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Doctor of Data Science and Analytics Dissertations
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The Ph.D. in Data Science and Analytics is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests.
We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection of computer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community. -Sherry Ni, Director, Ph.D. in Data Science and Analytics
This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.
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Dissertations from 2023 2023.
Quantification of Various Types of Biases in Large Language Models , Sudhashree Sayenju
Dissertations from 2022 2022
Appley: Approximate Shapley Values for Model Explainability in Linear Time , Md Shafiul Alam
Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics , Jonathan Boardman
Novel Instance-Level Weighted Loss Function for Imbalanced Learning , Trent Geisler
Debiasing Cyber Incidents – Correcting for Reporting Delays and Under-reporting , Seema Sangari
Dissertations from 2021 2021
Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset , Mohammad Masum
A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in Episodes of Care Healthcare Delivery System , Lauren Staples
Dissertations from 2020 2020
A CREDIT ANALYSIS OF THE UNBANKED AND UNDERBANKED: AN ARGUMENT FOR ALTERNATIVE DATA , Edwin Baidoo
Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies , Jessica M. Rudd
Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring , Yan Wang
A Novel Penalized Log-likelihood Function for Class Imbalance Problem , Lili Zhang
ATTACK AND DEFENSE IN SECURITY ANALYTICS , Yiyun Zhou
Dissertations from 2019 2019
One and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles , Bogdan Gadidov
Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis , Jie Hao
Deep Embedding Kernel , Linh Le
Ordinal HyperPlane Loss , Bob Vanderheyden
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Machine Learning - CMU
PhD Dissertations
[all are .pdf files].
Learning Models that Match Jacob Tyo, 2024
Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024
Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023
Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023
Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023
Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023
Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023
The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023
Collaborative learning by leveraging siloed data Sebastian Caldas, 2023
Modeling Epidemiological Time Series Aaron Rumack, 2023
Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023
Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023
Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023
Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023
Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023
Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023
Applied Mathematics of the Future Kin G. Olivares, 2023
METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023
NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023
Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023
Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023
Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022
Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022
Making Scientific Peer Review Scientific Ivan Stelmakh, 2022
Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022
Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022
Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022
Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022
Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022
Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022
Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022
Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021
Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021
Structure and time course of neural population activity during learning Jay Hennig, 2021
Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021
Meta Reinforcement Learning through Memory Emilio Parisotto, 2021
Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021
Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021
Statistical Game Theory Arun Sai Suggala, 2021
Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021
Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021
Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021
Curriculum Learning Otilia Stretcu, 2021
Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021
Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021
Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021
Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021
Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020
Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020
Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020
Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020
Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020
Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020
Learning DAGs with Continuous Optimization Xun Zheng, 2020
Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020
Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020
Towards Data-Efficient Machine Learning Qizhe Xie, 2020
Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020
Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020
Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020
Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020
Towards Efficient Automated Machine Learning Liam Li, 2020
LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020
Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020
Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020
Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020
Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019
Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019
Estimating Probability Distributions and their Properties Shashank Singh, 2019
Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019
Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019
Multi-view Relationships for Analytics and Inference Eric Lei, 2019
Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019
Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019
The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019
Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019
Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019
Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019
Unified Models for Dynamical Systems Carlton Downey, 2019
Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019
Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019
Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019
New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019
Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019
Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019
Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019
Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018
Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018
Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018
Statistical Inference for Geometric Data Jisu Kim, 2018
Representation Learning @ Scale Manzil Zaheer, 2018
Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018
Distribution and Histogram (DIsH) Learning Junier Oliva, 2018
Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018
Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018
Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018
Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018
Learning with Staleness Wei Dai, 2018
Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017
New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017
Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017
New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017
Active Search with Complex Actions and Rewards Yifei Ma, 2017
Why Machine Learning Works George D. Montañez , 2017
Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017
Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016
Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016
Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016
Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016
Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016
Combining Neural Population Recordings: Theory and Application William Bishop, 2015
Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015
Machine Learning in Space and Time Seth R. Flaxman, 2015
The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015
Shape-Constrained Estimation in High Dimensions Min Xu, 2015
Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015
Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015
Learning Statistical Features of Scene Images Wooyoung Lee, 2014
Towards Scalable Analysis of Images and Videos Bin Zhao, 2014
Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014
Modeling Large Social Networks in Context Qirong Ho, 2014
Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013
On Learning from Collective Data Liang Xiong, 2013
Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013
Mathematical Theories of Interaction with Oracles Liu Yang, 2013
Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013
Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013
Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013
Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013
Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013
GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013
Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013
Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013
New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)
Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012
Spectral Approaches to Learning Predictive Representations Byron Boots, 2012
Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012
Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012
Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012
Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012
Target Sequence Clustering Benjamin Shih, 2011
Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)
Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010
Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010
Rare Category Analysis Jingrui He, 2010
Coupled Semi-Supervised Learning Andrew Carlson, 2010
Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009
Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009
Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009
Theoretical Foundations of Active Learning Steve Hanneke, 2009
Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009
Detecting Patterns of Anomalies Kaustav Das, 2009
Dynamics of Large Networks Jurij Leskovec, 2008
Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008
Stacked Graphical Learning Zhenzhen Kou, 2007
Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007
Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007
Scalable Graphical Models for Social Networks Anna Goldenberg, 2007
Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007
Tools for Graph Mining Deepayan Chakrabarti, 2005
Automatic Discovery of Latent Variable Models Ricardo Silva, 2005
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10 Compelling Machine Learning Ph.D. Dissertations for 2020
Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 19, 2020 Daniel Gutierrez, ODSC
As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Their dissertations are highly focused on a specific problem. If you can find a dissertation that aligns with your areas of interest, consuming the research is an excellent way to do a deep dive into the technology. After reviewing hundreds of recent theses from universities all over the country, I present 10 machine learning dissertations that I found compelling in terms of my own areas of interest.
