# PhD Dissertations

## [All are .pdf files]

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**

Discovering Compact and Informative Structures through Data Partitioning**Madalina Fiterau-Brostean, 2015**

**Machine Learning in Space and TimeSeth 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

## Cortical spatiotemporal plasticity in visual category learning

Yang Xu, 2013

Yang Xu, 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