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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' print collection or in Proquest's Dissertations and Theses database.
Dissertations from 2024 2024
Advancement in In-Silico Drug Discovery from Virtual Screening Molecular Dockings to De-Novo Drug Design Transformer-based Generative AI and Reinforcement Learning , Dony Ang
A Novel Correction for the Multivariate Ljung-Box Test , Minhao Huang
Medical Image Analysis Based on Graph Machine Learning and Variational Methods , Sina Mohammadi
Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena , Sachi Perera
Global to Glocal: A Confluence of Data Science and Earth Observations in the Advancement of the SDGs , Rejoice Thomas
Dissertations from 2023 2023
Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies , Mohammed Alshahrani
Integration of Computer Algebra Systems and Machine Learning in the Authoring of the SANYMS Intelligent Tutoring System , Sam Ford
Voluntary Action and Conscious Intention , Jake Gavenas
Random Variable Spaces: Mathematical Properties and an Extension to Programming Computable Functions , Mohammed Kurd-Misto
Computational Modeling of Superconductivity from the Set of Time-Dependent Ginzburg-Landau Equations for Advancements in Theory and Applications , Iris Mowgood
Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data , Krupa Nagori
Stochastic Processes and Multi-Resolution Analysis: A Trigonometric Moment Problem Approach and an Analysis of the Expenditure Trends for Diabetic Patients , Isaac Nwi-Mozu
Applications of Causal Inference Methods for the Estimation of Effects of Bone Marrow Transplant and Prescription Drugs on Survival of Aplastic Anemia Patients , Yesha M. Patel
Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis , Albert Pierce
Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients , Ehsan Yaghmaei
Dissertations from 2022 2022
Computational Approaches to Facilitate Automated Interchange between Music and Art , Rao Hamza Ali
Causal Inference in Psychology and Neuroscience: From Association to Causation , Dehua Liang
Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues , Hanna Lu
Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom , Sunil Ramchandani
Probing the Boundaries of Human Agency , Sook Mun Wong
Dissertations from 2021 2021
Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder , Raymond Anden
Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models , Alexander Barrett
Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing , Natalie Best
Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods , Elia Eiroa Lledo
Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis , Arin Ghazarian
Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking , Nicholas LaHaye
Machine-Learning-Based Approach to Decoding Physiological and Neural Signals , Elnaz Lashgari
Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions , Justin Le
Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning , Shiva Lotfallahzadeh Barzili
Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures , Chelsea Parlett
Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data , Jianwei Zheng
Dissertations from 2020 2020
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
Allocation of Public Resources: Bringing Order to Chaos , Lance Clifner
A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay , Sidy Danioko
A Computational and Experimental Examination of the FCC Incentive Auction , Logan Gantner
Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning , Kayleigh Hyde
Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations , Oluyemi Odeyemi
On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis , Ismael L. Paiva
Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability , Luciano Rodriguez
Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder , Natalia Rosenfield
Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter , Viseth Sean
Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance , Christopher Watkins
Dissertations from 2019 2019
Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data , Kyle Anderson
Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images , Justin J. Gapper
Estimating Auction Equilibria using Individual Evolutionary Learning , Kevin James
Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs , Wenzhao Li
Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique , Chloe Martin-King
Theses from 2017 2017
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods , Michael Schwartz
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Home > CCSE > Data Science and Analytics > PhD DSA
Doctor of Data Science and Analytics Dissertations
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. 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 2024 2024.
A Holistic and Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives , Martin Keagan Wynne Brown
MEDICAL IMAGING DATASET MANAGEMENT LEVERAGING DEEP LEARNING FRAMEWORKS IN BREAST CANCER SCREENING , Inchan Hwang
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap , Srivatsa Mallapragada
Innovative Approaches for Identifying and Reducing Disparity in Machine Learning Model Performance – Bridging the Gap in Binary Classification for Health Informatics , Linglin Zhang
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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This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .
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.
MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.
If you have questions about MIT theses in DSpace, [email protected] . See also Access & Availability Questions or About MIT Theses in DSpace .
If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email [email protected] with any questions.
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Theses by Department
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Collections in this community
Doctoral theses, graduate theses, undergraduate theses, recent submissions.
