Digital Commons Network^™

Solar Power Forecasting Using Deep Learning Techniques, Fatma Abdulrahman

Theses

The growing utilization of solar energy and other renewable energy sources has heightened the demand for a proper forecasting model that enables efficient control of the energy supply as well as grid reliability. In this work, we concentrate about the use of novel techniques in ML models, namely Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to try to forecast power output in a solar-powered plant using past weather and power output records. Production of solar energy is naturally volatile because it is dependent on the weather conditions and is problematic in nature when integrated into power grids. Conventional …

Teaching Ai For Radiology Applications: A Multisociety-Recommended Syllabus From The Aapm, Acr, Rsna, And Siim, Felipe Kitamura, Timothy Kline, Daniel Warren, Linda Moy, Roxana Daneshjou, Farhad Maleki, Igor Santos, Judy Gichoya, Walter Wiggins, Brian Bialecki, Kevin O'Donnell, Adam E. Flanders, Matt Morgan, Nabile Safdar, Katherine P. Andriole, Raym Geis, Bibb Allen, Keith Dreyer, Matt Lungren, Monica J. Wood, Marc Kohli, Steve Langer, George Shih, Eduardo Farina, Charles E. Kahn, Ingrid Reiser, Maryellen Giger, Christoph Wald, John Mongan, Tessa Cook, Neil Tenenholtz

Department of Radiology Faculty Papers

No abstract available

Investigating Machine Learning Algorithms To Classify Label-Free Images Of Pancreatic Neuroendocrine Neoplasms, Noelle Daigle, Shuyuan Guan, Suzann Duan, Thomas G Knapp, Eung-Joo Lee, Ali Azhdarinia, Sukhen C Ghosh, Solmaz Aghaamiri, Servando Hernandez Vargas, Naruhiko Ikoma, Jeannelyn Estrella, Martin J Schnermann, Tobias Else, Michelle Kang Kim, Juanita L Merchant, Travis W Sawyer

The Brown Foundation: Institute of Molecular Medicine

Significance: Pancreatic neuroendocrine neoplasms (PNENs) are an uncommon cancer whose incidence rate has increased dramatically in recent years. Surgery is the only potentially curative treatment, which relies on both preoperative tumor localization and postoperative margin definition using histopathological examination for decision making. If pathology could be automated, valuable time and resources could be saved.

Aim: In this study, we investigate the ability of machine learning (ML) with handcrafted features, as well as deep learning, to classify label-free microscopy images of PNENs as a first step toward automated pathology of such tumors.

Approach: Patient samples of two different preparation types were …

Optimization Of A Mobile Imaging System To Aid In Evaluating Patients With Oral Lesions In A Dental Care Setting, Ruchika Mitbander, Jennifer Carns, Richard A Schwarz, Sean Anderson, Geethanjali Vipulanandan, Miriam Hernandez, Amelia E Hartzell, Jun Jiao, Ilona Rutkowska, Safia Durab, Sahar Sadeghi, Ashlee D Clayton, Nancy Bass, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum

Faculty, Staff and Student Publications

Significance: Oral cancer is often diagnosed at an advanced stage even though the oral cavity is easily accessible for examination. There is a need for tools to support early detection and help non-expert clinicians make appropriate referral recommendations.

Aim: We aim to evaluate a mobile detection of oral cancer (mDOC) smartphone-based autofluorescence imaging system by collecting data in a representative, low-prevalence population and optimizing a referral algorithm suitable for deployment in community dental settings.

Approach: Data were collected from patients at a community dental clinic using the mDOC system, which captures white light and autofluorescence images. A multi-input deep learning …

Machine Learning Classification Of Eeg Responses To Pain-Related Vs Non-Pain-Related Stimulus In Preterm Infants, Lojain Hamwi, Hang Du, Sara Jasim, Xiaogang Wang, Vibhuti Shah, Carol Cheng, Lorenzo Fabrizi, Maria Fitzgerald, Judith Meek, Nicole Racine, Ian Stedman, Rebecca Pillai Riddell

