A5079056190- Time Series Analysis and Forecasting
- Bayesian Methods and Mixture Models
- Neural dynamics and brain function
- Diffusion and Search Dynamics
- Advanced Text Analysis Techniques
- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Neural and Behavioral Psychology Studies
- Forecasting Techniques and Applications
- Gaussian Processes and Bayesian Inference
- Music and Audio Processing
- Anomaly Detection Techniques and Applications
- T-cell and B-cell Immunology
- Biochemical Analysis and Sensing Techniques
- Neural Networks and Applications
- Gait Recognition and Analysis
- Immune Cell Function and Interaction
- Regulation of Appetite and Obesity
- Memory and Neural Mechanisms
- Data Management and Algorithms
- Circadian rhythm and melatonin
- Data Stream Mining Techniques
- Structural Health Monitoring Techniques
- Face and Expression Recognition
- Blind Source Separation Techniques
Rice University
2020-2025
University of Warwick
2019-2021
Words represent a uniquely human information channel-humans use words to express thoughts and feelings assign emotional valence experience. Work from model organisms suggests that assignments are carried out in part by the neuromodulators dopamine, serotonin, norepinephrine. Here, we ask whether signaling these extends word semantics humans measuring sub-second neuromodulator dynamics thalamus (N = 13) anterior cingulate cortex 6) of individuals evaluating positive, negative, neutrally...
We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected via wrist-worn wearable devices. The proposed approach integrates covariates into the modeling of amplitude and phase parameters, facilitating cohort-level analysis with enhanced flexibility interpretability. To promote sparsity, we employ an l_1-ball projection prior, enabling precise control over complexity while identifying significant predictors. assess performances on simulated...
The noradrenaline (NA) system is one of the brain's major neuromodulatory systems; it originates in a small midbrain nucleus, locus coeruleus (LC), and projects widely throughout brain.
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Abstract Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across trials, but has been necessary due to the difficulties posed by inference trial-level ERPs. We introduce Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for amplitude, latency, ERP waveforms. apply RPAGP data from a...
We propose a Bayesian hidden Markov model for analyzing time series and sequential data where special structure of the transition probability matrix is embedded to explicit-duration semi-Markovian dynamics. Our formulation allows development highly flexible interpretable models that can integrate available prior information on state durations while keeping moderate computational cost perform efficient posterior inference. show benefits choosing approach HSMM estimation over its frequentist...
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The prefrontal cortex (PFC) is a region of the brain that in humans involved production higher-order functions such as cognition, emotion, perception, and behavior. Neurotransmission PFC produces by integrating information from other areas brain. At foundation neurotransmission, extension at functions, are an untold number coordinated molecular processes involving DNA sequence variants genome, RNA transcripts transcriptome, proteins proteome. These "multiomic" foundations poorly understood...
In Italy, an HLA-matched unrelated donor is currently the primary when a HLA matched sibling not found for allogeneic haematopoietic stem cell transplantation (HSCT). Better outcomes require optimal matching between and recipient at least HLA-A, -B, -C, -DRB1 loci; therefore, availability of donors important. The enormous polymorphism has always necessitated registries with large number individuals in order to be able provide well-matched substantial percentage patients. increase efficiency...
We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov where the states are defined through spectral properties regime. The number is unknown along with relevant periodicities, role which may vary across states. address this inference problem Bayesian nonparametric assuming sticky hierarchical Dirichlet process for switching dynamics between different while periodicities characterizing each state explored trans-dimensional chain Monte Carlo sampling...
We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. account temporal correlation in the data by assuming that observations are characterized an underlying and unobserved hidden discrete autoregressive process. assume emission distributions capture spatial dependencies state-specific precision matrices via horseshoe priors. characterize mixing probabilities process cumulative shrinkage prior accommodates zero-inflated parameters...
We propose a sparse vector autoregressive (VAR) hidden semi-Markov model (HSMM) for modeling temporal and contemporaneous (e.g. spatial) dependencies in multivariate nonstationary time series. The HSMM's generic state distribution is embedded special transition matrix structure, facilitating efficient likelihood evaluations arbitrary approximation accuracy. To promote sparsity of the VAR coefficients, we deploy an $l_1$-ball projection prior, which combines differentiability with positive...
We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov where the states are defined through spectral properties regime. The number is unknown along with relevant periodicities, role which may vary across states. address this inference problem Bayesian nonparametric assuming sticky hierarchical Dirichlet process for switching dynamics between different while periodicities characterizing each state explored trans-dimensional chain Monte Carlo sampling...
We propose a Bayesian hidden Markov model for analyzing time series and sequential data where special structure of the transition probability matrix is embedded to explicit-duration semi-Markovian dynamics. Our formulation allows development highly flexible interpretable models that can integrate available prior information on state durations while keeping moderate computational cost perform efficient posterior inference. show benefits choosing approach HSMM estimation over its frequentist...