Dr. Beniamino Hadj-Amar
Dr. Beniamino Hadj-Amar
Embedded Files
I am a tenure-track Assistant Professor in the Department of Biostatistics at the University of South Carolina (Columbia, SC).
My research lies at the interface of statistics and science, with a particular focus on the development of Bayesian methodologies for the automated analysis of complex dynamical time series.
Prior to joining USC, I was a Postdoctoral Fellow in the Department of Statistics at Rice University, where I worked with Marina Vannucci in collaboration with Read Montague's lab at Virginia Tech. I received my Ph.D. through the Oxford-Warwick Statistics Programme (OxWaSP), under the supervision of Bärbel Finkenstädt at the University of Warwick (UK).
e-mail: hadjamar@mailbox.sc.edu
Research Interests:
Research Interests:
Time Series
Hidden Markov Models
Bayesian Inference
Statistical Spectral Analysis
Bayesian Nonparametrics
Variable Selection
Graphical Models
Gaussian Processes
News
I am honored and delighted to receive the 2025 Blackwell–Rosenbluth Award, recognizing junior researchers for their international contributions to Bayesian statistics and to the community.
Upcoming Talks and Conferences:
Research:
To identify latent structures, deal with complex streams of temporal data, as well as properly account for model uncertainty, I often need to design highly flexible and interpretable models characterized by non-stationary behavior, non-linear features, and sparse structures. From a methodological perspective, I have experience working on switching models (e.g.hidden Markov and semi-Markov models), change-point models, Gaussian processes, Bayesian nonparametrics, graphical models, Bayesian variable selection, factor models, mixture models, statistical spectral analysis, and Markov chain Monte Carlo.
With my collaborators, I have been working on addressing scientific problems across several domains, with a particular focus on data from wearable devices—such as actigraphy and circadian monitoring in patients with epilepsy. I also work extensively in neuroscience, contributing to studies on neuromodulator dynamics using electrophysiological, electrochemical, and fMRI data from the conscious human brain. In addition, I have collaborated on projects in respiratory research, including the modeling of airflow trace data to identify patterns associated with sleep apnea.
Publications:
Publications:
Batten, S.*, Hadj-Amar, B.*, Ahrens, M.L., Oster, M.E., Hartle, A., Barbosa, L.S., Lohrenz, T., White, J.P., Witcher, M.R., Vannucci, M., Montague, R., Howe, M. & Difeliceantonio, A. Task evoked monoamine release in the human amygdala encodes liking (2025+, in submission).
Ouyang, J., Hadj-Amar, B., & Cao, X., . Scalable Network-Driven Variable Selection in Bayesian Linear Regression (2025+, submitted).
Charney, A.W., Liharska, L.E., Vornholt, E., Valentine, A., Lund, A., Hashemi, A., Thompson, R.C., Lohrenz , T., Johnson, J.S., Bussola, N., Cheng, E., Park, Y.J., Qasim, S., Aristel, A., Wilkins, L., Ziafat, K., Silk, H., Linares, L.M., Sullivan, B., Feng, C., Batten, S.R., Bang, D., Barbosa, L.S., Twomey, T., White, J.P., Vannucci, M., Hadj-Amar, B., Moya, E., Figee, M., Nadkarni, G.N., Breen, M.S., Kishida, K., Scarpa, J., Schadt, E.E., Saez, I., Montague, P.R., Beckmann, N.D. and Kopell, B.H. A transcriptional program associated with neurotransmission in the living human brain. 2025, Molecular Psychiatry (accepted).
Hadj-Amar, B., Bornstein, A., Guindani, M., & Vannucci, M., Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Multivariate Time Series Data, Journal of Computational and Graphical Statistics, 2025, (in press).
Hadj-Amar, B., Krishnan, V., & Vannucci, M., Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms in Patients with Epilepsy 2025, Data Science in Science, 4 (1).
