{"id":32258,"date":"2023-05-10T06:00:00","date_gmt":"2023-05-10T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=32258"},"modified":"2023-05-10T08:36:37","modified_gmt":"2023-05-10T15:36:37","slug":"top-data-science-ph-d-dissertations-2019-2020","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/05\/10\/top-data-science-ph-d-dissertations-2019-2020\/","title":{"rendered":"Top Data Science Ph.D. Dissertations (2019-2020)"},"content":{"rendered":"\n

The American Mathematical Society<\/a> (AMS) recently published in its Notices<\/em> 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. <\/p>\n\n\n\n

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!<\/p>\n\n\n\n

Alabama, Auburn University, Xu, Chi, Generalized Lasso Problem with Equality and Inequality Constraints Using ADMM<\/a>.<\/p>\n\n\n\n

Arizona, University of Arizona, Coatney, Ryan, A Responsible Softmax Layer in Deep Learnin<\/a>g.<\/p>\n\n\n\n

California, University of California, Berkeley, Lei, Lihua, Modern Statistical Inference for Classical Statistical Problems<\/a>. <\/p>\n\n\n\n

California, University of California, Berkeley, Walter, Simon, High-dimensional and Casual Inference<\/a>.<\/p>\n\n\n\n

California, University of California, Santa Cruz, Meng, Rui, Temporal Data Models Via Stochastic Process<\/a>. <\/p>\n\n\n\n

California, University of California, Santa Cruz, Shuler, Kurtis, Bayesian Hierarchical Models for Count Data<\/a>. <\/p>\n\n\n\n

Connecticut, University of Connecticut, Chen, Renjie, Topological Data Analysis for Clustering and Classifying Time Series<\/a>. <\/p>\n\n\n\n

Florida, Florida Institute of Technology, Rakala, Nandini, Multi-objective Optimization Based Machine Learning with Real-life Applications<\/a>. <\/p>\n\n\n\n

Florida, Florida Institute of Technology, Sun, Lizhe, Online Feature Selection with Annealing and Its Applications<\/a>. <\/p>\n\n\n\n

Georgia, Georgia State University, Perkerson, Eric, Learning with Noise, Sparse Errors, and Missing Data<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Georgia, Georgia State University, Poythress, JC, Regularization Techniques for Statistical Methods Utilizing Matrix\/Tensor Decompositions<\/a><\/p>\n\n\n\n

Illinois, University of Illinois at Chicago, Hao, Shuai, Support Points of Locally Optimal Designs for Multinomial Logistic Regression Models<\/a><\/p>\n\n\n\n

Illinois, University of Illinois at Chicago, Wang, Xuelong, Representative Approach for Big Data Dimension Reduction with Binary Responses<\/a><\/p>\n\n\n\n

Illinois, University of Illinois, Urbana-Champaign, Man, Albert, A Mode-jumping Algorithm for Exploratory Factor Analysis with Continuous and Binary Responses<\/a><\/p>\n\n\n\n

Illinois, University of Illinois, Urbana-Champaign, Xue, Fei, Variable Selection for High-dimensional Complex Data<\/a><\/p>\n\n\n\n

Indiana, Indiana University, Bloomington, Ding, Lei, Supervised Learning and Outlier Detection for High-dimensional Data Using Principal Components<\/a><\/p>\n\n\n\n

Indiana, Indiana University-Purdue University Indianapolis, Zhou, Dali, Massive Data K-means Clustering and Bootstrapping via A-optimal Subsampling<\/a><\/p>\n\n\n\n

Indiana, Purdue University, Xu, Yixi, Understanding Deep Neural Networks and other Nonparametric Methods in Machine Learning<\/a><\/p>\n\n\n\n

Indiana, University of Notre Dame, Baker, Cody, Second-Order Moments of Activity in Large Neural Network Models<\/a><\/p>\n\n\n\n

Indiana, University of Notre Dame, Pyle, Ryan, Dynamics and Computations in Recurrent Neural Networks<\/a><\/p>\n\n\n\n

Iowa, Iowa State University, Chakraborty, Abhishek, Some Bayes Methods for Biclustering and Vector Data with Binary Coordinates<\/a><\/p>\n\n\n\n

Louisiana, Tulane University, Qu, Zhe, High-dimensional Statistical Data Integration<\/a><\/p>\n\n\n\n

Maryland, Johns Hopkins University, Kundu, Prosenjit, Statistical Methods for Integrating Disparate Data Sources<\/a><\/p>\n\n\n\n

Maryland, University of Maryland, College Park, Goldblum, Micah Isaac, Adversarial Robustness and Robust Meta-Learning for Neural Networks<\/a><\/p>\n\n\n\n

Maryland, University of Maryland, College Park, Ren, Yixin, Regression Analysis of Recurrent Events with Measurement Errors<\/a><\/p>\n\n\n\n

Massachusetts, University of Massachusetts, Amherst, Hu, Weilong, Exploiting Unlabeled Data and Query Strategy Optimization with Adversarial Attacks in Active Learning<\/a><\/p>\n\n\n\n

Michigan, Michigan State University, Yang, Kaixu, Statistical Machine Learning Theory and Methods for High-dimensional Low Sample Size Problems<\/a><\/p>\n\n\n\n

Michigan, University of Michigan, Sun, Yitong, Random Features Methods in Supervised Learning<\/a><\/p>\n\n\n\n