Michigan Tech Publications

INTRODUCTION: Unmanaged pain in preterm infants can lead to long-term developmental consequences. Current pain assessment methods lack specificity, resulting in possible pain mismanagement in Neonatal Intensive Care Units (NICUs). This study explores the application of machine learning (ML) to differentiate between pain-related and non-pain-related cortical activity in preterm infants. OBJECTIVE: To evaluate the performance of ML models in distinguishing cortical EEG activity during a painful procedure in preterm infants across different postmenstrual ages (PMAs). METHODS: This observational study was conducted from June 2015 to May 2024 at Mount Sinai Hospital in Toronto, Canada, and University College London Hospital, United Kingdom. …

Machine Learning For Dissolved Oxygen Prediction In The Manawatu River Catchment, New Zealand, Imokhai T. Tenebe, Eunice O. Babatunde, Nkpa M. Ogarekpe, Egbe Etu Etu, Joshua Emakhu, Kindness E. Etu, Obiora B. Ezeudu, Oluwarotimi S. Oladele

Faculty Research, Scholarly, and Creative Activity

Uncontrolled anthropogenic activities have increased river pollution, resulting in poor water quality and a growing need for continuous monitoring, which is time-consuming and costly. This study evaluated the accuracy of machine learning (ML) models in predicting dissolved oxygen (DO) levels, aiming to reduce field monitoring efforts and minimize the number of measured parameters. Regression models (Huber, Linear, Ridge), Bagging methods (Extra Trees, Random Forest, Decision Trees), and Boosting techniques (Gradient Boosting, Light Gradient Boosting, AdaBoost) were tested using data from three sites in the Manawatu Catchment, New Zealand. Data spanning 1989–2014 included 12 water quality variables and was split into …

Fairer Public Benefit In Copyright Law, Amanda Levendowski Tepski

Georgetown Law Faculty Publications and Other Works

In 1966, a court considered expressly whether a secondary use of copyrighted works served a public benefit. While public benefit has become a subfactor of the fair use doctrine, it remains undefined, uncodified, and undertheorized. Courts often invoked public benefit in pivotal cases involving novel technologies, from home video recorders to digital libraries to algorithms--but they did not need to. After the recent Supreme Court decision in Google v. Oracle, however, public benefit is unavoidable: The Court stated that “we must take into account the public benefits the copying will likely produce.” A qualitative analysis of key public benefit cases …

Characterisation And Prediction Of Wax Precipitation For Effective Mitigation Using Data Analytics, Alek Deng Cyer Rehan, Aliyu Adebayo Sulaimon

Platform: A Journal of Engineering

The precipitation of wax in crude oil pipelines poses a significant operational challenge for the petroleum industry. This study proposes a novel approach to characterising crude oil and predicting wax appearance temperature (WAT) using advanced data analytics techniques, including group method of data handling (GMDH), support verctor machines (SVM), extreme gradient boosting (XGBoost) and various regression models, as alternatives to conventional artificial neural networks (ANN). Crude oil was classified into four categories: heavy oil/biodegraded, paraffinic oil, waxy oil, and light oil/paraffinic condensate. A detailed characterisation framework based on the gamma distribution function (GDF) was employed to split lumped hydrocarbon fractions …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Enhanced Prediction Of Condensation Pressure Drop In Mini And Microchannels Using Physics-Assisted Machine Learning, Farshad Barghi Golezani, Jiayuan Li, Forouzan Naderi, Alexander Meylikhov, Logan M. Pirnstill, Issam Mudawar, Chirag R. Kharangate

Faculty Scholarship

Reliable prediction of frictional pressure drop during condensation in mini‑ and microchannels underpins both thermal management effectiveness and overall heat transfer performance in compact two‑phase heat exchangers, cold‑plates, and on‑chip cooling loops. Excess pressure loss burdens pumps, raises electrical consumption, and can destabilize flow, whereas under‑prediction risks temperature overshoot and premature dryout. Conventional empirical correlations and flexible machine-learning models can lose accuracy once channel size, working fluid, or operating conditions stray beyond their testing range. This study uses a physics‑assisted machine‑learning framework that overlays an XGBoost residual learner on the Kim–Mudawar separated‑flow correlation to achieve high fidelity and robustness in …

Enhanced Pmu Data Imputation Using Variational Autoencoder: A Novel Approach For Improved Accuracy, Subin Koshy, Sunitha Rajan, Elizabeth P. Cheriyan

