Ph.D. Student
UCSD Computer Science
Office: EBU3B 3254
Resume: pdf | html
Relevant Courses
CSE 250A. Probabilistic Reasoning and Decision-Making (Lawrence Saul)
Belief Networks, Linear and Logistic Regression, Maximum Likelihood Estimation (MLE), Expectation Maximization, Hidden Markov Models

CSE 291. Unsupervised Learning (Lawrence Saul)
Principal Component Analysis, Factor Analysis, Non-negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA) and Variational Inference, Singular Value Decomposition, Low-Rank Matrix Approximations, Multidimensional Scaling (MDS)

ECE 175. Elements of Statistical Learning (Nuno Vasconcelos)
Bayes Decision Rule (BDR), Least Squares Problem, K-means, Kernels and Support Vector Machine (SVM)

ECE 271A. Statistical Learning I (Nuno Vasconcelos)
Multivariate Gaussian Classifier, Bias and Variance, Bayesian Parameter Estimation, Conjugate and Non-informative Priors, Kernel-based Density Estimates, Mixture Models

CSE 256. Statistical Natural Language Processing (Roger Levy)
Language Modeling, Topics Models for Text Categorization, Word Segmentation, Part-of-speech Tagging,Word-sense disambiguation and semantic roles

CSE 252C. Selected Topics in Vision & Learning (Serge Belongie)
Interest Point Detection, Spectral Graph Theoretic Grouping, Shape Matching, Kernel PCA and Support Vector Machines, Boosting and AdaBoost

Other Courses
CSE 200. Computability and Complexity
CSE 202. Algorithms and Analysis
CSE 221. Operating Systems
CSE 240A. Principles of Computer Architecture
CSE 291. Machine Perception
ECE 161A. Intro to Digital Signal Processing