About

Amy Mueller is a faculty member at Northeastern University College of Engineering, affiliated with the Department of Civil and Environmental Engineering (CEE) and the Marine Science and Engineering (MES) program. Her research focus involves areas related to civil and environmental engineering, with an emphasis on translational research. She is recognized for her contributions to advancing engineering education and research, as evidenced by her receipt of the Translational Research Award at the 27th Annual College of Engineering Faculty and Staff Awards. Her work contributes to the development of innovative solutions in her field, supporting the college's mission to foster engineering innovation and excellence.

Research topics

• Computer Science
• Medicine
• Engineering
• Composite material
• Artificial Intelligence
• Materials science
• Physics
• Data science
• Mathematics
• Pathology
• Management science
• Process engineering
• Environmental science
• Biology
• Internal medicine
• Statistics
• Geology

Selected publications

• Enhancing digital twin technology with community-led, science-driven participatory modeling: A case in green infrastructure planning

  Environment and Planning B Urban Analytics and City Science · 2025-02-19 · 3 citations

  articleOpen access

  Recent research, professional, and funding agendas have re-surfaced the importance of knowledge co-production and ethical participation to address urban tensions worldwide: urbanization and rapid climate change, disproportionately impacting socially vulnerable populations. Despite the rise of Digital Twins (DT), buoyed by the growth of computational and data technologies in the past 10 to 15 years, DT have fallen short of their promise to address these tensions. We present a participatory modeling (PM) platform, Fora.ai, to build on existing strengths of DT and overcome the most prevalent limitations of data-driven technologies. This platform (i.e., a set of visualization and simulation tools and facilitation and sense-making approaches) is organized around the iterative steps in PM: problem definition and goal setting, preference elicitation, collaborative scenario-building, simulation, tradeoff deliberation, and solution-building. We demonstrate the platform’s effectiveness when set within a stakeholder-led process that integrates diverse knowledge, data sources, and values in pursuit of equitable green infrastructure (GI) planning to address flooding. The immediate visualization of simulated impacts, followed by reflection on causal and spatial relationships and tradeoffs across diverse priorities, enhanced participants’ collective understanding of how GI interacts with the built environment and physical conditions to inform their intervention scenarios. The facilitated use of Fora.ai enabled a collaborative socio-technical sense-making process, whereby participants transitioned from untested beliefs to designs that were specifically tailored to the problem in the study area and the diversity of values represented, attending to both localized flooding and neighborhood-level impacts. They also derived generalizable design principles that could be applied elsewhere. We show how the combination of specific facilitation practices and platform features leverage the power of data, computational modeling, and social complexity to contribute to collaborative learning and creative and equitable solution-building for urban sustainability and climate resilience.

  PublisherDOI

• Use of a multihazard sensor system for understanding rider experience and environmental conditions on urban rail

  Environmental Research Infrastructure and Sustainability · 2025-11-12

  articleOpen accessSenior authorCorresponding

  Abstract The range of potential environmental hazards to which public transit riders and operators are exposed include vibration, noise, and air pollution. Understanding these conditions—and their interactions—at a granular level across geographically expansive networks is critical to assessing, designing, and evaluating options for infrastructure improvements. However, the majority of studies to date have focused on a single or few parameters at varied spatial and temporal scales. This work presents a cost-effective and semi-automated approach to simultaneously measuring vibration, noise, and air pollution (particulate matter) at a resolution sufficient to resolve differences in rider experience on a segment-by-segment basis ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩾</mml:mtext> </mml:mrow> </mml:math> 1 sample·min −1 ). The system was validated through a study of the Massachusetts Bay Transportation Authority urban rapid transit system (Boston, MA). Vibration, noise, and particulate matter hotspots were identified. Vibration data grouped by line, perhaps pointing to differences in train or track characteristics within the transit network, while noise hotspots were not predicted by studied system characteristics and therefore are likely driven by track-specific issues (e.g. aging, curves). Air pollution levels were systematically higher (1) during peak ridership hours and (2) when trains traveled through underground sections of the system, with an abrupt change at the segment where trains transitioned between below and above ground. Correlations are observed between hazards, especially between air pollution and noise ( R = 0.54), implying that targeted interventions (e.g. related to braking or rail systems) could be especially effective in improving the experience of the rider, especially in underground sections where there is less ventilation and more reverberation. This study presents the utility of a multihazard approach for improving understanding of the rider experience in public transit systems and as a method for informing transition system management, suggesting an important next step of assessment by infrastructure management experts to develop processes for converting data-driven insights into candidate system improvements.

