Each curve’s distribution is defined by a fine-scale model whose parameters depend upon a coarse-scale latent process. Through simulations, we show that our model produces more interpretable state estimates and more accurate parameter estimates compared to existing methods. We also consider several computational challenges when modelling state-switching functional data with hidden Markov models.
Modelling multi-scale, state-switching functional data with hidden Markov models
Published:
Data sets composed of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods in functional data analysis. We detail a hierarchical approach that treats the curves as observations from a hidden Markov model. The distribution of each curve is then defined by another fine-scale model that may involve autoregression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movements of a northern resident killer whale (Orcinus orca) off the western coast of Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods.
Variance-reduced stochastic optimization for efficient inference of hidden Markov models
Published:
Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying data set for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire data set. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series data sets more efficiently than existing baselines.
Stochastic optimization for efficient inference in ecological hidden Markov models
Published:
Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying data set for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire data set. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series data sets more efficiently than existing baselines.
teaching
Learning Assistant, CU Boulder
Learning Assistant, University of Colorado, Applied Mathematics, 2015
- APPM 3170: Discrete Mathematics, Fall 2015
- APPM 3310: Matrix Methods and Applications, Spring 2015
- APPM 3310: Matrix Methods and Applications, Fall 2014
- APPM 2350: Calculus 3 for Engineers, Spring 2014
Teaching Assistant, CU Boulder
Teaching Assistant, University of Colorado, Applied Mathematics, 2018
- APPM 1235: Pre-Calculus for Engineers, Spring 2018
- APPM 1350: Calculus 1 for Engineers, Fall 2017
Teaching Assistant, UBC Vancouver
Teaching Assistant, University of British Columbia, Statistics, 2024
- STAT 443: Time Series and Forecasting, Spring 2024
- STAT 300: Intermediate Statistics, Fall 2021
- STAT 300: Intermediate Statistics, Fall 2019