Transport Properties of Divertor Edge Plasmas Measured with Multi-Spectral Imaging
Entanglement and Chaos in Quantum Field Theory and Gravity
Illuminating the Cosmos: dark matter, primordial black holes, and cosmic dawn
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Machine Learning - CMU
PhD Dissertations
[all are .pdf files].
Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024
The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024
Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024
Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024
UNDERSTANDING, FORMALLY CHARACTERIZING, AND ROBUSTLY HANDLING REAL-WORLD DISTRIBUTION SHIFT Elan Rosenfeld, 2024
Representing Time: Towards Pragmatic Multivariate Time Series Modeling Cristian Ignacio Challu, 2024
Foundations of Multisensory Artificial Intelligence Paul Pu Liang, 2024
Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion Ian Char, 2024
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
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.
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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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Research Topics & Ideas: Data Science
50 Topic Ideas To Kickstart Your Research Project
If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.
PS – This is just the start…
We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.
To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .
Data Science-Related Research Topics
- Developing machine learning models for real-time fraud detection in online transactions.
- The use of big data analytics in predicting and managing urban traffic flow.
- Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
- The application of predictive analytics in personalizing cancer treatment plans.
- Analyzing consumer behavior through big data to enhance retail marketing strategies.
- The role of data science in optimizing renewable energy generation from wind farms.
- Developing natural language processing algorithms for real-time news aggregation and summarization.
- The application of big data in monitoring and predicting epidemic outbreaks.
- Investigating the use of machine learning in automating credit scoring for microfinance.
- The role of data analytics in improving patient care in telemedicine.
- Developing AI-driven models for predictive maintenance in the manufacturing industry.
- The use of big data analytics in enhancing cybersecurity threat intelligence.
- Investigating the impact of sentiment analysis on brand reputation management.
- The application of data science in optimizing logistics and supply chain operations.
- Developing deep learning techniques for image recognition in medical diagnostics.
- The role of big data in analyzing climate change impacts on agricultural productivity.
- Investigating the use of data analytics in optimizing energy consumption in smart buildings.
- The application of machine learning in detecting plagiarism in academic works.
- Analyzing social media data for trends in political opinion and electoral predictions.
- The role of big data in enhancing sports performance analytics.
- Developing data-driven strategies for effective water resource management.
- The use of big data in improving customer experience in the banking sector.
- Investigating the application of data science in fraud detection in insurance claims.
- The role of predictive analytics in financial market risk assessment.
- Developing AI models for early detection of network vulnerabilities.
Data Science Research Ideas (Continued)
- The application of big data in public transportation systems for route optimization.
- Investigating the impact of big data analytics on e-commerce recommendation systems.
- The use of data mining techniques in understanding consumer preferences in the entertainment industry.
- Developing predictive models for real estate pricing and market trends.
- The role of big data in tracking and managing environmental pollution.
- Investigating the use of data analytics in improving airline operational efficiency.
- The application of machine learning in optimizing pharmaceutical drug discovery.
- Analyzing online customer reviews to inform product development in the tech industry.
- The role of data science in crime prediction and prevention strategies.
- Developing models for analyzing financial time series data for investment strategies.
- The use of big data in assessing the impact of educational policies on student performance.
- Investigating the effectiveness of data visualization techniques in business reporting.
- The application of data analytics in human resource management and talent acquisition.
- Developing algorithms for anomaly detection in network traffic data.
- The role of machine learning in enhancing personalized online learning experiences.
- Investigating the use of big data in urban planning and smart city development.
- The application of predictive analytics in weather forecasting and disaster management.
- Analyzing consumer data to drive innovations in the automotive industry.
- The role of data science in optimizing content delivery networks for streaming services.
- Developing machine learning models for automated text classification in legal documents.
- The use of big data in tracking global supply chain disruptions.
- Investigating the application of data analytics in personalized nutrition and fitness.
- The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
- Developing predictive models for customer churn in the telecommunications industry.
- The application of data science in optimizing advertisement placement and reach.
Recent Data Science-Related Studies
While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.
Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.
- Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
- Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
- Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
- Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
- An Essay on How Data Science Can Strengthen Business (Santos, 2023)
- A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
- You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
- Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
- Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
- A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
- Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
- Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
- Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
- Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
- TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
- Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
- MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
- COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
- Analysis on the Application of Data Science in Business Analytics (Wang, 2022)
As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.
Get 1-On-1 Help
If you’re still unsure about how to find a quality research topic, check out our Private Coaching service, the perfect starting point for developing a unique, well-justified research topic.