Turkish Journal of Electrical Engineering and Computer Sciences

Data collected from various measurement devices, such as phasor measurement units (PMUs), form the backbone of power system monitoring algorithms such as state estimation algorithms. Even though modern PMUs are efficient, data inaccuracies and loss can still occur due to problems like equipment malfunctions, communication issues, and hardware or software glitches. Missing values in the datasets can also affect the overall quality, impacting the algorithms dependent on them. This article introduces a novel technique for real-time imputation of missing data using variational autoencoder (VAE) and utilizing the latent space representation acquired from VAE. The method has demonstrated enhanced accuracy in …

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Novel Biological Prompting Technology: Automatic Directional Control By Eye-Tracking Sensor, Guannan Liu, Justin Wang, Hsiao Chun Wu, Gaojian Huang, Anil Kumar, Yiyan Wu, Shih Yu Chang, Yue Luo

Faculty Research, Scholarly, and Creative Activity

Biological prompting or human teleoperation have been appealing to many researchers lately. Hands-free and voice-free command or control of vehicles, aircraft, machines, robots, etc. would provide huge convenience to users and allow them for multi-tasking. In this work, we would like to dedicate to a new biological prompting function, namely automatic directional control using instantaneous eye gaze data. In this work, we would like to investigate how to apply advanced machine-learning models to design novel automatic gaze-direction identification techniques. We explore the difficulty, namely infliction with outliers and variations, in dealing with eye-tracking data and propose to transform the original …

Machine Learning Approach For Fake Image Forensics, Abdullah Mujawib Alashjaee

University of Bisha Journal for Basic and Applied Sciences

Digital forensics is crucial in investigating and analyzing digital evidence, including images, to uncover potential crimes and identify manipulated or fraudulent content. A significant challenge in digital forensics is distinguishing between authentic and manipulated images. This paper applies a popular pre-trained deep learning model, VGG16, for feature extraction in real vs. fake image detection and various machine learning (ML) models as classifiers. The feature extraction part until the flatten layer of the VGG16 model is utilized to extract discriminative features that capture high-level image representations comprising local and global patterns. ML models, including Random Forest, Logistic Regression, Decision Tree, and …

Novel Convolution Neural Network For Enhanced Network Attack Pattern Recognition, Salahaldeen Duraibi

University of Bisha Journal for Basic and Applied Sciences

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel …

Uav-Based Transport Management For Smart Cities Using Machine Learning, Sweekruthi Balivada, Jerry Gao, Yuting Sha, Manisha Lagisetty, Damini Vichare

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? A smart transport management system based on UAV data integrating advanced machine learning and deep learning techniques is proposed to enhance road anomaly detection and severity classification. The system employs a comprehensive multi-stage framework, integrating a high-precision obstacle detection model, six specialized severity classification models, and an aggregation model to deliver accurate anomaly assessment, enabling strategic, data-driven road maintenance and enhanced transportation safety. What is the implication of the main finding? A scalable and efficient solution is proposed to enhance road safety and optimize transportation management through intelligent anomaly detection and severity assessment. This …

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding laser-induced dynamics on metal surfaces poses significant challenges due to the intricate interplay between electronic and phononic degrees of freedom, which evolve on distinct timescales. In this study, we introduce a machine learning-accelerated approach to molecular dynamics simulations that incorporates anisotropic electronic friction, providing deeper insights into these complex processes. Our framework extends the accessible time and length scales for nonadiabatic dynamics simulations, enabling a detailed investigation of the laser-induced activation of oxygen on the Ru(0001) surface. Statistical analysis reveals that strong electronic excitation dominates the first 800 fs after laser exposure. Beyond this timescale, energy deposited by electronic …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty, Staff and Students Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Developing Workflows For Passive Acoustic Detection Of Bedload Transport, Quinn Morgan

Dissertations and Theses

Bedload transport is defined as the amount of sediment, including gravel and rocks, traveling down stream. Monitoring bedload transport is important for river safety, hydrological studies and conservation efforts. Existing methods of directly measuring bedload transport (or bedload flux) involve lowering a collection device into a river and measuring the sediment collected; which can be expensive and time consuming. Hydroacoustic sensors, such as hydrophones, have had success tracking bedload flux remotely. This works by measuring the relatively high frequency of sediment impacts to map onto total bedload transported. No perfected method for detection of sediment generated noise (SGN) currently exists. …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

A Polar Turbulence Invariant Map With Applicability To Realisable Machine Learning Turbulence Models, James G. Wnek, Christopher Schrock, Eric M. Wolf, Mitch Wolff

