A5065662869- Scientific Computing and Data Management
- HIV, Drug Use, Sexual Risk
- Hepatitis C virus research
- Distributed and Parallel Computing Systems
- Simulation Techniques and Applications
- Colorectal Cancer Screening and Detection
- demographic modeling and climate adaptation
- COVID-19 epidemiological studies
- Health Systems, Economic Evaluations, Quality of Life
- Genetic factors in colorectal cancer
- HIV/AIDS Research and Interventions
- Water resources management and optimization
- Liver Disease Diagnosis and Treatment
- Hepatitis B Virus Studies
- Mathematical Biology Tumor Growth
- Mathematical and Theoretical Epidemiology and Ecology Models
- Complex Systems and Decision Making
- Advanced Causal Inference Techniques
- Opioid Use Disorder Treatment
- Substance Abuse Treatment and Outcomes
- Statistical Methods and Inference
- Opinion Dynamics and Social Influence
- Hydrology and Watershed Management Studies
- Gastric Cancer Management and Outcomes
- Mental Health Research Topics
Argonne National Laboratory
2016-2025
University of Chicago
2015-2025
University of Illinois Chicago
2023-2025
Chicago Department of Public Health
2021-2024
Indiana University Bloomington
2020
University of Moratuwa
2020
Alex's Lemonade Stand Foundation
2019
Decision Sciences (United States)
2018
Kellogg's (Canada)
2018
Dallas County
2013
Abstract Purpose This paper is to describe development of the features and functions Repast Simphony, widely used, free, open source agent-based modeling environment that builds on 3 library. Simphony was designed from ground up with a focus well-factored abstractions. The resulting code has modular architecture allows individual components such as networks, logging, time scheduling be replaced needed. family software collectively been under continuous for more than 10 years. Method Includes...
Abstract Background Blood-based biomarker tests can potentially change the landscape of colorectal cancer (CRC) screening. We characterize conditions under which blood test screening would be as effective and cost-effective annual fecal immunochemical testing or decennial colonoscopy. Methods used 3 Cancer Information Surveillance Modeling Network–Colon models to compare scenarios no screening, testing, colonoscopy, a meeting Centers for Medicare & Medicaid (CMS) coverage criteria (74%...
One way to reduce the time of conducting optimization studies is evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions Bayesian have been proposed. They work by building a surrogate model black-box simultaneously select multiple via an infill criterion. Still, despite increased availability computing resources that enable large-scale parallelism, strategies for selecting few tens evaluations become limiting due complexity more...
Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often different patterns that require disparate software packages and complex data flows cause difficulties in assembling managing experiments on these machines. This paper presents a workflow makes progress scaling ensembles, specifically this first release, ensembles of deep neural networks...
Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host sometimes unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory clinical studies, helping identify the factors driving treatment’s success or failure. However, given uncertainties regarding underlying biology, these take many potential forms, in addition to encompassing...
Abstract The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale transport, and emergent tissue-scale propagation limited. Moreover, little known about immune system-virus-tissue interactions how these can result in low-level (asymptomatic) infections some cases acute respiratory distress syndrome (ARDS) others,...
CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents Chicago as they move between colocate in 1.2 distinct places, including households, schools, workplaces, hospitals, determined by individual hourly activity schedules dynamic such isolating because symptom onset. Disease progression dynamics incorporated within each agent track transitions possible COVID-19 disease states, based on heterogeneous attributes, exposure...
As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities model exploration approaches (e.g., calibration, metaheuristics, data assimilation) simulations (or any “black box” application code) parallel scripting language to run scientific workflows on a variety resources, from desktop...
As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities model exploration approaches (e.g., calibration, metaheuristics, data assimilation) simulations (or any black box application code) parallel scripting language to run scientific workflows on a variety resources, from desktop...
We present an integrated framework for enabling dynamic exploration of design spaces cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our combines PhysiCell, open source agent-based platform and other multicellular systems, EMEWS, extreme-scale model exploration. build immunosurveillance against heterogeneous tumours, which includes spatial dynamics stochastic tumour-immune contact interactions. implement active learning genetic...
Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as development of cancer) and effect actions screening) on these events. often have many unknown parameters; calibration is searching parameter space select parameters result in accurate MSM prediction a wide range targets. We develop Incremental Mixture Approximate Bayesian Computation...
The aftermath of the initial phase COVID-19 pandemic may contribute to widening disparities in colorectal cancer (CRC) outcomes due differential disruptions CRC screening. This comparative microsimulation analysis uses two CISNET models simulate impact ongoing screening induced by on long-term outcomes. We evaluate three channels through which was disrupted: delays screening, regimen switching, and discontinuation. these measured number life-years lost compared a scenario without any...
Chronic hepatitis B virus (HBV) infection poses a significant global health threat, causing severe liver diseases including cirrhosis and hepatocellular carcinoma. We characterized HBV DNA kinetics in primary human hepatocytes (PHH) over 32 days post-inoculation (pi) used agent-based modeling (ABM) to gain insights into lifecycle spread. Parallel PHH cultures were mock-treated or entry inhibitor Myr-preS1 (6.25 μg/mL) was initiated 24h pi. In untreated PHH, 3 viral kinetic patterns...
ABSTRACT Agent‐based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting diverse individual interactions environments. However, these are usually stochastic highly parametrized, requiring precise calibration predictive performance. When considering realistic numbers of agents properly stochasticity, this high‐dimensional can be computationally prohibitive. This paper presents a random forest‐based surrogate technique...
We introduce a simple mechanism for the evolution of small world networks. Our model is growing network in which all connections are made locally to geographically nearby sites. Although purely locally, growth leads stretching old and high clustering. results suggest that abundance networks constrained systems natural consequence system local interactions.
The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds seemingly equivalent systems, many isolated research claims, and a steep learning curve. To address some these challenges lay the groundwork transforming workflows development, WorkflowsRI ExaWorks projects partnered to bring international community together. This paper reports on discussions findings from two virtual "Workflows Community Summits" (January April, 2021). overarching goals...
'Getting to Zero' (GTZ) initiatives aim eliminate new HIV infections over a projected time frame. Increased preexposure prophylaxis (PrEP) uptake among populations with the highest incidence, such as young Black MSM, is necessary accomplish this aim. Agent-based network models (ABNMs) can help guide policymakers on strategies increase PrEP uptake.
Scientific workflows have been used almost universally across scientific domains, and underpinned some of the most significant discoveries past several decades. Many these high computational, storage, and/or communication demands, thus must execute on a wide range large-scale platforms, from large clouds to upcoming exascale high-performance computing (HPC) platforms. These executions be managed using software infrastructure. Due popularity workflows, workflow management systems (WMSs)...