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Open Access Theses and Dissertations
Thursday, April 18, 8:20am (EDT): Searching is temporarily offline. We apologize for the inconvenience and are working to bring searching back up as quickly as possible.
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OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1100 colleges, universities, and research institutions . OATD currently indexes 7,225,126 theses and dissertations.
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Recent Dissertation Topics
Kerstin Emily Frailey - “PRACTICAL DATA QUALITY FOR MODERN DATA & MODERN USES, WITH APPLICATIONS TO AMERICA’S COVID-19 DATA"
Dissertation Advisor: Martin Wells
Initial job placement: Co-Founder & CEO
David Kent - “Smoothness-Penalized Deconvolution: Rates of Convergence, Choice of Tuning Parameter, and Inference"
Dissertation Advisor: David Ruppert
Initial job placement: VISITING ASSISTANT PROFESSOR - Cornell University
Yuchen Xu - “Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation”
Dissertation Advisor: David Matteson
Initial job placement: Postdoctoral Fellow - UCLA
Siyi Deng - “Optimal and Safe Semi-supervised Estimation and Inference for High-dimensional Linear Regression"
Dissertation Advisor: Yang Ning
Initial job placement: Data Scientist - TikTok
Peter (Haoxuan) Wu - “Advances in adaptive and deep Bayesian state-space models”
Initial job placement: Quantitative Researcher - DRW
Grace Deng - “Generative models and Bayesian spillover graphs for dynamic networks”
Initial job placement: Data Scientist - Research at Google
Samriddha Lahiry - “Some problems of asymptotic quantum statistical inference”
Dissertation Advisor: Michael Nussbaum
Initial job placement: Postdoctoral Fellow - Harvard University
Yaosheng Xu - “WWTA load-balancing for parallel-server systems with heterogeneous servers and multi-scale heavy traffic limits for generalized Jackson networks”
Dissertation Advisor: Jim Dai
Initial job placement: Applied Scientist - Amazon
Seth Strimas-Mackey - “Latent structure in linear prediction and corpora comparison”
Dissertation Advisor: Marten Wegkamp and Florentina Bunea
Initial job placement: Data Scientist at Google
Tao Zhang - “Topics in modern regression modeling”
Dissertation Advisor: David Ruppert and Kengo Kato
Initial job placement: Quantitative Researcher - Point72
Wentian Huang - “Nonparametric and semiparametric approaches to functional data modeling”
Initial job placement: Ernst & Young
Binh Tang - “Deep probabilistic models for sequential prediction”
Initial job placement: Amazon
Yi Su - “Off-policy evaluation and learning for interactive systems"
Dissertation Advisor: Thorsten Joachims
Initial job placement: Berkeley (postdoc)
Ruqi Zhang - “Scalable and reliable inference for probabilistic modeling”
Dissertation Advisor: Christopher De Sa
Jason Sun - “Recent developments on Matrix Completion"
Initial job placement: LinkedIn
Indrayudh Ghosal - “Model combinations and the Infinitesimal Jackknife : how to refine models with boosting and quantify uncertainty”
Dissertation Advisor: Giles Hooker
Benjamin Ryan Baer - “Contributions to fairness and transparency”
Initial job placement: Rochester (postdoc)
Megan Lynne Gelsinger - “Spatial and temporal approaches to analyzing big data”
Dissertation Advisor: David Matteson and Joe Guinness
Initial job placement: Institute for Defense Analysis
Zhengze Zhou - “Statistical inference for machine learning : feature importance, uncertainty quantification and interpretation stability”
Initial job placement: Facebook
Huijie Feng - “Estimation and inference of high-dimensional individualized threshold with binary responses”
Initial job placement: Microsoft
Xiaojie Mao - “Machine learning methods for data-driven decision making : contextual optimization, causal inference, and algorithmic fairness”
Dissertation Advisor: Nathan Kallus and Madeleine Udell
Initial job placement: Tsinghua University, China
Xin Bing - “Structured latent factor models : Identifiability, estimation, inference and prediction”
Initial job placement: Cambridge (postdoc), University of Toronto
Yang Liu - “Nonparametric regression and density estimation on a network"
Dissertation Advisor: David Ruppert and Peter Frazier
Initial job placement: Research Analyst - Cubist Systematic Strategies
Skyler Seto - “Learning from less : improving and understanding model selection in penalized machine learning problems”
Initial job placement: Machine Learning Researcher - Apple
Jiekun Feng - “Markov chain, Markov decision process, and deep reinforcement learning with applications to hospital management and real-time ride-hailing”
Initial job placement:
Wenyu Zhang - “Methods for change point detection in sequential data”
