Data Science Commons^™

Learning Latent Causal Dynamics, Weiran Yao, Guangyi Chen, Kun Zhang

Machine Learning Faculty Publications

One critical challenge of time-series modeling is how to learn and quickly correct the model under unknown distribution shifts. In this work, we propose a principled framework, called LiLY, to first recover time-delayed latent causal variables and identify their relations from measured temporal data under different distribution shifts. The correction step is then formulated as learning the low-dimensional change factors with a few samples from the new environment, leveraging the identified causal structure. Specifically, the framework factorizes unknown distribution shifts into transition distribution changes caused by fixed dynamics and time-varying latent causal relations, and by global changes in observation. We …

Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin

Publications

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating …

Data Analytics And Visualization Dsp 562, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Advanced Topics In Machine Learning Dsp 566, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Introduction To Statistical Computing Dsp 565, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Applications Of Data Science In Biological Science Dsp 569, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Mathematical Foundations For Data Science Ams/Dsp 563, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Advanced Database Concepts, Cloud Computing And Big Data Dsp 567, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Data Science For Business Dsp 568, Harrison Dekker

Collection Development Reports and Documents

No abstract provided.

Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore

SDSU Data Science Symposium

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …

Multiple Approaches Converge On Three Biological Subtypes Of Meningioma And Extract New Insights From Published Studies, James C Bayley, Caroline C Hadley, Arif O Harmanci, Akdes S Harmanci, Tiemo J Klisch, Akash J Patel

Faculty, Staff and Student Publications

One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational …

Development Of Guidelines For Collecting Transit Ridership Data, Hong Yang, Kun Xie, Sherif Ishak, Qingyu Ma, Yang Liu

Computational Modeling & Simulation Engineering Faculty Publications

Transit ridership is a critical determinant for many transit applications such as operation optimizations and project prioritization under performance-based funding mechanisms. As a result, the quality of ridership data is of utmost importance to both transit administrative agencies and transit operators. Many transit operators in Virginia report their ridership data to the Department of Rail and Public Transportation (DRPT) and the National Transit Database (NTD). However, with no specific guidelines available to transit agencies in Virginia for collecting ridership data, the heterogeneous mixture of diverse data collection methods and technologies has often raised concerns about the consistency and quality of …

Mental Health In The Uk Biobank: A Roadmap To Self-Report Measures And Neuroimaging Correlates, Rosie K. Dutt, Kayla Hannon, Ty O. Easley, Joseph C. Griffis, Wei Zhang, Janine D. Bijsterbosch

Statistical and Data Sciences: Faculty Publications

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in …

Verticox: Vertically Distributed Cox Proportional Hazards Model Using The Alternating Direction Method Of Multipliers, Wenrui Dai, Xiaoqian Jiang, Luca Bonomi, Yong Li, Hongkai Xiong, Lucila Ohno-Machado

Faculty, Staff and Student Publications

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the …

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

Blockchain: Key Principles, Nadezda Chikurova

Dissertations, Theses, and Capstone Projects

“Blockchain: Key Principles” is an interactive visual project that explains the importance of data privacy and security, decentralized computing, and open-source software in the modern digital world through the history of the underlying principles of blockchain technology. Some of these key concepts have their roots in the time before the Information Age. By explaining the history of these principles, I want to present the fact that over the past centuries, humanity has been fighting for their privacy, security, and the ability to efficiently express themselves one way or another. Blockchain technology, which was introduced to the public in 2008 through …

Air Pollution, Climate Change, And Our Health, Kathia Vargas Feliz

Dissertations, Theses, and Capstone Projects

Climate change is a subject that is creating a lot of controversies nowadays. From newspapers to researchers, there are big efforts going on trying to bring awareness about the effects of air pollution and climate change over time. It is recommended that governments all over the world, and people from all communities act by taking care of the environment because the situation might turn out to be irremediable. There is a quote by Leonardo Dicaprio stating, “Climate change is real. It is happening right now; it is the most urgent threat facing our entire species and we need to …

The Data Analytics And The Science Revolution, Leila Halawi, Amal Clarke, Kelly George

Publications

This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they …

Liquidity Commonality With Factor Models, Ernesto Garcia Iii

Dissertations, Theses, and Capstone Projects

Market microstructure research has recently devoted attention to a phenomenon called commonality in liquidity. In this dissertation, I will analyze commonality in liquidity using a novel factor model approach and a generalized definition of commonality in liquidity. This analysis will show that commonality in liquidity is rarely a marketwide phenomenon and is mostly restricted to stocks with a large market capitalization. Additionally, commonality in liquidity is a very recent phenomenon whose appearance coincides with a rise in passive investing after the Dotcom Bubble burst and, more so, after the 2008 Financial Crisis. I will present evidence that suggests commonality in …