Montana, Montana State University, Theobold, Allison, Supporting Data-intensive Environmental Science Research: Data Science Skills for Scientific Practitioners of Statistics<\/a><\/p>\n\n\n\n

New Jersey, Princeton University, Ma, Chao, Mathematical Theory of Neural Network Models for Machine Learning<\/a><\/p>\n\n\n\n

New York, Columbia University, Dieng, Adji, Deep Probabilistic Graphical Modeling<\/a><\/p>\n\n\n\n

New York, Columbia University, Yousuf, Kashif, Essays in High Dimensional Time Series Analysis<\/a><\/p>\n\n\n\n

New York, Cornell University, Tan, Hui Fen, Interpretable Approaches to Opening Up Black-box Models<\/a><\/p>\n\n\n\n

Ohio, Bowling Green State University, Polin, Afroza, Simultaneous Inference for High Dimensional and Correlated Data<\/a><\/p>\n\n\n\n

Ohio, Bowling Green State University, Yousef, Mohammed, Two-Stage SCAD Lasso for Linear Mixed Model Selection<\/a><\/p>\n\n\n\n

Ohio, University of Cincinnati, Li, Miaoqi, Statistical models and algorithms for large data with complex dependence structures<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Pennsylvania, Carnegie Mellon University, Ye, Weicheng, Bandit Methods and Selective Prediction in Deep Learning<\/a><\/p>\n\n\n\n

Pennsylvania, Pennsylvania State University, University Park, Li, Changcheng, Topics in High-dimensional Statistical Inference<\/a><\/p>\n\n\n\n

Pennsylvania, Pennsylvania State University, University Park, Liu, Wanjun, New Statistical Tools for High-dimensional Data Modeling<\/a><\/p>\n\n\n\n

Pennsylvania, Pennsylvania State University, University Park, Mirshani, Ardalan, Regularization Methods In Functional Data Analysis<\/a><\/p>\n\n\n\n

Pennsylvania, Pennsylvania State University, University Park, Parsons, Jacob, The Integration and Evaluation of Multiple Data Sources<\/a><\/p>\n\n\n\n

Pennsylvania, University of Pennsylvania, Karatapanis, Konstantinos, Certain Systems Arising In Stochastic Gradient Descent<\/a><\/p>\n\n\n\n

Texas, University of Texas at Austin, Zhang, Jiong, Efficient Deep Learning for Sequence Data<\/a><\/p>\n\n\n\n

Washington, University of Washington, Gao, Lucy, Statistical Inference for Clustering<\/a><\/p>\n\n\n\n

Washington, University of Washington, Aicher, Christopher, Scalable Learning in Latent State Sequence Models<\/a><\/p>\n\n\n\n

Washington, University of Washington, Li, Yicheng, Bayesian Hierarchical Models and Moment Bounds for High-dimensional Time Series<\/a><\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. 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. <\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n

Join us on LinkedIn: https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n

Join us on Facebook: https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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.<\/p>\n","protected":false},"author":37,"featured_media":32355,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,182,180,67,268,56,77,84,1],"tags":[437,133,264,277,705,1294,134,95],"acf":[],"yoast_head":"\nTop Data Science Ph.D. Dissertations (2019-2020) - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/05\/10\/top-data-science-ph-d-dissertations-2019-2020\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top Data Science Ph.D. Dissertations (2019-2020) - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"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. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/05\/PhD_shutterstock_1015610122_NEW.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-8oi","jetpack-related-posts":[{"id":21994,"url":"https:\/\/insidebigdata.com\/2019\/01\/16\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-december-2018\/","url_meta":{"origin":32258,"position":0},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 December 2018","date":"January 16, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":22953,"url":"https:\/\/insidebigdata.com\/2019\/07\/18\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-june-2019\/","url_meta":{"origin":32258,"position":1},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 June 2019","date":"July 18, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":22263,"url":"https:\/\/insidebigdata.com\/2019\/03\/15\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-february-2019\/","url_meta":{"origin":32258,"position":2},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 February 2019","date":"March 15, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":22366,"url":"https:\/\/insidebigdata.com\/2019\/03\/26\/imperial-college-london-and-coursera-announce-new-online-masters-degrees-in-machine-learning\/","url_meta":{"origin":32258,"position":3},"title":"Imperial College London and Coursera Announce New Online Master’s Degrees in Machine Learning","date":"March 26, 2019","format":false,"excerpt":"Imperial College London, a globally ranked top 10 university, today announced an online MSc in Machine Learning on Coursera, a leading online learning platform. This is a new online master\u2019s degrees in machine learning and will help students pursue the most advanced engineering roles in artificial intelligence (AI), data science,\u2026","rel":"","context":"In "Education \/ Training"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/03\/machine-learning_SHUTTERSTOCK.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":22155,"url":"https:\/\/insidebigdata.com\/2019\/02\/20\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-january-2019\/","url_meta":{"origin":32258,"position":4},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 January 2019","date":"February 20, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":21252,"url":"https:\/\/insidebigdata.com\/2018\/10\/17\/best-arxiv-org-ai-machine-learning-deep-learning-september-2018\/","url_meta":{"origin":32258,"position":5},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 September 2018","date":"October 17, 2018","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/32258"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=32258"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/32258\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/32355"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=32258"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=32258"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=32258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}