Initial job placement: Research Scientist - Institute for Infocomm Research
Liao Zhu - “The adaptive multi-factor model and the financial market"
Initial job placement: Quantitative Researcher - Two Sigma
Xiaoyun Quan - “Latent Gaussian copula model for high dimensional mixed data, and its applications”
Dissertation Advisor: James Booth and Martin Wells
Praphruetpong (Ben) Athiwaratkun - "Density representations for words and hierarchical data"
Dissertation Advisor: Andrew Wilson
Initial job placement: AI Scientist - AWS AI Labs
Yiming Sun - “High dimensional data analysis with dependency and under limited memory”
Dissertation Advisor: Sumanta Basu and Madeleine Udell
Zi Ye - “Functional single index model and jensen effect"
Dissertation Advisor: Giles Hooker
Initial job placement: Data & Applied Scientist - Microsoft
Hui Fen (Sarah) Tan - “Interpretable approaches to opening up black-box models”
Dissertation Advisor: Giles Hooker and Martin Wells
Daniel E. Gilbert - “Luck, fairness and Bayesian tensor completion”
Yichen zhou - “asymptotics and interpretability of decision trees and decision tree ensemblesg”.
Initial job placement: Data Scientist - Google
Ze Jin - “Measuring statistical dependence and its applications in machine learning”
Initial job placement: Research Scientist, Facebook Integrity Ranking & ML - Facebook
Xiaohan Yan - “Statistical learning for structural patterns with trees”
Dissertation Advisor: Jacob Bien
Initial job placement: Senior Data Scientist - Microsoft
Guo Yu - “High-dimensional structured regression using convex optimization”
Dan kowal - "bayesian methods for functional and time series data".
Dissertation Advisor: David Matteson and David Ruppert
Initial job placement: assistant professor, Department of Statistics, Rice University
Keegan Kang - "Data Dependent Random Projections"
David sinclair - "model selection results for high dimensional graphical models on binary and count data with applications to fmri and genomics", liu, yanning – "statistical issues in the design and analysis of clinical trials".
Dissertation Advisor: Bruce Turnbull
Nicholson, William Bertil – "Tools for Modeling Sparse Vector Autoregressions"
Tupper, laura lindley – "topics in classification and clustering of high-dimensional data", chetelat, didier – "high-dimensional inference by unbiased risk estimation".
Initial Job Placement: Assistant Professor Universite de Montreal, Montreal, Canada
Gaynanova, Irina – "Estimation Of Sparse Low-Dimensional Linear Projections"
Dissertation Advisor: James Booth
Initial Job Placement: Assistant Professor, Texas A&M, College Station, TX
Mentch, Lucas – "Ensemble Trees and CLTS: Statistical Inference in Machine Learning"
Initial Job Placement: Assistant Professor, University of Pittsburgh, Pittsburgh, PA
Risk, Ben – "Topics in Independent Component Analysis, Likelihood Component Analysis, and Spatiotemporal Mixed Modeling"
Dissertation Advisors: David Matteson and David Ruppert
Initial Job Placement: Postdoctoral Fellow, University of North Carolina, Chapel Hill, NC
Zhao, Yue – "Contributions to the Statistical Inference for the Semiparametric Elliptical Copula Model"
Disseration Advisor: Marten Wegkamp
Initial Job Placement: Postoctoral Fellow, McGill University, Montreal, Canada
Chen, Maximillian Gene – "Dimension Reduction and Inferential Procedures for Images"
Dissertation Advisor: Martin Wells
Earls, Cecelia – Bayesian hierarchical Gaussian process models for functional data analysis
Dissertation Advisor: Giles Hooker
Initial Job Placement: Lecturer, Cornell University, Ithaca, NY
Li, James Yi-Wei – "Tensor (Multidimensional Array) Decomposition, Regression, and Software for Statistics and Machine Learning"
Initial Job Placement: Research Scientist, Yahoo Labs
Schneider, Matthew John – "Three Papers on Time Series Forecasting and Data Privacy"
Dissertation Advisor: John Abowd
Initial Job Placement: Assistant Professor, Northwestern University, Evanston, IL
Thorbergsson, Leifur – "Experimental design for partially observed Markov decision processes"
Initial Job Placement: Data Scientist, Memorial Sloan Kettering Cancer Center, New York, NY
Wan, Muting – "Model-Based Classification with Applications to High-Dimensional Data in Bioinformatics"
Initial Job Placement: Senior Associate, 1010 Data, New York, NY
Johnson, Lynn Marie – "Topics in Linear Models: Methods for Clustered, Censored Data and Two-Stage Sampling Designs"
Dissertation Advisor: Robert Strawderman
Initial Job Placement: Statistical Consultant, Cornell, Statistical Consulting Unit, Ithaca, NY
Tecuapetla Gomez, Inder Rafael – "Asymptotic Inference for Locally Stationary Processes"
Initial Job Placement: Postdoctoral Fellow, Georg-August-Universitat Gottigen, Gottigen, Germany.