[Related article: Introduction to Bayesian Deep Learning ]
I hope you’ll find several that match your own fields of inquiry. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!
1. Bayesian Modeling and Variable Selection for Complex Data
As we routinely encounter high-throughput data sets in complex biological and environmental research, developing novel models and methods for variable selection has received widespread attention. This dissertation addresses a few key challenges in Bayesian modeling and variable selection for high-dimensional data with complex spatial structures.
2. Topics in Statistical Learning with a Focus on Large Scale Data
Big data vary in shape and call for different approaches. One type of big data is the tall data, i.e., a very large number of samples but not too many features. This dissertation describes a general communication-efficient algorithm for distributed statistical learning on this type of big data. The algorithm distributes the samples uniformly to multiple machines, and uses a common reference data to improve the performance of local estimates. The algorithm enables potentially much faster analysis, at a small cost to statistical performance.
Another type of big data is the wide data, i.e., too many features but a limited number of samples. It is also called high-dimensional data, to which many classical statistical methods are not applicable.
This dissertation discusses a method of dimensionality reduction for high-dimensional classification. The method partitions features into independent communities and splits the original classification problem into separate smaller ones. It enables parallel computing and produces more interpretable results.
3. Sets as Measures: Optimization and Machine Learning
The purpose of this machine learning dissertation is to address the following simple question:
How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?
Optimization and machine learning have proved remarkably successful in applications requiring the choice of single vectors. Some tasks, in particular many inverse problems, call for the design, or estimation, of sets of objects. When the size of these sets is a priori unknown, directly applying optimization or machine learning techniques designed for single vectors appears difficult. The work in this dissertation shows that a very old idea for transforming sets into elements of a vector space (namely, a space of measures), a common trick in theoretical analysis, generates effective practical algorithms.
4. A Geometric Perspective on Some Topics in Statistical Learning
Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this dissertation is to develop a fundamental understanding of statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms. The treatment of these different problems shares the common theme of emphasizing the underlying geometric structure.
5. Essays on Random Forest Ensembles
A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. The first part of this dissertation demonstrates that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. The work explores the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. The second part of this dissertation places a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. The work then analyzes the parameters that control the bandwidth of this kernel and discuss useful generalizations.
6. Marginally Interpretable Generalized Linear Mixed Models
A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a model leads to parameter estimates that must be interpreted conditional on the latent variables. In many cases, interest lies not in these conditional parameters, but rather in marginal parameters that summarize the average effect of the predictors across the entire population. Due to the structure of the generalized linear mixed model, the average effect across all individuals in a population is generally not the same as the effect for an average individual. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of an approximation. Another popular approach in this setting is to fit a marginal model using generalized estimating equations. This strategy is effective for estimating marginal parameters, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. Thus, there exists a need for a better approach.
This dissertation defines a class of marginally interpretable generalized linear mixed models that leads to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distinguishing feature of these models is an additive adjustment that accounts for the curvature of the link function and thereby preserves a specific form for the marginal mean after integrating out the latent random variables.
7. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media
The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, the work outlines the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, the work suggests that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized “Other” and 2) the extent to which the language is explicit versus implicit. The work then uses knowledge gained from this typology to tackle the “problem of offensive language” in hate speech detection.
8. Lasso Guarantees for Dependent Data
Serially correlated high dimensional data are prevalent in the big data era. In order to predict and learn the complex relationship among the multiple time series, high dimensional modeling has gained importance in various fields such as control theory, statistics, economics, finance, genetics and neuroscience. This dissertation studies a number of high dimensional statistical problems involving different classes of mixing processes.
9. Random forest robustness, variable importance, and tree aggregation
Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex data sets. In addition to making predictions, random forests can be used to assess the relative importance of feature variables. This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness.
10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery
This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution.
I hope you enjoyed learning about these compelling machine learning dissertations.