  PublisherDOI

• Poster Abstract: Towards a Predictive Model for Improved Placement of Solar-Powered Urban Sensing Nodes

  2024-05-13

  articleSenior author

  In a world driven by data, cities are increasingly interested in deploying networks of smart city devices for urban and environmental monitoring. To be successful, these networks must be reliable, low-cost, and easy to install and maintain—criteria that are all significantly affected by the design choices around power and can seemingly be satisfied with the use of solar energy. However, solar power is not ubiquitous throughout cities, making it difficult to know where to place nodes to avoid charging issues and thus potentially increasing maintenance costs. This abstract describes the development of a machine learning model that predicts whether any arbitrary location in a city will have solar charging issues. Using data from a large-scale real-world solar-powered sensor deployment in Chicago, Illinois and open data about building location and height, the binary classification model outputs the probability of adequate solar charging at a node location with 77% accuracy on the held-out test set. This work lays the foundation for those deploying future solar-powered urban sensor networks to have more confidence in the reliability of their chosen node locations.

  PublisherDOI

• Author response for "The role of sensors and community engagement in the mission toward equitable, healthy cities"

  2024-08-10

  peer-review1st authorCorresponding
  PublisherDOI

• Chromium removal from concentrated ammonium-nitrate solution: Electrocoagulation with iron in a plug-flow reactor

  Separation and Purification Technology · 2024-08-24 · 4 citations

  article
  PublisherDOI

• 173 Spatial transcriptomics analysis reveals association between DDR1-collagen interactions and immune exclusion in human cancers

