The American Mathematical Society (AMS) recently notice Monthly Journal A long list of all PhDs in Mathematics and Statistics awarded between 1st July 2019 and 30th June 2020. Degrees come from 242 departments at 186 US universities.
I like to keep track of research areas in my field, so I went through the entire public list and picked 48 papers with strong relevance to data science, machine learning, AI, and deep learning. I called. The list below is organized alphabetically by state. fun!
Auburn University, Alabama, Xu, Chi, Generalized Lasso Problem with Equality and Inequality Constraints Using ADMM.
Arizona, University of Arizona, Courtney, Ryan, Responsible Softmax Layer for Deep Learning.
Ray, Lihua, UC Berkeley, CA, Modern Statistical Inference for Classical Statistical Problems.
California, University of California, Berkeley, Walter, Simon, High-Dimensional Casual Reasoning.
California, University of California, Santa Cruz, Meng, Rui, Temporal Data Models with Stochastic Processes.
California, University of California, Santa Cruz, Shuler, Kurtis, Bayesian hierarchical model for count data.
Connecticut, University of Connecticut, Chen, Renjie, Topological Data Analysis for Time Series Clustering and Classification.
Florida, Florida Tech, Lakala, Nandini, Multi-objective optimization-based machine learning with real-world applications.
Online feature selection and its applications by Sun, Lizhe, Annealing, Florida Institute of Technology, Florida.
Georgia, Georgia State University, Parkerson, Eric, Learning with Noise, Sparse Errors, Missing Data
Georgia, Georgia State University, Chung, Hee Cheol, Contributions to Statistical Inference of Small Sample Size Data: Small Region Estimation and High Dimensional Low Sample Size Data
Georgia, Georgia State University, Poythress, JC, Regularization Methods for Statistical Methods Utilizing Matrix/Tensor Decomposition
Illinois, University of Illinois at Chicago, Hao, Shuai, Support Points for Locally Optimal Design of Multinomial Logistic Regression Models
Illinois, University of Illinois at Chicago, Wang, Xuelong, Typical Approaches to Big Data Dimensionality Reduction with Binary Responses
University of Illinois, Urbana-Champaign, Mann, Albert, Mode Jumping Algorithms for Exploratory Factor Analysis with Continuous and Binomial Responses
State of Illinois, University of Illinois, Urbana-Champaign, Xue, Fay, Variable Selection for High-Dimensional Complex Data
Indiana State, Indiana University, Bloomington, Ding, Ray, Supervised Learning and Outlier Detection for High-Dimensional Data Using Principal Components
Indiana University, Indiana-Purdue University Indianapolis, Zhou, Dali, Large Data K-Means Clustering and Bootstrapping with A-Optimal Subsampling
Indiana, Purdue University, Xu, Yixi, Understanding Deep Neural Networks and Other Nonparametric Techniques in Machine Learning
Baker, Cody, University of Notre Dame, Indiana, Second Moment of Activity in Large Neural Network Models
Pyle, Ryan, University of Notre Dame, Indiana, Dynamics and Computation in Recurrent Neural Networks
Iowa, Iowa State University, Chakraborty, Abhishek, some Bayesian methods for vector data using biclustering and binary coordinates
Louisiana, Tulane University, Qu, Zhe, High Dimensional Statistical Data Integration
Maryland, Johns Hopkins University, Kundu, Prosenjit, Statistical Methods for Integrating Heterogeneous Data Sources
State of Maryland, University of Maryland, College Park, Goldblum, Micah Isaac, Adversarial Robustness and Robust Meta-Learning in Neural Networks
Maryland, University of Maryland, College Park, Wren, Yisin, Regression analysis of recurrent events with measurement error
Massachusetts, University of Massachusetts, Amherst, Hu, Weilong, Utilizing unlabeled data and optimizing query strategies with adversarial attacks in active learning
Michigan, Michigan State University, Yang, Kaixu, Theory and Methods of Statistical Machine Learning for High-Dimensional Low-Sample-Size Problems
State of Michigan, University of Michigan, Sun, Yitong, Random Feature Methods in Supervised Learning
Montana State University, Montana, Theobold, Allison, Supporting Data-Intensive Environmental Science Research: Data Science Skills for Statistical Scientific Practitioners
New Jersey, Princeton University, Ma, Chao, The Mathematical Theory of Neural Network Models for Machine Learning
New York, Columbia University, Dieng, Aji, Deep Probabilistic Graphical Modeling
New York, Columbia University, Yusuf, Kasif, Essay on High Dimensional Time Series Analysis
New York, Cornell University, Tang, Hui Feng, Interpretable Approaches to Open Black-Box Models
Ohio, Bowling Green State University, Pauline, Afroza, Simultaneous Inference on High-Dimensional and Correlated Data
Bowling Green State University, Ohio, Yousef, Mohammed, Two-Stage SCAD Lasso for Linear Mixed Model Selection
Ohio, University of Cincinnati, Lee, Miaoqi, Statistical Models and Algorithms for Large Data with Complex Dependency Structures
Portland State University, Oregon, Rose, Anthony, Leveraging Model Flexibility and Deep Structure: Applications to Nonparametric and Deep Models and Deep Model Compression in Computer Vision Processes
Pennsylvania, Carnegie Mellon University, Ye, Weicheng, Bandit method and selective prediction in deep learning
Pennsylvania, Pennsylvania State University, University Park, Lee, Great Wall, Topics in Higher 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, Milshani, Ardaran, Regularization Methods in Functional Data Analysis
Pennsylvania, Pennsylvania State University, University Park, Parsons, Jacob, Integration and Evaluation of Multiple Data Sources
Pennsylvania, University of Pennsylvania, Karatapanis, Konstantinos, specific systems arising in stochastic gradient descent
TX, 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, Yichen, Bayesian Hierarchical Models and Moment Boundaries for High-Dimensional Time Series
C.Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist at insideBIGDATA. In addition to being a technology journalist, Daniel is also a data scientist consultant, author, educator, and serves on numerous advisory boards for various start-ups.
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