Batten, S.*, Hartle, A.*, Barbosa. L*, Hadj-Amar, B.*, Bang. D.*, ...,Vannucci, M., & Montague, R., Emotional Words Evoke Region and Valence-Specific Patterns of Concurrent Neuromodulator Release in Human Thalamus and Cortex, 2025, Cell Reports 44(1), 115162.
Hadj-Amar, B., Jewson, J. & Vannucci, M., Bayesian Sparse Vector Autoregressive Switching Models with Application to Human Gesture Phase Segmentation, Annals of Applied Statistics, 2024, 18(3), 2511-2531
Pluta, D*., Hadj-Amar, B.*, Li, M., Zhao, Y., Versace, F. & Vannucci, M., Improved Data Quality and Statistical Power of Trial-Level Event-Related Potentials with Bayesian Random-Shift Gaussian Processes, Nature Portoflio, Scientific Reports, 2024, 14, 8856.
Bang, D.*, Luo, Y.*, Barbosa, L*., Batten, S*., Hadj-Amar, B.*, Twomey, T., Melville, N., White, J., Torres, A., Celaya, X., Ramaiah, P., McClure, S.M., Brewer, G.A., Bina, R.W., Lohrenz, T., King-Casas, B., Chiu, P., Vannucci, M., Kishida, K., Witcher, M. and Montague, P.R, Noradrenaline tracks emotional modulation of attention in human amygdala. Current Biology, 2023, 33 (22) : 5003-5010.
Hadj-Amar, B., Jewson, J. & Fiecas, M., Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity, Bayesian Analysis, 2023, 18 (2) : 547-577.
Sacchi, N., Ciceri, F., Bonifazi, F., Algeri, M., Gallina, A., Pollichieni, S., Raggio, E., Hadj-Amar, B., Lombardini, L., Pupella, S., Liumbruno, G., and Cardillo, M. Availability of HLA-allele-matched unrelated donors and registry size: estimation from haplotype frequency in the Italian population, Journal of Human Immunology, 2021, 82 (10) : 758-766.
Hadj-Amar, B., Finkenstädt, B., Fiecas, M. & Huckstepp, R., Identifying the Recurrence of Sleep Apnea Using a Harmonic Hidden Markov Model, Annals of Applied Statistics, 2021, 15 (3) : 1171-1993.
Hadj-Amar, B., Bayesian Analysis of Nonstationary Periodic Time Series, Ph.D. Thesis, 2020.
Hadj-Amar, B., Finkenstädt, B., Fiecas, M., Levi, F. & Huckstepp, R., Bayesian Model Search for Nonstationary Periodic Time Series, Journal of the American Statistical Association, T&M, 2020, 115 (531) : 1320–1335.
* : joint first author
Software:
Software:
AutoNOM: Automatic Nonstationary Oscillatory Modelling, a Julia software to model nonstationary periodic time series.
HHMM: Harmonic Hidden Markov Model, a Julia software to model time-varying periodic and oscillatory processes by means of a Bayesian non-parametric HHMM.
BayesianApproxHSMM: a stan software (and R utilities) to model time series and sequential data using a Bayesian approximation to Hidden semi-Markov models.
mvHMM: contains a suite of software that utilizes multivariate Bayesian hidden Markov models (HMM) for the analysis of signals from monoamine neurotransmitters.
sparseVARHSMM a stan software (and R utilities) to model temporal and contemporaneous (e.g. spatial) dependencies in multivariate time series data using a flexible VAR HSMM.
BayesRPAGP: Bayesian Random Phase-Amplitude Gaussian Process. An R software for Bayesian inference of trial-level analysis of ERP data.
CALC: Bayesian covariate-dependent anti-logistic circadian model for activity data . A stan software (and R utilities) for cohort-level Bayesian modeling of circadian rest-activity rhythms designed to integrate wearable actigraphy with demographic and clinical predictors.
sggmDAR: a Julia implementation for Bayesian estimation of sparse, time-varying Gaussian graphical models under hidden regime switching via discrete autoregressive (DAR) dynamics, featuring automatic order and complexity learning.
Page updated
Google Sites
Report abuse