  Regular and Young Investigator Award Abstracts · 2024-11-01

  articleOpen accessSenior author

  <h3>Background</h3> Discoidin domain receptor 1 (DDR1) is overexpressed in many cancers and is a potential therapeutic target. DDR1 is implicated in immune exclusion and collagen alignment in in vivo models¹ and is associated with immune exclusion across human tumors based on analysis of H&amp;E, multiplex immunofluorescence, and bulk gene expression.² The emergence of spatial transcriptomics technology has enabled highly-multiplexed or whole-transcriptome spatially-resolved biomarker analysis. Here, we compiled and harmonized Visium spatial gene expression data (10x Genomics) from 224 human tumors across 14 tumor types and investigated the relationship between DDR1, DDR1-collagen interactions, and immune exclusion. <h3>Methods</h3> Spatial gene expression data from human tumors were collected from various publications.^3-21 Gene expression was normalized to mean of 30,000 counts per spot per sample and using SCTransform within each sample to allow for comparison of gene expression across and within samples. Spots were clustered using Louvain clustering. Clusters were assigned as stromal or epithelial based on enrichment of a stromal gene signature.²² The degree of immune exclusion was measured using the log2 ratio of average expression of a CD8 signature²³ in stromal vs. epithelial spots for each sample (figure 1). Samples with mean CD8 signature scores in the lowest 20th percentile were removed. Ligand-receptor (LR) interaction analysis was performed using COMMOT.²⁴ LR co-expression is defined as the sum of signaling received across spots for each sample and for each LR pair from CellChatDB, CellTalkDB, and custom-defined DDR1-collagen interactions (figure 2). <h3>Results</h3> High DDR1 expression is associated with CD8 exclusion across the full dataset of 14 tumor types (r=0.27, p = 0.0006), with strongest association in hormone receptor positive breast cancer (BC) (r = 0.67, p = 0.0003) and non-small cell lung cancer (r = 0.58, p = 0.007) (figure 3). DDR1-collagen interactions are significantly associated with CD8 exclusion for most collagen types evaluated (I, II, III, IV, V, VIII, XI) across tumor types and within tumor types including breast and gastric cancers (figure 4). DDR1-collagen interactions are enriched among the top LR pairs and pathways associated with immune exclusion. <h3>Conclusions</h3> We developed a pipeline to quantify spatially-resolved gene expression across various human tumors. DDR1 mRNA and DDR1-collagen co-expression are correlated with the degree of CD8 exclusion with varying strength of associations across tumor types. These insights are valuable for better understanding the biology of DDR1 in the tumor microenvironment. <h3>References</h3> Sun X, <i>et al</i>. Tumour DDR1 promotes collagen fibre alignment to instigate immune exclusion. <i>Nature</i> 2021;<b>599</b>:673-678. Sher X, <i>et al</i>. Discoidin Domain Receptor 1 (DDR1) expression is associated with degree of immune exclusion across epithelial tumors. <i>Cancer Res</i> 2024;<b>84</b>:2916-2916. Arora R, <i>et al</i>. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. <i>Nat Commun</i> 2023;<b>14</b>:5029. Barkley D, <i>et al</i>. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. <i>Nat Genet</i> 2022;<b>54</b>:1192-1201. Bassiouni R, <i>et al</i>. Spatial transcriptomic analysis of a diverse patient cohort reveals a conserved architecture in triple-negative breast cancer. <i>Cancer Res</i> 2023;<b>83</b>:34-48. Coutant A,<i> et al</i>. Spatial transcriptomics reveal pitfalls and opportunities for the detection of rare high-plasticity breast cancer subtypes. <i>Lab Invest</i> 2023;<b>103</b>:100258. Denisenko E, <i>et al</i>. Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones. <i>Nat Commun</i> 2024;<b>15</b>:2860. De Zuani M, <i>et al</i>. Spatial transcriptomics (10x Visium) of human NSCLC lesions and non-involved tissue. <i>BioStudies</i> 2024. Garbarino O,<i> et al</i>. Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis. <i>Aging Cell</i> 2023;<b>22</b>:e13853. Human Lung Cancer, 11 mm Capture Area (FFPE), Spatial Gene Expression Dataset by Space Ranger v2.0.1, 10x Genomics, (2023, February 13) Jang Ei, <i>et al.</i> Clinical molecular subtyping reveals intrinsic mesenchymal reprogramming in gastric cancer cells. <i>Exp Mol Med</i>. 2023;<b>55</b>:974-986. Kim S, <i>et al</i>. Integrative analysis of spatial and single-cell transcriptome data from human pancreatic cancer reveals an intermediate cancer cell population associated with poor prognosis. <i>Genome Med</i> 2024;<b>16</b>:20. Lyubetskaya A, <i>et al</i>. Assessment of spatial transcriptomics for oncology discovery. <i>Cell Rep Methods</i> 2022;<b>2</b>. Mei Y, <i>et al</i>. Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response. <i>Nat Cancer</i> 2023;<b>4:</b>1273-1291. Meylan M, <i>et al</i>. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. <i>Immunity</i> 2022;<b>55</b>:527-541. Ren Y, <i>et al</i>. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. <i>Nat Commun</i> 2023;<b>14</b>:1028. Stur E, <i>et al</i>. Spatially resolved transcriptomics of high-grade serous ovarian carcinoma. <i>iScience</i> 2022;<b>25</b>(3). Tashireva L,<i> et al</i>. Spatial heterogeneity of integrins and their ligands in primary breast tumors.<i> Discov Med</i> 2023;<b>35</b>:910-920. Various Tumors, Spatial Gene Expression Dataset by Space Ranger, 10x Genomics. Villemin J, <i>et al</i>. Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. <i>Nucleic Acids Res</i> 2023;<b>51</b>:4726-4744. Zhu J, <i>et al</i>. Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. <i>Exp Mol Med</i> 2022;<b>54</b>:2060-2076. Shih AJ, <i>et al</i>. Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq. <i>PLoS one</i> 2018;<b>13</b>:e0206785. Jerby-Arnon L, <i>et al</i>. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. <i>Cell</i> 2018; <b>175</b>:984-997. Cang Z, <i>et al</i>. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. <i>Nat. Methods</i> 2023;<b>20</b>:218-228.