Bar, Haim – "Parallel Testing, and Variable Selection -- a Mixture-Model Approach with Applications in Biostatistics"
Dissertation Advisor: James Booth
Initial Job Placement: Postdoc, Department of Medicine, Weill Medical Center, New York, NY
Cunningham, Caitlin – "Markov Methods for Identifying ChIP-seq Peaks"
Initial Job Placement: Assistant Professor, Le Moyne College, Syracuse, NY
Ji, Pengsheng – "Selected Topics in Nonparametric Testing and Variable Selection for High Dimensional Data"
Dissertation Advisor: Michael Nussbaum
Initial Job Placement: Assistant Professor, University of Georgia, Athens, GA
Morris, Darcy Steeg – "Methods for Multivariate Longitudinal Count and Duration Models with Applications in Economics"
Dissertation Advisor: Francesca Molinari
Initial Job Placement: Research Mathematical Statistician, Center for Statistical Research and Methodology, U.S. Census Bureau, Washington DC
Narayanan, Rajendran – "Shrinkage Estimation for Penalised Regression, Loss Estimation and Topics on Largest Eigenvalue Distributions"
Initial Job Placement: Visiting Scientist, Indian Statistical Institute, Kolkata, India
Xiao, Luo – "Topics in Bivariate Spline Smoothing"
Dissertation Advisor: David Ruppert
Initial Job Placement: Postdoc, Johns Hopkins University, Baltimore, MD
Zeber, David – "Extremal Properties of Markov Chains and the Conditional Extreme Value Model"
Dissertation Advisor: Sidney Resnick
Initial Job Placement: Data Analyst, Mozilla, San Francisco, CA
Clement, David – "Estimating equation methods for longitudinal and survival data"
Dissertation Advisor: Robert Strawderman
Initial Job Placement: Quantitative Analyst, Smartodds, London UK
Eilertson, Kirsten – "Estimation and inference of random effect models with applications to population genetics and proteomics"
Dissertation Advisor: Carlos Bustamante
Initial Job Placement: Biostatistician, The J. David Gladstone Institutes, San Francisco CA
Grabchak, Michael – "Tempered stable distributions: properties and extensions"
Dissertation Advisor: Gennady Samorodnitsky
Initial Job Placement: Assistant Professor, UNC Charlotte, Charlotte NC
Li, Yingxing – "Aspects of penalized splines"
Initial Job Placement: Assistant Professor, The Wang Yanan Institute for Studies in Economics, Xiamen University
Lopez Oliveros, Luis – "Modeling end-user behavior in data networks"
Dissertation Advisor: Sidney Resnick
Initial Job Placement: Consultant, Murex North America, New York NY
Ma, Xin – "Statistical Methods for Genome Variant Calling and Population Genetic Inference from Next-Generation Sequencing Data"
Initial Job Placement: Postdoc, Stanford University, Stanford CA
Kormaksson, Matthias – "Dynamic path analysis and model based clustering of microarray data"
Dissertation Advisor: James Booth
Initial Job Placement: Postdoc, Department of Public Health, Weill Cornell Medical College, New York NY
Schifano, Elizabeth – "Topics in penalized estimation"
Initial Job Placement: Postdoc, Department of Biostatistics, Harvard University, Boston MA
Hanlon, Bret – "High-dimensional data analysis"
Dissertation Advisor: Anand Vidyashankar
Shaby, Benjamin – "Tools for hard bayesian computations"
Initial Job Placement: Postdoc, SAMSI, Durham NC
Zipunnikov, Vadim – "Topics on generalized linear mixed models"
Initial Job Placement: Postdoc, Department of Biostatistics, Johns Hopkins University, Baltimore MD
Barger, Kathryn Jo-Anne – "Objective bayesian estimation for the number of classes in a population using Jeffreys and reference priors"
Dissertation Advisor: John Bunge
Initial Job Placement: Pfizer Incorporated
Chan, Serena Suewei – "Robust and efficient inference for linear mixed models using skew-normal distributions"
Initial Job Placement: Statistician, Takeda Pharmaceuticles, Deerfield IL
Lin, Haizhi – "Distressed debt prices and recovery rate estimation"
Dissertation Advisor: Martin Wells
Initial Job Placement: Associate, Fixed Income Department, Credit Suisse Securities (USA), New York, NY
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EBSCO Open Dissertations
EBSCO Open Dissertations makes electronic theses and dissertations (ETDs) more accessible to researchers worldwide. The free portal is designed to benefit universities and their students and make ETDs more discoverable.