Mechanical and Materials Engineering Faculty Publications

Invariant maps are a useful tool for turbulence modelling, and the rapid growth of machine learning-based turbulence modelling research has led to renewed interest in them. They allow different turbulent states to be visualised in an interpretable manner and provide a mathematical framework to analyse or enforce realisability. Current invariant maps, however, are limited in machine learning models by the need for costly coordinate transformations and eigendecomposition at each point in the flow field. This paper introduces a new polar invariant map based on an angle that parametrises the relationship of the principal anisotropic stresses, and a scalar that describes …

Hybrid Feature Selection And Ensemble Classification For Climate Change Indicators: A Machine Learning Approach, Firas Ali Hashim, Yasmin Makki Mohialden, Nadia Mahmood Hussien

Terra Joule Journal

Climate change poses a significant challenge across various domains, necessitating advanced analytical techniques for effective monitoring and decision-making. This paper introduces a hybrid machine learning pipeline that selects and classifies features of climate change indicators. A synthetic dataset, representing different climate indicators and their application fields, is generated and processed using a combination of SelectKBest and Recursive Feature Elimination with Cross-Validation (RFECV) for robust feature selection. An ensemble of random forest and gradient-boosting classifiers is employed to enhance prediction accuracy. The predicted methodology has attained the lower initial accuracy of classification of 0.4 with five chosen climate characteristics. Optimizing the …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Computational String Theory Using Machine Learning, Giorgi Butbaia

Doctoral Dissertations

Low-energy couplings of certain superstring theories are related to the masses of the particles. One of the most promising superstring theories is $\mathcal{N}=1$ supersymmetric $E_8\times E_8$ heterotic string theory. In a certain embedding of the standard model into $E_8\times E_8$ gauge group of the heterotic string, the particle spectrum consists of $(\mathbf{27})^3$, $(\overline{\mathbf{27}})^3$, $(\mathbf{1})^3$ and $\mathbf{1}\cdot \mathbf{27}\cdot\overline{\mathbf{27}}$ couplings. While the $(\overline{\mathbf{27}})^3$ couplings are quasi-topological, the $(\mathbf{27})^3$ explicitly depend on the Ricci-flat metric on Calabi-Yau.

In this dissertation, we explore recently developed techniques for calculating the normalized Yukawa couplings on various Calabi-Yau manifolds. In Chapter 2, we describe a novel architecture, …

Deepmvp: Deep Learning Models Trained On High-Quality Data Accurately Predict Ptm Sites And Variant-Induced Alterations, Bo Wen, Chenwei Wang, Kai Li, Ping Han, Matthew V Holt, Sara R Savage, Jonathan T Lei, Yongchao Dou, Zhiao Shi, Yi Li, Bing Zhang

Center on Aging Staff Publications

Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its …

Using Machine Learning Methods To Evaluate Undergraduates’ Executive Function In The Context Of Game-Based Measurement, Ming Chen

Dissertations, Theses, and Capstone Projects

Traditional performance-based measurements have faced criticism due to their potential to induce test fatigue and their limited ecological validity. In contrast, game-based assessments have demonstrated the capacity to engage participants effectively in tests and enhance ecological validity by replicating real-world scenarios. Nevertheless, conventional statistical approaches may prove insufficient in capturing the intricate, non-linear associations between participants' gaming performance and their cognitive capabilities. Leveraging the potential of machine learning (ML) techniques, which can model complex problems, offers a promising avenue for estimating students' performance in video games. This dissertation aims to explore various ML methods for the validation of three game-based …

Novel Machine Learning Methods For Quasar Variability And Strong Gravitational Lensing, Joshua Fagin

Dissertations, Theses, and Capstone Projects

Machine learning methods are well suited to handle the unprecedented data volume expected from upcoming wide-field surveys. The European Space Agency's Euclid telescope was launched in July 2023 and is expected to observe billions of galaxies at high resolution, including tens of thousands of strongly lensed galaxies. The Rubin Observatory Legacy Survey of Space and Time (LSST) will monitor tens of millions of quasars throughout its ten-year lifetime, thousands of which will be strongly lensed. This flood of data will enable measuring lens galaxy mass distributions and substructure, as well as probing quasar accretion disk structure and black hole properties …