Data Science Commons^™

Out Of Order And Causally Correct: Ready-Event Discovery Through Data-Dependence Analysis, Erik John Jensen, James Leathrum Jr., Christopher Lynch, Katherine Smith, Ross Gore

Electrical & Computer Engineering Faculty Publications

Data-dependence analysis can identify causally-unordered events in a pending event set. The execution of these events is independent from all other scheduled events, making them ready for execution. These events can be executed out of order or in parallel. This approach may find and utilize more parallelism than spatial-decomposition parallelization methods, which are limited by the number of subdomains and by synchronization methods. This work provides formal definitions that use data-dependence analysis to find causally-unordered events and uses these definitions to measure parallelism in several discrete-event simulation models. A variant of the event-graph formalism is proposed, which assists with identifying …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

Leveraging Gpt-4o For Automated Extraction Of Neural Projections From Scientific Literature, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

Generalizing Medical Image Segmentation Task With Efficient Deep Learning Models, Abel A. Reyes-Angulo

Dissertations, Master's Theses and Master's Reports

Medical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Advancing Prediction Of Stimulant Medication Misuse Through Graph Representation Learning, Hamid Razavi

Theses

The misuse of stimulant prescription medications poses a significant and escalating public health concern in the United States, particularly among young adults. Addressing this issue requires sophisticated methodologies capable of uncovering complex patterns and relationships in data. Geometric Deep Learning, a paradigm designed to analyze data with non-Euclidean structures, has achieved remarkable success across various domains, offering a powerful framework for tackling complex graph structure data challenges.

This study leverages Graph Convolutional Networks (GCNs) to predict the likelihood of stimulant medication misuse using data from the National Survey on Drug Use and Health (NSDUH). Individuals are represented as nodes in …

Cropsync: Ai-Powered Sustainable Crop Management, Ziad Doughan, Ibrahim Mneimneh, Zouheir Nakouzi, Noor Al Khaib, Samer Damaj, Jamal Chaaban, Hamza Mrad, Sari Itani

BAU Journal - Science and Technology

CropSync is a smart agriculture system that uses AI and IoT technologies to enable sustain- able crop management and precision farming. The system aims to address the challenges faced by the agriculture sector, such as increasing food production to meet global population demands while minimizing environmental impact. CropSync integrates sensors, cameras, and cloud-based analytics to provide farmers with real-time insights and recommendations for optimizing crop cul- tivation. The system upholds engineering professional and ethical standards, considering broader social, environmental, and economic implications. From a social perspective, CropSync improves food security and enhances farmers’ livelihoods through increased productivity and efficient re- …

An Ontology-Based Approach For Understanding Appendicectomy Processes And Associated Resources, Xubing Hao, Licong Cui, Ping Yu

Faculty, Staff and Student Publications

Background: Traditional methods for analysing surgical processes often fall short in capturing the intricate interconnectedness between clinical procedures, their execution sequences, and associated resources such as hospital infrastructure, staff, and protocols.

Aim: This study addresses this gap by developing an ontology for appendicectomy, a computational model that comprehensively represents appendicectomy processes and their resource dependencies to support informed decision making and optimise appendicectomy healthcare delivery.

Methods: The ontology was developed using the NeON methodology, drawing knowledge from existing ontologies, scholarly literature, and de-identified patient data from local hospitals.

Results: The resulting ontology comprises 108 classes, including 11 top-level classes and …

Physics-Informed Heterogeneous Spatiotemporal Graph Neural Network For Reservoir Simulation, Ahmed A.M.A. Abdullah

LSU Master's Theses

Reservoir simulation is the state-of-the-art method for predicting the flow of petroleum reservoir fluids in porous media. It provides an accurate and unbiased prediction of the performance of petroleum reservoirs under different operating conditions. Despite its advantages, reservoir simulation is computationally expensive; with typical full-field simulation models running for several hours. This limitation is worsened when simulating reservoirs with several equations for each cell, such as multiphysics or compositional reservoir simulation. The goal of this research is to provide a fast and accurate spatiotemporal machine-learning model that incorporates discretized governing mass balance equations for training. To achieve this, we propose …

