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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
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap , Srivatsa Mallapragada
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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Data science masters theses.
The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. This collection contains a selection of masters theses or capstone projects by MSDS graduates.
Collection Details
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Machine Learning - CMU
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PhD Dissertations
[all are .pdf files].
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
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Thesis/Capstone for Master's in Data Science | Northwestern SPS - Northwestern School of Professional Studies
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Data Science
Capstone and thesis overview.
Capstone and thesis are similar in that they both represent a culminating, scholarly effort of high quality. Both should clearly state a problem or issue to be addressed. Both will allow students to complete a larger project and produce a product or publication that can be highlighted on their resumes. Students should consider the factors below when deciding whether a capstone or thesis may be more appropriate to pursue.
A capstone is a practical or real-world project that can emphasize preparation for professional practice. A capstone is more appropriate if:
- you don't necessarily need or want the experience of the research process or writing a big publication
- you want more input on your project, from fellow students and instructors
- you want more structure to your project, including assignment deadlines and due dates
- you want to complete the project or graduate in a timely manner
A student can enroll in MSDS 498 Capstone in any term. However, capstone specialization courses can provide a unique student experience and may be offered only twice a year.
A thesis is an academic-focused research project with broader applicability. A thesis is more appropriate if:
- you want to get a PhD or other advanced degree and want the experience of the research process and writing for publication
- you want to work individually with a specific faculty member who serves as your thesis adviser
- you are more self-directed, are good at managing your own projects with very little supervision, and have a clear direction for your work
- you have a project that requires more time to pursue
Students can enroll in MSDS 590 Thesis as long as there is an approved thesis project proposal, identified thesis adviser, and all other required documentation at least two weeks before the start of any term.
From Faculty Director, Thomas W. Miller, PhD
![data science thesis pdf Tom Miller](https://sps.northwestern.edu/include/images/550x310/tom-miller_550x310.jpg)
Capstone projects and thesis research give students a chance to study topics of special interest to them. Students can highlight analytical skills developed in the program. Work on capstone and thesis research projects often leads to publications that students can highlight on their resumes.”
A thesis is an individual research project that usually takes two to four terms to complete. Capstone course sections, on the other hand, represent a one-term commitment.
Students need to evaluate their options prior to choosing a capstone course section because capstones vary widely from one instructor to the next. There are both general and specialization-focused capstone sections. Some capstone sections offer in individual research projects, others offer team research projects, and a few give students a choice of individual or team projects.
Students should refer to the SPS Graduate Student Handbook for more information regarding registration for either MSDS 590 Thesis or MSDS 498 Capstone.
Capstone Experience
If students wish to engage with an outside organization to work on a project for capstone, they can refer to this checklist and lessons learned for some helpful tips.
Capstone Checklist
- Start early — set aside a minimum of one to two months prior to the capstone quarter to determine the industry and modeling interests.
- Networking — pitch your idea to potential organizations for projects and focus on the business benefits you can provide.
- Permission request — make sure your final project can be shared with others in the course and the information can be made public.
- Engagement — engage with the capstone professor prior to and immediately after getting the dataset to ensure appropriate scope for the 10 weeks.
- Teambuilding — recruit team members who have similar interests for the type of project during the first week of the course.
Capstone Lesson Learned
- Access to company data can take longer than expected; not having this access before or at the start of the term can severely delay the progress
- Project timeline should align with coursework timeline as closely as possible
- One point of contact (POC) for business facing to ensure streamlined messages and more effective time management with the organization
- Expectation management on both sides: (business) this is pro-bono (students) this does not guarantee internship or job opportunities
- Data security/masking not executed in time can risk the opportunity completely
Publication of Work
Northwestern University Libraries offers an option for students to publish their master’s thesis or capstone in Arch, Northwestern’s open access research and data repository.
Benefits for publishing your thesis:
- Your work will be indexed by search engines and discoverable by researchers around the world, extending your work’s impact beyond Northwestern
- Your work will be assigned a Digital Object Identifier (DOI) to ensure perpetual online access and to facilitate scholarly citation
- Your work will help accelerate discovery and increase knowledge in your subject domain by adding to the global corpus of public scholarly information
Get started:
- Visit Arch online
- Log in with your NetID
- Describe your thesis: title, author, date, keywords, rights, license, subject, etc.
- Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.)
- Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release)
- Save your work to the repository
Your thesis manuscript or capstone report will then be published on the MSDS page. You can view other published work here .
For questions or support in publishing your thesis or capstone, please contact [email protected] .
- DSpace@MIT Home
- MIT Libraries
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.
Permissions
MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.
Theses by Department
- Comparative Media Studies
- Computation for Design and Optimization
- Computational and Systems Biology
- Department of Aeronautics and Astronautics
- Department of Architecture
- Department of Biological Engineering
- Department of Biology
- Department of Brain and Cognitive Sciences
- Department of Chemical Engineering
- Department of Chemistry
- Department of Civil and Environmental Engineering
- Department of Earth, Atmospheric, and Planetary Sciences
- Department of Economics
- Department of Electrical Engineering and Computer Sciences
- Department of Humanities
- Department of Linguistics and Philosophy
- Department of Materials Science and Engineering
- Department of Mathematics
- Department of Mechanical Engineering
- Department of Nuclear Science and Engineering
- Department of Ocean Engineering
- Department of Physics
- Department of Political Science
- Department of Urban Studies and Planning
- Engineering Systems Division
- Harvard-MIT Program of Health Sciences and Technology
- Institute for Data, Systems, and Society
- Media Arts & Sciences
- Operations Research Center
- Program in Real Estate Development
- Program in Writing and Humanistic Studies
- Science, Technology & Society
- Science Writing
- Sloan School of Management
- Supply Chain Management
- System Design & Management
- Technology and Policy Program
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An Approach to Fault Management Design for the Proposed Mars Sample Return EDL and Ascent Phase Architectures
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Silicon Photomultipliers as Free Space Optical Communication Sensors
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Study of Cavity Geometry to Improve Optical Quality of Windows in Hypersonic Flow
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The Art and Science of Data Analysis
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Description
This thesis aims to utilize data analysis and predictive modeling techniques and apply them in different domains for gaining insights. The topics were chosen keeping the same in mind. Analysis of customer interests is a crucial factor in present marketing trends and hence we worked on twitter data which is a significant part of digital marketing. Neuroscience, especially psychological behavior, is an important research area. We chose eye tracking data based on which we differentiated human concentration while watching controllable (video game) videos and uncontrollable (sports) videos. Currently, cities are using data analysis for becoming smart cities. We worked on … continued below
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Physical Description
vii, 31 pages
Creation Information
Daita, Ananda Rohit May 2018.
This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by the UNT Libraries to the UNT Digital Library , a digital repository hosted by the UNT Libraries . It has been viewed 2380 times, with 8 in the last month. More information about this thesis can be viewed below.
People and organizations associated with either the creation of this thesis or its content.
- Daita, Ananda Rohit
- Namuduri, Kamesh Major Professor
Committee Members
- Guturu, Parthasarathy
- University of North Texas Publisher Info: www.unt.edu Place of Publication: Denton, Texas
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Degree Information
- Name: Master of Science
- Level: Master's
- Department: Department of Electrical Engineering
- College: College of Engineering
- Discipline: Electrical Engineering
- PublicationType: Master's Thesis
- Grantor: University of North Texas
This thesis aims to utilize data analysis and predictive modeling techniques and apply them in different domains for gaining insights. The topics were chosen keeping the same in mind. Analysis of customer interests is a crucial factor in present marketing trends and hence we worked on twitter data which is a significant part of digital marketing. Neuroscience, especially psychological behavior, is an important research area. We chose eye tracking data based on which we differentiated human concentration while watching controllable (video game) videos and uncontrollable (sports) videos. Currently, cities are using data analysis for becoming smart cities. We worked on the City of Lewisville emergency services data and predicted the vehicle-accident-prone areas for development of precautionary measures in those areas.
- data analysis
- digital marketing
- eye tracking
- smart cities
Library of Congress Subject Headings
- Eye tracking.
- Internet marketing.
- Quantitative research.
- Smart cities.
- Thesis or Dissertation
Unique identifying numbers for this thesis in the Digital Library or other systems.
- Accession or Local Control No : submission_1135
- Archival Resource Key : ark:/67531/metadc1157624
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UNT Theses and Dissertations
Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community .
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- June 6, 2018, 1:19 p.m.
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Daita, Ananda Rohit. The Art and Science of Data Analysis , thesis , May 2018; Denton, Texas . ( https://digital.library.unt.edu/ark:/67531/metadc1157624/ : accessed July 3, 2024 ), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu ; .