Increasing Discovery & Usage of ETD Research
With EBSCO Open Dissertations, institutions are offered an innovative approach to driving additional traffic to ETDs in institutional repositories. Our goal is to help make their students’ theses and dissertations as widely visible and cited as possible.
EBSCO Open Dissertations extends the work started in 2014, when EBSCO and the H.W. Wilson Foundation created American Doctoral Dissertations which contained indexing from the H.W. Wilson print publication, Doctoral Dissertations Accepted by American Universities, 1933-1955. In 2015, the H.W. Wilson Foundation agreed to support the expansion of the scope of the American Doctoral Dissertations database to include records for dissertations and theses from 1955 to the present.
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Libraries can add theses and dissertations to the database, making them freely available to researchers everywhere while increasing traffic to their institutional repository. ETD metadata is harvested via OAI and integrated into EBSCO’s platform, where pointers send traffic to the institution's IR.
EBSCO integrates this data into their current subscriber environments and makes the data available on the open web via opendissertations.org .
VIDEO
COMMENTS
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 ...
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. Tengyu Ma Approved for the Stanford University Committee on Graduate Studies. Stacey F. Bent, Vice Provost for Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format.
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 ...
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
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
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.
University of Washington
The central goal of this thesis is to bridge the divide between theoretical linguistics—the scien-tific inquiry of language—and applied data-driven statistical language processing, to provide deeper insight into data and to build more powerful, robust models. To corroborate the practi-
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF DISSERTATION APPROVAL Dr. Shiaofen Fang, Chair Department of Computer and Information Science Dr. Li Shen Department of Computer and Information Science Dr. Snehasis Mukhopadhyay Department of Computer and Information Science Approved by: Dr. Shiaofen Fang Department of Computer and Information ...
PhD Dissertations [All are .pdf files] Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024 The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024. Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024. Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024
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.
Abstract. To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning ...
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.
View Details. 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 ...
Open Access Theses and Dissertations. Database of free, open access full-text graduate theses and dissertations published around the world. Direct Link. University of Southern California. 3550 Trousdale Parkway. Los Angeles, CA 90089. Database of free, open access full-text graduate theses and dissertations published around the world.
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.
Data Science-Related Research Topics. Developing machine learning models for real-time fraud detection in online transactions. The use of big data analytics in predicting and managing urban traffic flow. Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
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 ...
2019. - "Density representations for words and hierarchical data". - "High dimensional data analysis with dependency and under limited memory". Dissertation Advisor: Sumanta Basu and Madeleine Udell. - "Functional single index model and jensen effect". - "Interpretable approaches to opening up black-box models".
EBSCO Open Dissertations makes electronic theses and dissertations (ETDs) more accessible to researchers worldwide. The free portal is designed to benefit universities and their students and make ETDs more discoverable. Content Includes: 1,500,000 electronic theses and dissertations. 320 worldwide universities that have loaded their ...
n Parker, Deborah LucasAbstract:This thesis consists of three essays that theoretically and empirically investigate the asset pricing and macroeconomic implications of uncertainty shocks, propose new measures for model robustness, explain the joint dynamics on equity exces.
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