Editor’s note: Interested in more data science research? Check out the Research Frontiers track at ODSC Europe this September 17-19 or the ODSC West Research Frontiers track this October 27-30.
Daniel Gutierrez, ODSC
Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.
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Home > Dissertations and Theses > Computational and Data Sciences (PhD) Dissertations > 17
Computational and Data Sciences (PhD) Dissertations
Development of integrated machine learning and data science approaches for the prediction of cancer mutation and autonomous drug discovery of anti-cancer therapeutic agents.
Steven Agajanian , Chapman University Follow
Date of Award
Fall 1-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Computational and Data Sciences
First Advisor
Gennady Verkhivker
Second Advisor
Hesham El-Askary
Third Advisor
Erik Linstead
Fourth Advisor
Cyril Rakovski
Few technological ideas have captivated the minds of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and biological phenomena. In this dissertation, we capitalize on these achievements and use machine learning to study drug receptor sites and design drugs to target these sites. First, we analyze the significance of various single nucleotide variations and assess their rate of contribution to cancer. Following that, we used a portfolio of machine learning and data science approaches to design new drugs to target protein kinase inhibitors. We show that these techniques exhibit strong promise in aiding cancer research and drug discovery.
Creative Commons License
Recommended Citation
S. Agajanian, "Development of integrated machine learning and data science approaches for the prediction of cancer mutation and autonomous drug discovery of anti-cancer therapeutic agents," Ph.D. dissertation, Chapman University, Orange, CA, 2021. https://doi.org/10.36837/chapman.000220
Since January 20, 2021
https://doi.org/10.36837/chapman.000220
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Home > FACULTIES > Computer Science > CSD-ETD
Computer Science Theses and Dissertations
This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository
Theses/Dissertations from 2024 2024
A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani
Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy
Theses/Dissertations from 2023 2023
Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar
Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian
Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh
INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee
Algorithms and Software for Oligonucleotide Design , Qin Dong
Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani
De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han
Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley
A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He
A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani
Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu
Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li
Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li
DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li
Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai
Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad
The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi
Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam
Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi
Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat
Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul
On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi
Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza
Migration in Edge Computing , Arshin Rezazadeh
A Modified Hopfield Network for the K-Median Problem , Cody Rossiter
Predicting Network Failures with AI Techniques , Chandrika Saha
Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha
An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa
Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia
Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia
IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales
A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad
Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim
A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba
Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong
Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang
Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar
Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang
Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi
Theses/Dissertations from 2022 2022
The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt
Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy
Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez
Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu
Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte
Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell
Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch
Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh
Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost
Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie
Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka
Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling
Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan
Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti
Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida
False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito
Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford
An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson
Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem
A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid
Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma
Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi
Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis
Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg
Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang
Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang
Predicting and Modifying Memorability of Images , Mohammad Younesi
Theses/Dissertations from 2021 2021
Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed
A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar
Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani
Contrastive Learning of Auditory Representations , Haider Al-Tahan
Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi
Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak
Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman
Improving Reader Motivation with Machine Learning , Tanner A. Bohn
A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang
Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee
A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen
Exploratory Search with Archetype-based Language Models , Brent D. Davis
Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo
Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon
A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du
Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi
Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan
A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar
Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami
Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara
Parallel Arbitrary-precision Integer Arithmetic , Davood Mohajerani
A Technique for Evaluating the Health Status of a Software Module Using Process Metrics , . Ria
Visual Analytics for Performing Complex Tasks with Electronic Health Records , Neda Rostamzadeh
Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems , Mohsen Shirpour
A Generative-Discriminative Approach to Human Brain Mapping , Deepanshu Wadhwa
WesternAccelerator:Rapid Development of Microservices , Haoran Wei
A Lightweight and Explainable Citation Recommendation System , Juncheng Yin
Mitosis Detection from Pathology Images , Jinhang Zhang
Theses/Dissertations from 2020 2020
Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury , Sheikh S. Abdullah
Towards the Development of Network Service Cost Modeling-An ISP Perspective , Yasmeen Ali
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Harvard University Theses, Dissertations, and Prize Papers
The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.
Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.
Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).
Other highlights include:
- The collection of Mathematical theses, 1782-1839
- The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871
- Ph.D. dissertations of astronomer Cecilia Payne-Gaposchkin (Ph.D. 1925) and physicist John Hasbrouck Van Vleck (Ph.D. 1922)
- Undergraduate honors theses of novelist John Updike (A.B. 1954), filmmaker Terrence Malick (A.B. 1966), and U.S. poet laureate Tracy Smith (A.B. 1994)
- Undergraduate prize papers and dissertations of philosophers Ralph Waldo Emerson (A.B. 1821), George Santayana (Ph.D. 1889), and W.V. Quine (Ph.D. 1932)
- Undergraduate honors theses of U.S. President John F. Kennedy (A.B. 1940) and Chief Justice John Roberts (A.B. 1976)
What does a prize-winning thesis look like?