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BSc/MSc Thesis
Our research group offers various interesting topics for a BSc or MSc thesis, the latter both in Computer Science and Scientific Computing . These topics are typically closely related to ongoing research projects (see our Research Page and Publications ). Below, we outline the basic procedure you should follow when planning to do a thesis in our group. Please read the following carefully! You also might want to take a quick look at past topics students covered in their theses. Please also note that we currently cannot accommodate all requests for advising a thesis as in current semester as well as in the upcoming summer semester 2024 we are already advising numerous MSc and BSc theses.
Requirements
A key requirement is that you have taken some advanced courses offered by our group. This includes Data Science for Text Analytics or Complex Network Analysis (ICNA) and the more recent master level class on Natural Language Processing with Transformers (INLPT). Student should also have some background in machine learning, ideally in combination with NLP. We also strongly recommend that prior to starting a thesis (especially a BSc thesis) in our group, you do an advanced software practical to become familiar with the data and tools we use in many of our projects. Most students typically do this in the semester before they officially start their thesis. Further requirements include
- very good programming experience with Python (strongly preferred, including framework like pandas and numpy)
- solid background in statistics and linear algebra
- (optionally) experience with the machine learning frameworks such as PyTorch
- (optionally) experience with NLP frameworks such as spaCy, gensim, LangChain
- (optionally) experience with Opensearch or Elasticsearch
- knowledge using tools such as Github and Docker
It is also advantageous if you have taken some graduate courses in the areas of efficient algorithms (e.g., IEA1 ) and in particular machine learning (e.g., IML , IFML or IAI ). Being familiar with frameworks like scikit-learn , Keras or PyTorch is advantageous.
If you have only taken the undergraduate course introduction to databases (IDB) and none of the other above courses, it is unlikely that we can accommodate your request.
Make also sure that you are familiar with the examination regulations ("Prüfungsordnung") that apply to your program of study.
Getting in Contact
Prior to getting in contact with us you should, of course, read this page in its entirety. If you think your interests and expertise are a good fit for our group and research activities, send an email to Prof. Michael Gertz with the subject "Anfrage BSc Arbeit" or "Anfrage MSc Arbeit" and include the following information:
- your current transcript (as PDF). You can download this from the LSF .
- information about your field of application ("Anwendungsfach"), in particular the courses you have taken
- your programming experience and projects you worked on
- areas of interest based on the research conducted in our group
- any other information you think might strengthen your request
We will then review this information and get back to you with the scheduling of an appointment in person to discuss further details.
Thesis Expose
Once we agree on a topic for your thesis, before you officially register for a thesis, we would like to get an idea of how you approach scientific research and whether you are able to do scientific writing. For this, we require that you write an expose of your planned thesis research (see, e.g., here or here ) . This document is about 4-6 pages and has to include a description of
- the context of your project and research
- problem statement(s)
- objectives and planned approaches
- related work
- milestones towards a timely completion of the thesis
Especially for the related work, it is important that you get a good overview early on in your thesis project; of course, your advisor will give you some starting points. Most of the time, such an expose becomes an integral part of the introductory chapter of your thesis, so there is no time and effort wasted. The expose needs to be submitted to your advisor on schedule (which you arrange with your advisor), who will then discuss the expose with you and coordinate the next steps. Occasionally we also have students give a 10-15 minute presentation of their research plan in front of the members of our group in order to get further ideas, comments, suggestions, and pointers on their thesis.
Official Registration
In agreement with your advisor, after you have submitted an expose of good quality, you plan for an official start date of the thesis. For this, please fill out the form suitable for your program of study:
- Für Anmeldung einer Bachelorarbeit, siehe hier .
- For officially registering your master's thesis, see here .
- Registration form for a MSc thesis in Scientific Computing (please see Mrs. Kiesel to obtain a form).
Hand in this form to Prof. Michael Gertz who will then turn in the signed form.
Thesis Research and Advising
- Here are some hints on grammar and style we maintain locally.
