Top Data Science Ph.D. Dissertations (2019-2020)

The American Mathematical Society (AMS) recently published in its Notices monthly journal a long list of all the doctoral degrees conferred from July 1, 2019 to June 30, 2020 for mathematics and statistics. The degrees come from 242 departments in 186 universities in the U.S.

I enjoy keeping a pulse on the research realm for my field, so I went through the entire published list and picked out 48 dissertations that have high relevance to data science, machine learning, AI and deep learning. The list below is organized alphabetically by state. Enjoy!

Alabama, Auburn University, Xu, Chi, Generalized Lasso Problem with Equality and Inequality Constraints Using ADMM.

Arizona, University of Arizona, Coatney, Ryan, A Responsible Softmax Layer in Deep Learning.

California, University of California, Berkeley, Lei, Lihua, Modern Statistical Inference for Classical Statistical Problems.

California, University of California, Berkeley, Walter, Simon, High-dimensional and Casual Inference.

California, University of California, Santa Cruz, Meng, Rui, Temporal Data Models Via Stochastic Process.

California, University of California, Santa Cruz, Shuler, Kurtis, Bayesian Hierarchical Models for Count Data.

Connecticut, University of Connecticut, Chen, Renjie, Topological Data Analysis for Clustering and Classifying Time Series.

Florida, Florida Institute of Technology, Rakala, Nandini, Multi-objective Optimization Based Machine Learning with Real-life Applications.

Florida, Florida Institute of Technology, Sun, Lizhe, Online Feature Selection with Annealing and Its Applications.

Georgia, Georgia State University, Perkerson, Eric, Learning with Noise, Sparse Errors, and Missing Data

Georgia, Georgia State University, Chung, Hee Cheol, Some Contributions to Statistical Inference on Small Sample Size Data: Small Area Estimation and High Dimension Low Sample Size Data

Georgia, Georgia State University, Poythress, JC, Regularization Techniques for Statistical Methods Utilizing Matrix/Tensor Decompositions

Illinois, University of Illinois at Chicago, Hao, Shuai, Support Points of Locally Optimal Designs for Multinomial Logistic Regression Models

Illinois, University of Illinois at Chicago, Wang, Xuelong, Representative Approach for Big Data Dimension Reduction with Binary Responses

Illinois, University of Illinois, Urbana-Champaign, Man, Albert, A Mode-jumping Algorithm for Exploratory Factor Analysis with Continuous and Binary Responses

Illinois, University of Illinois, Urbana-Champaign, Xue, Fei, Variable Selection for High-dimensional Complex Data

Indiana, Indiana University, Bloomington, Ding, Lei, Supervised Learning and Outlier Detection for High-dimensional Data Using Principal Components

Indiana, Indiana University-Purdue University Indianapolis, Zhou, Dali, Massive Data K-means Clustering and Bootstrapping via A-optimal Subsampling

Indiana, Purdue University, Xu, Yixi, Understanding Deep Neural Networks and other Nonparametric Methods in Machine Learning

Indiana, University of Notre Dame, Baker, Cody, Second-Order Moments of Activity in Large Neural Network Models

Indiana, University of Notre Dame, Pyle, Ryan, Dynamics and Computations in Recurrent Neural Networks

Iowa, Iowa State University, Chakraborty, Abhishek, Some Bayes Methods for Biclustering and Vector Data with Binary Coordinates

Louisiana, Tulane University, Qu, Zhe, High-dimensional Statistical Data Integration

Maryland, Johns Hopkins University, Kundu, Prosenjit, Statistical Methods for Integrating Disparate Data Sources

Maryland, University of Maryland, College Park, Goldblum, Micah Isaac, Adversarial Robustness and Robust Meta-Learning for Neural Networks

Maryland, University of Maryland, College Park, Ren, Yixin, Regression Analysis of Recurrent Events with Measurement Errors

Massachusetts, University of Massachusetts, Amherst, Hu, Weilong, Exploiting Unlabeled Data and Query Strategy Optimization with Adversarial Attacks in Active Learning

Michigan, Michigan State University, Yang, Kaixu, Statistical Machine Learning Theory and Methods for High-dimensional Low Sample Size Problems

Michigan, University of Michigan, Sun, Yitong, Random Features Methods in Supervised Learning

Montana, Montana State University, Theobold, Allison, Supporting Data-intensive Environmental Science Research: Data Science Skills for Scientific Practitioners of Statistics

New Jersey, Princeton University, Ma, Chao, Mathematical Theory of Neural Network Models for Machine Learning

New York, Columbia University, Dieng, Adji, Deep Probabilistic Graphical Modeling

New York, Columbia University, Yousuf, Kashif, Essays in High Dimensional Time Series Analysis

New York, Cornell University, Tan, Hui Fen, Interpretable Approaches to Opening Up Black-box Models

Ohio, Bowling Green State University, Polin, Afroza, Simultaneous Inference for High Dimensional and Correlated Data

Ohio, Bowling Green State University, Yousef, Mohammed, Two-Stage SCAD Lasso for Linear Mixed Model Selection

Ohio, University of Cincinnati, Li, Miaoqi, Statistical models and algorithms for large data with complex dependence structures

Oregon, Portland State University, Rhodes, Anthony, Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression

Pennsylvania, Carnegie Mellon University, Ye, Weicheng, Bandit Methods and Selective Prediction in Deep Learning

Pennsylvania, Pennsylvania State University, University Park, Li, Changcheng, Topics in High-dimensional Statistical Inference

Pennsylvania, Pennsylvania State University, University Park, Liu, Wanjun, New Statistical Tools for High-dimensional Data Modeling

Pennsylvania, Pennsylvania State University, University Park, Mirshani, Ardalan, Regularization Methods In Functional Data Analysis

Pennsylvania, Pennsylvania State University, University Park, Parsons, Jacob, The Integration and Evaluation of Multiple Data Sources

Pennsylvania, University of Pennsylvania, Karatapanis, Konstantinos, Certain Systems Arising In Stochastic Gradient Descent

Texas, University of Texas at Austin, Zhang, Jiong, Efficient Deep Learning for Sequence Data

Washington, University of Washington, Gao, Lucy, Statistical Inference for Clustering

Washington, University of Washington, Aicher, Christopher, Scalable Learning in Latent State Sequence Models

Washington, University of Washington, Li, Yicheng, Bayesian Hierarchical Models and Moment Bounds for High-dimensional Time Series

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideAI News. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies. 

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