Student Data Analysis Projects

Students are required to demonstrate their grasp of fundamental data analysis and machine learning concepts and techniques in the context of a focused project. The project should focus on a substantive problem involving the analysis of one or more data sets and the application of state-of-the art machine learning and data mining methods, or on suitable simulations where this is deemed appropriate. Or, the project may focus on machine learning methodology and demonstrate its applicability to substantial examples from the relevant literature. The project may involve the development of new methodology or extensions to existing methodology.

Semi-supervised context-aware discovery of unknown audio concepts [.pdf] - Antonio Juarez, 3/13

Learning Frames from Text with an Unsupervised Latent Variable Model [.pdf] - Brendan O'Connor, 10/12

TREEGL: Reverse Engineering Tree-Evolving Gene Networks Underlying Developing Biological Lineages [.pdf] - Ankur Parikh, 10/12

Automated Learning of Subcellular Location Patterns in Confocal Fluorescence Images from Human Protein Atlas [.pdf] - Jieyue Li, 10/12

Conditional Sparse Coding and Multiple Regression for Grouped Data [.pdf] - Min Xu, 5/12

Active, semi-supervised learning to utilize human oracles [.pdf] - Robert Fisher, 5/12

Decoding Word Semantics from Magnetocencephalography Time Series Transformations [.pdf] - Alona Fyshe, 5/12

Layered Timeseries Analysis for Smart Grid Agents [.pdf] - Prashant Reddy, 5/12

Learning Global Properties of Scene Images Based on Their Correlational Structures [.pdf] - Wooyoung Lee, 5/12

Understanding the Interaction between Interests, Conversations and Friendships in Facebook [.pdf] - Qirong Ho, 4/12

Trade-offs in Explanatory Model Learning [.pdf] - Madalina Fiterau, 3/12

Dynamics of Visual Category Learning with Magnetoencephalography [.pdf] - Yang Xu, 12/11

Online Detection of Unusual Events in Videos via Dynamic Sparse Coding [.pdf] - Bin Zhao, 11/11

Valid Statistical Inference on Automatically Matched Files [.pdf] - Rob Hall, 11/11

Modeling Correlated Purchase Behavior in Large-Scale Networks:A Markov Random Field (MRF) Approach [.pdf] - Liye Ma, 4/11

Multi-factor Analysis for Classifying fMRI Brain Images [.pdf] - Sung Won Park, 4/11

Learning the Sparsity Parameter in a Generalized Fast Subset Sums Framework for Bayesian Event Detection [.pdf] - Kan Shao, 4/11

CANTINA+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites [.pdf] - Guang Xiang, 4/11

Comparing Data Sources in High Dimensions [.pdf] - Di Liu, 3/11

Cross-Species Queries of Large Gene Expression Databases [.pdf] - Hai-Son Le, 3/11

Clustering Under Natural Stability Assumptions [.pdf] - Pranjal Awasthi, 3/11

Automated Unmixing of Complex Protein Subcellular Location Patterns [.pdf] - Tao Peng, 2/11

Extracting Subpopulations from Large Social Networks [.pdf] - Bin Zhang, 2/11

Inferring Rates of Domain Shuffling Using a Birth-Death and Gain Model - Maureen Stolzer, 1/11

Anomaly Detection for Astronomical Data [.pdf] - Liang Xiong, 12/10

An Efficient Proximal Gradient Method for General Structured Sparse Learning [.pdf] - Xi Chen, 11/10

Learning Dynamic Models from Non-sequenced Data [.pdf] - Tzu-Kuo Huang, 11/10

Multiple Domain User Personalization [.pdf] - Yucheng Low, 11/10

Learning Opponent's Strategies In the RoboCup Small Size League [.pdf] - Felipe Trevizan, 10/10

Parallel Splash Belief Propagation [.pdf] - Joseph Gonzalez, 5/10

Trends and Differences in Industrial Safety Perception survey results: Subset Selection and Minimax Bound based Hypothesis Testing - Liu Yang, 5/10

Learning to Tag using Noisy Labels [.pdf] - Edith Law, 4/10

Polonium: Tera-Scale Graph Mining for Malware Detection [.pdf] - Duen Horng Chau, 4/10

Data Mining with MapReduce: Graph and Tensor Algorithms with Applications [.pdf] - Charalampos Tsourakakis, 4/10

Learning Directed Graphical Models from Nonlinear and Non-Gaussian Data [.pdf] - Robert Tillman, 3/10

Semi-parametric Methods for Estimating Time-varying Graph Structure [.pdf] - Mladen Kolar, 2/10

Genetic Population Structure in Pacific Islanders [.pdf] - Suyash Shringarpure, 2/10

Discovery of Student Strategies using Hidden Markov Model Clustering [.pdf] - Benjamin Shih, 1/10

Grasping in Primates: Mechanics and Neural Basis [.pdf] - Lucia Castellanos, 12/09

Parallel WalkSAT with Clause Learning [.pdf] - Austin McDonald, 12/09

Semi-Supervised Discovery of Named Entities and Relations from the Web [.pdf] - Sophie Wang, 11/09

Learning Stable Linear Dynamical Systems [.pdf] - Byron Boots, 6/09

Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature [.pdf] - Amr Ahmed, 5/09

FilterBoost: Regression and Classification on Large Datasets [.pdf] - Joseph Bradley, 5/09

Learning Compressible Models [.pdf] - Yi Zhang, 5/09

Center-Piece Subgraphs: Problem Definition and Fast Solutions [.pdf] - Hanghang Tong, 8/08

Graph-Based Semi-Supervised Learning as a Generative Model [.pdf] - Jingrui He, 8/08