  PublisherOA PDFDOI

• A kinetic model for cathodic degradation of explosives in a flow-through electrochemical reactor

  Journal of Water Process Engineering · 2024-02-01 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

• The role of sensors and community engagement in the mission toward equitable, healthy cities

  Environmental Research Letters · 2024-08-16 · 4 citations

  articleOpen access1st authorCorresponding

  The role of sensors and community engagement in the mission toward equitable, healthy cities, Amy Mueller, M. Patricia Fabian, Bianca Navarro-Bowman, Barbara Espinosa Barrera, Yasser Aponte, Ben Cares, Karl Allen, Roseann Bongiovanni, Madeleine Scammel

  PublisherOA PDFDOI

• In Pursuit of Local Solutions for Climate Resilience: Sensing Microspatial Inequities in Heat and Air Pollution within Urban Neighborhoods in Boston, MA

  Sustainability · 2023-02-07 · 13 citations

  articleOpen accessSenior author

  Environmental hazards vary locally and even street to street resulting in microspatial inequities, necessitating climate resilience solutions that respond to specific hyperlocal conditions. This study uses remote sensing data to estimate two environmental hazards that are particularly relevant to community health: land surface temperature (LST; from LandSat) and air pollution (AP; from motor vehicle volume via cell phone records). These data are analyzed in conjunction with land use records in Boston, MA to test (1) the extent to which each hazard concentrates on specific streets within neighborhoods, (2) the infrastructural elements that drive variation in the hazards, and (3) how strongly hazards overlap in space. Though these data rely on proxies, they provide preliminary evidence. Substantial variations in LST and AP existed between streets in the same neighborhood (40% and 70–80% of variance, respectively). The former were driven by canopy, impervious surfaces, and albedo. The latter were associated with main streets and zoning with tall buildings. The correlation between LST and AP was moderate across census tracts (r = 0.4) but modest across streets within census tracts (r = 0.16). The combination of results confirms not only the presence of microspatial inequities for both hazards but also their limited coincidence, indicating that some streets suffer from both hazards, some from neither, and others from only one. There is a need for more precise, temporally-dynamic data tracking environmental hazards (e.g., from environmental sensor networks) and strategies for translating them into community-based solutions.

  PublisherOA PDFDOI

• Updated diagnostic criteria and nomenclature for neurofibromatosis type 2 and schwannomatosis: An international consensus recommendation

  Genetics in Medicine · 2022-06-09 · 271 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Harold F. Hemond

  Massachusetts Institute of Technology

  21 shared

• Matthew Orosz

  Massachusetts Institute of Technology

  17 shared

• Miriam J. Smith

  University of Manchester

  10 shared

• D. Gareth Evans

  The Christie NHS Foundation Trust

  10 shared

• Conxi Lázaro

  7 shared

• Eva Trevisson

  University of Padua

  6 shared

• P. Wolkenstein

  6 shared

• Victor‐Felix Mautner

  Universität Hamburg

  6 shared

Labs

• Environmental Sensing LaboratoryPI

Awards & honors

• Excellence in Mentoring Award (2026)
• Faculty Research Team Award – iSUPER Impact Engine (2025)
• Faculty Fellow Award (2024)
• AEESP Distinguished Service Award (2023)
• Northeastern University CEE Excellence in Teaching Award (20…