Arindam Fadikar
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View article: A spatially-aware unsupervised pipeline to identify co-methylation regions in DNA methylation data
A spatially-aware unsupervised pipeline to identify co-methylation regions in DNA methylation data Open
DNA methylation (DNAm) plays a central role in modern epigenetic research; however, the high dimensionality of DNAm data comprising hundreds of thousands of spatially ordered probes continues to present major analytical challenges. The mul…
View article: Adaptive Grid-Based Thompson Sampling for Efficient Trajectory Discovery
Adaptive Grid-Based Thompson Sampling for Efficient Trajectory Discovery Open
Bayesian optimization (BO) is a powerful framework for estimating parameters of computationally expensive simulation models, particularly in settings where the likelihood is intractable and evaluations are costly. In stochastic models ever…
View article: Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes
Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes Open
Public health experts studying infectious disease spread often seek granular insights into population health outcomes. Metapopulation models offer an effective framework for analyzing disease transmission through subpopulation mixing. Thes…
View article: Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions
Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions Open
There is much focus in the field of HIV prevention research on understanding the impact of social determinants of health (e.g., housing, employment, incarceration) on HIV transmission and developing interventions to address underlying stru…
View article: Gearing Gaussian process modeling and sequential design towards stochastic simulators
Gearing Gaussian process modeling and sequential design towards stochastic simulators Open
This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from the literature are presented: input varying noise variance, …
View article: O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression
O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression Open
This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it p…
View article: Group Heteroscedasticity - A Silent Saboteur of Power and False Discovery in RNA-Seq Differential Expression
Group Heteroscedasticity - A Silent Saboteur of Power and False Discovery in RNA-Seq Differential Expression Open
Despite the availability of several high-profile, state-of-the-art methods, analyzing bulk RNA-Seq data continues to face significant challenges. Evidence from recent studies has highlighted that popular differential expression (DE) tools,…
View article: Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo Open
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arr…
View article: O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes
O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes Open
Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to acco…
View article: Trajectory-oriented optimization of stochastic epidemiological models
Trajectory-oriented optimization of stochastic epidemiological models Open
Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a mo…
View article: Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis Open
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing speciali…
View article: Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z Open
Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Mot…
View article: Analyzing Stochastic Computer Models: A Review with Opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Open
In modern science, computer models are often used to understand complex\nphenomena, and a thriving statistical community has grown around analyzing\nthem. This review aims to bring a spotlight to the growing prevalence of\nstochastic compu…
View article: Scalable Epidemiological Workflows to Support COVID-19 Planning and Response
Scalable Epidemiological Workflows to Support COVID-19 Planning and Response Open
The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to pr…
View article: Scalable Statistical Inference of Photometric Redshift via Data\n Subsampling
Scalable Statistical Inference of Photometric Redshift via Data\n Subsampling Open
Handling big data has largely been a major bottleneck in traditional\nstatistical models. Consequently, when accurate point prediction is the primary\ntarget, machine learning models are often preferred over their statistical\ncounterparts…
View article: Scalable Epidemiological Workflows to Support COVID-19 Planning and Response
Scalable Epidemiological Workflows to Support COVID-19 Planning and Response Open
The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to pr…
View article: Forecasting influenza activity using machine-learned mobility map
Forecasting influenza activity using machine-learned mobility map Open
View article: Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Scalable Statistical Inference of Photometric Redshift via Data Subsampling Open
View article: Analyzing Stochastic Computer Models: A Review with Opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Open
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer…
View article: Stochastic Simulators: An Overview with Opportunities
Stochastic Simulators: An Overview with Opportunities Open
In modern science, deterministic computer models are often used to understand complex phenomena, and a thriving statistical community has grown around effectively analysing them. This review aims to bring a spotlight to the growing prevale…
View article: Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints
Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints Open
Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minim…