- Some easy, purely syntactic hints on writing good research papers (from Prof. Felix Naumann )
- Dos and don'ts, Universität Heidelberg, Prof. Dr. Anette Frank
- Leitfaden zur Abfassung wissenschaftlicher Arbeiten, Ruhr-Universität Bochum, Katarina Klein
- Leitfaden zur Abfassung wissenschaftlicher Arbeiten, TU Dresden, Maria Lieber
In addition, you can find a detailed description how to write a seminar paper using our template for seminar papers. The hints in this template might also be crucial when you are writing a thesis: [ seminar template .zip ] [ report sample pdf ] [ slides english pdf ] [ slides german pdf ]
Feel also free to ask us for copies of BSc/MSc thesis students did in the past in our group.
Thesis Template
- Thesis template [.zip] ; see a sample PDF here .
Thesis Presentation
- English LaTeX-Beamer template for the presentation: template [.zip] , sample PDF
- German LaTeX-Beamer template for the presentation: template [.zip] , sample PDF
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Erasmus School of Economics. Master Thesis: Data Science and Marketing Analytics. Interpretable Machine Learning for Attribution Modeling. A Machine Learning Approach for Conversion Attribution in Digital Marketing Student name: Jordy Martodipoetro Student number: 454072 Supervisor: Dr. Kathrin Gruber Second assessor: Prof. Bas Donkers Date ...
PDF. Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena, Sachi Perera. PDF. Global to Glocal: A Confluence of Data Science and Earth Observations in the Advancement of the SDGs, Rejoice Thomas. Dissertations from 2023 PDF
the association between variables. The first assumption of this method is that there exists a linear relationship (formula 7) between the predictor variables. formula 7 - Multiple linear regression. Y = β0 + β1. x1 + β2x2 + · · · + βpxp + εWhere βi are unknown constants, representing the model coefficient, and .
This thesis contains three self-contained chapters that adjust di erent aspects of high dimensional analysis. Chapter 1. A catalytic prior distribution is designed to stabilize a high-dimensional \working model" by shrinking it toward a \simpli ed model." The shrinkage is achieved by supplementing the observed data with a small amount of \synthetic
Harvard University
thesis is an exploration by well-motivated simulation scenarios. (3) Find/collect an appropriate set of data to illustrate the method. The context of the data should be explained, as well as a discussion of the results and an interpretation for the context of the data. Main reference: A. Fisher, C. Rudin, F. Dominici (2019).
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8-1 Three different data science competitions held during the period of 2014-2015. On the left is the data model for KDD Cup 2014, at the bottom center is the data model for IJCAI, and on the right is the data model of KDD Cup 2015. A total of 906 teams took part in these competitions. We note that two out of three competitions are
Thesis Outline This dissertation is organized into five parts. Part 1 gives a high-level overview of the content of this dissertation. Part 2 (Chapter 2) is the modern revisit of the classical idea of Kelly gambling using distributional robust ... views on mathematics, on data science, and furthermore, on life choices. His unconditional ...
University of Washington
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
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.
Data Science Masters Theses. The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis.
Inwook Moon. geting by Data Science MethodsYear2020Number of pages45The objective of this thesis is performing a segmentation analysis as well as. lassifying target segment members with a given survey data. With the performance of this customer survey data analysis, the purpose of this research is to confirm the.
Data Science for. Small Businesses. by. Aveesha Sharma. A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science. Approved April 2016 by the Graduate Supervisory Committee: Arbi Ghazarian, Chair Ashraf Gaffar Srividya Bansal.
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
Masters Thesis for Data Science Author: Rana Muhammad Ahmad [email protected] Matricola: 1734354 . A New approach to piping engineering data control and management in the epc company during the engineering phase | Author: Rana Muhammad Ahmad 1 Acknowledgments
Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.) Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release) Save your work to the repository; Your thesis manuscript or capstone report will then be published on the MSDS page.
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.
This thesis aims to utilize data analysis and predictive modeling techniques and apply them in different domains for gaining insights. The topics were chosen keeping the same in mind. Analysis of customer interests is a crucial factor in present marketing trends and hence we worked on twitter data which is a significant part of digital marketing. Neuroscience, especially psychological behavior ...
CART decision tree methodology, classification trees, regression trees, interactive dihotomiser, C4.5, C5.5, decision stump, conditional decision tree, M5, and etc. 9. Logistic regression ...
Thesis template [.zip]; see a sample PDF here. Thesis Presentation Once you have submitted your thesis to the respective examination office (Mrs. Sopka for Computer Science, Mrs. Kiesel for Scientific Computing), together with your advsior, you schedule the presentation of your thesis. Once we have determined a date and time (in the case of a ...