Influence Of Antibody–Drug Conjugate Cleavability, Drug-To-Antibody Ratio, And Free Payload Concentration On Systemic Toxicities: A Systematic Review And Meta-Analysis, Shou Ching Tang, Carrie Wynn, Tran Le, Martin Mccandless, Yunxi Zhang, Ritesh Patel, Nita Maihle, William Hillegass

School of Medicine Faculty Publications

While in theory antibody drug conjugates (ADCs) deliver high-dose chemotherapy directly to target cells, numerous side effects are observed in clinical practice. We sought to determine the effect of linker design (cleavable versus non-cleavable), drug-to-antibody ratio (DAR), and free payload concentration on systemic toxicity. Two systematic reviews were performed via PubMed search of clinical trials published between January 1998—July 2022. Eligible studies: (1) clinical trial for cancer therapy in adults, (2) ≥ 1 study arm included a single-agent ADC, (3) ADC used was commercially available/FDA-approved. Data was extracted and pooled using generalized linear mixed effects logistic models. 40 clinical trials …

Advancing Continuous Manufacturing: The Role Of Process Analytical Technology In Process Development, Samuel R. Henson

Electronic Theses and Dissertations

The pharmaceutical industry is actively pursuing technologies which improve manufacturing processes with the goal of producing high-quality pharmaceutical products for patients, manifesting in an industry-wide investment in continuous manufacturing (CM). Process analytical technology (PAT) has been recognized for its successful monitoring of critical quality attributes during routine production and is often cited alongside CM due to its ability to make timely, in-line measurements of intermediate materials. Various PAT tools are valuable in process development, particularly as continuous wet granulation processes are developed for use within pharmaceutical manufacturing. This work applied PAT and chemometric modeling during CM process development to enhance …

How Should China Respond To “Pan-Data Sovereignty” Competition Among China, U.S., And Eu—An Analysis Based On The Digital Stack Model?, Yan Liu, Congjing Ran

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data sovereignty has become deeply intertwined with various economic and social development factors such as technology, trade, economy, culture, society, and politics, leading to a “Pan-Data Sovereignty” competition pattern in the digital space. Through the digital stack model, which examines digital technologies in a layered framework, we can more clearly assess the competitive capacities in“Pan-Data Sovereignty” of China, United States, and European Union. The analysis identifies a three-tiered global “Pan-Data Sovereignty” competition structure among China, U.S., and EU, with each entity holding distinct advantages across various layers of the digital stack. Intense future competition is anticipated in fields such as …

Safety And Optimality Monitors For Learning-Enabled Systems Using Conformal Prediction, Jackson Cox

McKelvey School of Engineering Graduate Student Theses & Dissertations

The use of machine learning to create data-driven plant models and controllers has led to an increased need for safety and optimality monitors for model-based systems. System plant models are subject to uncertainty due to learning constraints such as unseen data and overfitting or physical constraints such as unknown dynamics and noise. This uncertainty is detrimental to safety-critical systems and must be properly regulated. To curb this uncertainty, we create prediction sets using the guarantees provided by Conformal Prediction. With a user-specified high probability, these prediction sets contain the true plant system states for an entire prediction horizon, which we …

Where To Build Food Banks: A Machine Learning Approach, Gavin Ruan

The Journal of Purdue Undergraduate Research

Over 44 million Americans currently suffer from food insecurity, of whom 13 million are children. Food insecurity has been shown to cause a wide range of both physical and developmental issues. Across the United States, thousands of food banks and pantries serve as vital sources of food and other forms of aid for food-insecure families. By optimizing food bank locations, food banks and their resources would become more accessible to families who desperately require it. The aim of this paper is to build a machine learning framework that is able to optimize food bank locations and to consider factors such …

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

And Climate Justice For All

DePaul Magazine

DePaul is taking its environmental sustainability and equity prowess to the next level through its Just DePaul and President's Sustainability Committee initiatives that incorporate a climate action plan and student voices. Plus, community partnership courses that involve students in environmental action and justice efforts.