About me

I am pursuing a Ph.D. in Chemical Engineering at Northeastern University under the supervision of Professor Eduardo Sontag. My research focuses in the modeling and control of dynamical systems in systems biology and cancer therapy with interests that extend to the areas of light optics, applied mathematics, high-performance computing, chemical engineering, and machine learning.

I can be reached at anhphong.t [at] gmail [dot] com. I am always happy to discuss collaborations.

Publications

Delicate balances in cancer chemotherapy: modeling immune recruitment and emergence of systemic drug resistance.
Tran, A.P., Al-Radhawi, M.A., Kareva, I, Junjie, W, Waxman, D.J. and Sontag E.D., 2019. In submission to Nature Cancer. [bioRxiv PDF]

Modeling voxel-based Monte Carlo light transport with curved and oblique boundary surfaces.
Tran, A.P. and S.L. Jacques, 2019. Journal of Biomedical Optics (accepted).

Monte Carlo light transport simulations are most oftentimes performed in regularly spaced three-dimensional voxels, a type of data representation that naturally struggles to represent boundary surfaces with curvature and oblique angles. Not accounting properly for such boundaries with index of refractivity mismatches can lead to important inaccuracies, not only in the calculated angles of reflection and transmission, but also in the amount of light that transmits through or reflects from these mismatched boundary surfaces.
A new Monte Carlo light transport algorithm is introduced to deal with curvature and oblique angles of incidence when simulated photons encounter mismatched boundary surfaces.

Improving model-based fNIRS analysis using mesh-based anatomical and light transport models.
Tran, A.P., Yan, S. and Fang, Q, 2019. Neurophotonics (accepted). [Software] [arXiV PDF]

Over the past decade, functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brain. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. In this paper, we aim to highlight the importance of accurate anatomical modeling in the context of fNIRS, and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis.

Selective photobiomodulation for emotion regulation: model-based dosimetry study.
Tran, A.P., Cassano, P., Katnani, H., Bleier, B.S., Hamblin, M.R., Yuan, Y. and Fang, Q., 2019. Neurophotonics. [PDF]

The transcranial photobiomodulation (t-PBM) technique is a promising approach for the treatment of a wide range of neuropsychiatric disorders, including disorders characterized by poor regulation of emotion such as major depressive disorder (MDD). In this study, we examined various approaches to deliver red and near-infrared light to the dorsolateral (dlPFC) and ventromedial prefrontal (vmPFC) cortex in the human brain, both of which have shown strong relevance to the treatment of MDD.

Dual-grid mesh-based Monte Carlo algorithm for efficient photon transport simulations in complex three-dimensional media.
Yan, S., Tran, A.P. and Fang, Q., 2019. Journal of Biomedical Optics. [PDF] [Software]

The mesh-based Monte Carlo (MMC) method is an efficient algorithm to model light propagation inside tissues with complex boundaries, but choosing appropriate mesh density can be challenging. A fine mesh improves the spatial resolution of the output but requires more computation. In this paper, we propose an improved MMC dual-grid MMC or DMMC to accelerate photon simulations by using a coarsely tessellated tetrahedral mesh for ray-tracing computation and an independent voxelated grid for output data storage. The decoupling between the ray-tracing and data storage grids allows us to simultaneously achieve faster simulations and improved output spatial accuracy.

On the estimation of high-dimensional surrogate models of steady-state of plant-wide processes characteristics.
Tran, A.P. and Georgakis, C., 2018. Computers & Chemical Engineering. [PDF]

Industrial chemical processes, especially in the presence of recycle loops, exhibit highly nonlinear behaviors that oftentimes do not have a closed-form expression. In this work, we show how high-dimensional steady-state surrogate models can be developed with high accuracy by combining the use of design of experiment techniques to alleviate computational costs and regularization techniques to avoid overfitting given the large number of terms involved. Through examples of three chemical process simulations (the Tennessee Eastman problem, an ethyl benzene, and a mono-isopropyl amine plant), we demonstrate the accuracy of the models and show applications of these surrogate models.

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