Transition Metal Phosphides For High Performance Electrochemical Energy Storage Devices, Amina Saleh

Theses and Dissertations

Electrochemical energy storage technologies are nowadays playing a leading role in the global effort to address the energy challenges. A lot of attention has been devoted to designing hybrid devices known as supercapatteries which combine the merits of supercapacitors (high power density) and rechargeable batteries (high energy density). Transition metal phosphides (TMP) are a rising star for supercapattery anode materials thanks to their high conductivity, metalloid characteristics, and kinetic favorability for fast electron transport. Herein, new TMP-based materials were synthesized for use as supercapattery positive electrodes, via a multifaceted approach to yield devices enjoying concurrently high power and energy densities. …

Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher

Articles

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …

P3k14c, A Synthetic Global Database Of Archaeological Radiocarbon Dates, Erick Robinson

Anthropology Faculty Publications and Presentations

Archaeologists increasingly use large radiocarbon databases to model prehistoric human demography (also termed paleo-demography). Numerous independent projects, funded over the past decade, have assembled such databases from multiple regions of the world. These data provide unprecedented potential for comparative research on human population ecology and the evolution of social-ecological systems across the Earth. However, these databases have been developed using different sample selection criteria, which has resulted in interoperability issues for global-scale, comparative paleo-demographic research and integration with paleoclimate and paleoenvironmental data. We present a synthetic, global-scale archaeological radiocarbon database composed of 180,070 radiocarbon dates that have been cleaned according …

Machine Learning For Predicting Risk Of Early Dropout In A Recovery Program For Opioid Use Disorder, Assaf Gottlieb, Andrea Yatsco, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

BACKGROUND: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.

METHODS: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning …

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil K. Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin

LSU Master's Theses

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, …

On The Horizon: Nanosatellite Constellations Will Revolutionize The Internet Of Things (Iot), Diane Janosek

Seattle Journal of Technology, Environmental, & Innovation Law

The Internet of Things has experienced exponential growth and use across the globe with 25.1 billion devices currently in use. Until recently, the functionality of the IoT was dependent on secure data flow between internet terrestrial stations and the IoT devices. Now, a new alternative path of data flow is on the horizon.

IoT device manufacturers are now looking to outer space nanosatellite constellations to connect to a different type of internet. This new internet is no longer terrestrial with fiber cables six feet underground but now looking up, literally, 200 to 300 miles above the earth, to communicate, connect …

Sociodemographic And Clinical Characteristics Associated With Improvements In Quality Of Life For Participants With Opioid Use Disorder, Assaf Gottlieb, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant's reported quality of life (QoL) at the beginning of the program and at successive intervals.

Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL.

Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, …

Author’S Reflections On Making Sense Of Numbers: Quantitative Reasoning For Social Research, Jane E. Miller

Numeracy

Miller, Jane E. 2021. Making Sense of Numbers: Quantitative Reasoning for Social Research. (Los Angeles: SAGE Publications) 608 pp. ISBN 978-1544355597.

This article introduces and provides an excerpt from Making Sense of Numbers: Quantitative Reasoning for Social Research, published by Sage. The book explains and illustrates how making sense of numbers involves integrating concepts and skills from mathematics, statistics, study design, and communications, along with information about the specific topic and context under study. It teaches how to avoid making common errors of logic, calculation, and interpretation by introducing a systematic approach and a healthy dose of skepticism …

Opening A Window To Evolution: David Angelini’S Research On Genetic Adaptation Gets Push From Mcvey Data Science Initiative, Christina Nunez

Colby Magazine

Most people think of soapberry bugs as little more than a nuisance, if they think of them at all. Found across much of the southeastern United States, the oblong insect is harmless to humans and likes to hang out on plants native to the soapberry family, hence its straightforward name.

Lncrnafunc: A Knowledgebase Of Lncrna Function In Human Cancer, Mengyuan Yang, Huifen Lu, Jiajia Liu, Sijia Wu, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse human diseases. To date, systematic and intensive annotations on diverse interaction regulations of lncRNAs in human cancer were not available. Here, we built lncRNAfunc, a knowledgebase of lncRNA function in human cancer at https://ccsm.uth.edu/lncRNAfunc, aiming to provide a resource and reference for providing therapeutically targetable lncRNAs and intensive interaction regulations. To do this, we collected 15 900 lncRNAs across 33 cancer types from TCGA. For individual lncRNAs, we performed multiple interaction analyses of different biomolecules including DNA, RNA, and protein levels. …