Information Propagation on the Web: Patterns and a Model [.pdf] - Mary McGlohon, 11/07

Maximum Likelihood Estimation in Latent Class Models for Contingency Table Data [.pdf] - Yi Zhou, 11/07

A Comparison of Methods for Transductive Transfer Learning [.pdf] - Andrew Arnold, 5/07

Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement [.pdf] - Hao Cen, 5/07

The Complexity of Interactive Machine Learning [.pdf] - Stephen Hanneke, 5/07

T-cube: Fast Extraction of Time Series from Large Datasets [.pdf] - Maheshkumar Sabhnani, 5/07

Learning Selectively conditioned Forest Structures with Applications to DBNs and Classification [.pdf] - Brian Ziebart, 5/07

Large-Scale Automated Analysis of Location Patterns in Randomly-Tagged 3T3 Cells [.pdf] - Juchang Hua, 4/07

Continuous Hidden Process Model for Time Series [.pdf] - Yanxin Shi, 4/07

Modeling Networks Using Kronecker Multiplication [.pdf] - Jurij Leskovec, 4/07

Gene Family Classification using a Semi-Supervised Learning Method [.pdf] - Nan Song, 1/07

Feature Reduction for Improved Recognition of Subcellular Location Patterns in Fluorescence Microspoe Images [.pdf] - Kai Huang, 11/06

Intelligent Light Control using Sensor Networks [.pdf] - Vipul Singhvi, 9/06

Incremental Hierarchical Clustering of Text Documents [.pdf] - Nachiketa Sahoo, 5/06

Using Customer's Reported Forecasts to Predict Future Sales [.pdf] - Nihat Altintas, 5/06

Data Mining in Macroeconomic Data Sets [.pdf] - Ping Chen, 4/06

Dynamic Social Network Analysis using Latent Space Models [.pdf] - Purnamrita Sarkar, 4/06

Active Learning for Identifying Function Threshold Boundaries [.pdf] - Brent Bryan, 4/06

Anomoly Detection in Multivariate Time Series [.pdf] - Kustav Das, 3/06

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables [.pdf] - Frederick Eberhardt, 9/05

N-1 Experiments Suffice to Determine the Causal Relations Among N Variables [.pdf] - Frederick Eberhardt, 9/05

Conditional Density Estimation using Finite Mixture Models with an Application to Astrophysics [.pdf] - Alex Rojas-Pena, 7/05

Location proteomics - Building subcellular location trees from high resolution 3D fluorescence microsope images of randomly-tagged proteins [.pdf] - Xiang Chen, 5/05

Tabu Search Enhanced Markov Blanket Classifier for High Dimensional Data Sets [.pdf] - Xue Bai, 1/05

Clustering Short Time Series Gene Expression Data [.pdf] - Jason Ernst, 11/04

A Hierarchical Graphical Model for Record Linkage [.pdf] - Pradeep Ravikumar, 5/04

Learning Robust Rules from Data: The GenTree Algorithm [.pdf] - Yiheng Li, 4/04

Advances in Network Tomography [.pdf] - Edoardo Airoldi, 10/03

Improved Recognition of Protein Subcellular Location Patterns via Feature Selection and Classifier Ensembles [.pdf] - Kai Huang, 8/03

Fractal Dimension for Data Mining [.pdf] - Sree Krishna Kumaraswamy, 7/03

Tools for Graph Mining [.pdf] - Yiping Zhan, 6/03

Using Machine Learning to Detect Cognitive States across Multiple Subjects [.pdf] - Xuerui Wang, 5/03

People Tracking Using Many Simple Sensors [.pdf] - Daniel Wilson, 5/03

Simultaneous Localization and Mapping using Sparse Extended Iinformation Filters [.pdf] - Yufeng Liu, 4/03

Multi-agent Learning in Extensive Games with Complete Information [.pdf] - Pu Huang, 1/03

Compromising Privacy with Trail Re-Identification: The REIDIT Algorithms [.pdf] - Bradley Malin, 12/02

Mining Computer Tutor-Student Interaction Data to Assess Students Reading and Predict Future Behavior [.pdf] - Peng Jia, 10/02

A Method for Automatically Finding Interpretations of Reduced Dimension Representations [.pdf] - Marc Fasnacht, 9/02

A Method for Automatically Finding Structural Motifs in Proteins [.pdf] - Marc Fasnacht, 9/02

Learning Rich Neural Network Topologies [.pdf] - Matteo Matteucci, 7/02

The Structure of the Unobserved [.pdf] - Ricardo Silva, 6/02

Learning from Labeled and Unlabeled Data with Label Propagation [.pdf] - Xiaojin Zhu, 6/02

The "DGX" Distribution for Mining Massive, Skewed Data [.pdf] - Zhiqiang Bi, 5/02

Diffusion Kernels on Graphs and Other Discrete Input Spaces [.pdf] - Risi Imre Kondor, 4/02

Large-scale Automated Forecasting using Fractals [.pdf] - Deepayan Chakrabarti, 4/02

Planning for Single and Multiple Actors in Markov Decision Processes with Deterministic Hidden State [.pdf] - Jamie Schulte, 12/01

Boosting and Maximum Likelihood for Expontial Models [.pdf] - Guy Lebanon, 9/01

Framework for using grocery data for early detection of Bio-terrorism attacks [.pdf] - Anna Goldenberg, 9/01

Causal Inference [.pdf] and Additive Models[.pdf] - Tianjiao Chu, 5/01

Using Error-Correcting Codes for Efficient Text Categorization with a Large Number of Categories [.pdf] - Rayid Ghani, 5/01

A Boosting Approach to Topic Spotting on Subdialogues [.pdf] - Kary Myers, 2000