If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.
Accessing These Materials
How to access materials at the Harvard University Archives
How to find and request dissertations, in person or virtually
How to find and request undergraduate honors theses
How to find and request Thomas Temple Hoopes Prize papers
How to find and request Bowdoin Prize papers
- email: Email
- Phone number 617-495-2461
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Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.
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Computational and Data Sciences (PhD) Dissertations. Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries ...
High-dimensional data are now widely collected in many areas to make scienti c discoveries or build complicated predictive models. The high dimensionality of such data requires analyses to have greater exibility in modeling while ensuring the re-producibility of discoveries. This thesis contains three self-contained chapters that
The PhD Website. The Ph.D. in Data Science and Analytics is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests. We launched the first formal PhD program in Data Science in 2015.
in scope and quality as a dissertation for the degree of Doctor of Philosophy. Tatsunori Hashimoto I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Tengyu Ma Approved for the Stanford University Committee on Graduate Studies.
PhD Dissertations [All are .pdf files] Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023. Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023. METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023. Applied Mathematics of the Future Kin G. Olivares, 2023
THE EVOLUTION OF BIG DATA AND ITS BUSINESS APPLICATIONS Marwah Ahmed Halwani Dissertation Prepared for the Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS May 2018 . Halwani, Marwah Ahmed. ... professionals will be prepared in data science programs, to aid in the entire process of preparing
scope and quality as a dissertation for the degree of Doctor of Philosophy. (Stephen P. Boyd) I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Emmanuel J. Candes) Approved for the Stanford University Committee on Graduate ...
This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes.
University of Washington
Human brain data including structural-MRI, function-MRI and di usion MRI [1] hold great promise for a systematic characterization of human brain connectivity and its relationship with cognition and behavior. This study mainly focus on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis ...
MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.
Thesis Title: Investigating the Impact of Big Data Analytics on Supply Chain Operations: Case Studies from the UK Private Sector A thesis submitted for the degree of Doctor of Philosophy By Ruaa Hasan Brunel Business School Brunel University London 2021 . 2 | P a g e
PhD Theses. 2023 Kristo, Ani Engineering a high-performing, learning-enhanced sorting algorithm (3.9 MB) • Tim Kraska, advisor ... Bounds and Applications of Concentration of Measure in Fair Machine Learning and Data Science (7.0 MB) • Eli Upfal, advisor Dursun, Kayhan Query Processing for Data Analytics on Modern Multicore Systems (2.7 MB)
This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness. 10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery. This dissertation solves two important problems in the modern analysis of big climate data.
Few technological ideas have captivated the minds of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and biological phenomena. In this dissertation, we ...
educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools. The goal of this option is not to educate all students in the foundations of data science but rather to provide advanced education to the students who will push the state-of-the-art in data science methods in their domain.
You may also want to consult these sites to search for other theses: Google Scholar; NDLTD, the Networked Digital Library of Theses and Dissertations.NDLTD provides information and a search engine for electronic theses and dissertations (ETDs), whether they are open access or not. Proquest Theses and Dissertations (PQDT), a database of dissertations and theses, whether they were published ...
Doctoral Theses. Theses by Department. Computational and Systems Biology; ... Institute for Data, Systems, and Society; Media Arts & Sciences; Operations Research Center ... and Planetary Sciences. (654) Electrical Engineering and Computer Science (572) Aeronautics and Astronautics. (552)... View More Date Issued 2000 - 2024 (13822) 1910 - 1999 ...
Theses/Dissertations from 2022. PDF. The Design and Implementation of a High-Performance Polynomial System Solver, Alexander Brandt. PDF. Defining Service Level Agreements in Serverless Computing, Mohamed Elsakhawy. PDF. Algorithms for Regular Chains of Dimension One, Juan P. Gonzalez Trochez. PDF.
The Harvard University Archives' collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University's history.. Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research ...
This is for PhD students working on a qualitative thesis who have completed their data collection and analysis and are at the stage of writing up. The materials should also be useful if you are writing up a 'mixed-methods' thesis, including chapters of analysis and discussion of qualitative data.
Writing a Doctoral Thesis or Dissertation in the Social Sciences Anne Jordan, Ph.D. Ontario Institute for Studies in Education University of Toronto ©2020 A guide for doctoral students at various stages of their doctoral theses and dissertations: Designing their thesis proposals, developing their research
School of Arts & Aesthetics 1 3 School of Biotechnology 4 22 School of Computational and Integrative Sciences 23 24 School of Computer and Systems Sciences 25 41 School of Environmental Sciences 42 94 School of Information Technology 95 96 School of International Studies 97 309.
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