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Education

Advising

• Wei Zhang, PhD student, Computer Science
• Huanan Zhang, PhD student, Computer Science
• Sheng Chen, PhD student, Computer Science
• David Roe, PhD student, BICB

Alumni

• Ze Tian, PhD, 2012. Microsoft-BING. Thesis: Learning with Kernels and Graphs to Understand Cancer DNA Copy Number Variations
• TaeHyun Hwang, PhD, 2011. Assistant Professor, University of Texas Southwestern Medical Center. Thesis: Network-based learning algorithms for understanding human disease
• Maoqiang Xie, Visiting scholar 2010-2011, Nankai University
• Changjin Hong, PostDoc, 2010. Boston University
• Baryun Hwang, M.S., 2009. LG Ltd. Thesis: Finding Consistent Biomarkers from Independent Microarray Gene Expression.
• Yixin Chen (co-advised), Master student, Health Informatics
• Thanh Le, B.S., 2012, Graduate student, Pennsylvania State University
• Xiang Yao, B.S., 2012, Graduate student, Columbia University
• Mila Gorodetsky, B.S., 2010. Graduate student, CMU. Thesis: A Constrained Label Propagation for Biomarker Discovery in Genetic Data.

Courses

• CSCI 8980 (Fall 2012):
  New Topics in Bioinformatics: Next Generation Sequencing and Biological Network Analysis

• CSCI 5525 (Spring 2012 and Fall 2010):
  Machine Learning
  
  The objective of this class is to provide conceptual, theoretical and experimental training in machine learning. The lectures will cover learning models for regression, classification, clustering, dimensionality reduction, and reinforcement learning... (read more)
  The objective of this class is to provide conceptual, theoretical and experimental training in machine learning. The lectures will cover learning models for regression, classification, clustering, dimensionality reduction, and reinforcement learning. The models are in the categories of Bayesian methods, decision trees, perceptrons, kernel machines, hidden Markov models, and graphical models. Various examples of applications in bioinformatics, text classification and image analysis will be discussed with demos. The specific goals for students include 1) To learn fundamental concepts in machine learning, 2) to acquire hands-on experiences with implementation of machine learning algorithms, and to learn how to formulate and solve application questions with appropriate machine learning methods. (Brief description)

• CSCI 5481 (Fall 2011, Fall 2008):
  Computational Techniques for Genomics
  
  The objective of this course is to give a comprehensive introduction to the current computational challenges as well as fundamental algorithms and computational models in modern computational molecular biology... (read more)
  The objective of this course is to give a comprehensive introduction to the current computational challenges as well as fundamental algorithms and computational models in modern computational molecular biology. Recently, the exciting genome projects have been greatly accelerated by the advent of large-scale sequencing technologies. Up to now, the complete genome sequences of around 750 species have become available, and anther 646 genomes are on the way for delivery in very near future. The tremendous amount of sequence data promise to shed light on the underlying biology principles carried and encoded by the genomes, and the evolutionary history of the species. It is by nature computational to prepare and study these large genome sequences, and many computational tools play a major role. In this course we will cover algorithms and computational models in the following four major themes,

  • Sequence analysis algorithms and models for processing and analyzing the genome data
  • Phylogenetic models and algorithms for inferring evolutionary history
  • Gene prediction and genome annotation algorithms
  • Models and algorithms for protein structure analysis and prediction (if time permits)

  These topics span a variety of computational methods such as local/global sequence alignment algorithms, hidden Markov models, motif finding algorithms, multiple sequence alignment algorithms, phylogenetic trees and algorithms for building and analyzing phylogenetic trees, gene prediction algorithms, gene function prediction algorithms, protein secondary structure prediction algorithms and homology modeling of protein structures. (Brief description)

• CSCI 5461 (Spring 2011, 2010, 2009, 2008):
  Functional Genomics, Systems Biology and Bioinformatics
  
  This course gives an introduction to various types of functional genomic data available and current computational and statistical methods used for analyzing the data to answer questions in functional genomics and systems biology... (read more)
  Recent developments in biotechnology have enabled large-scale DNA sequencing and high-throughput measurement of several cellular phenomena such as gene expression, protein-protein interactions and protein localization. These technologies have generated an unprecedented amount of data that contains rich information about gene function and systems-level organization of the cell. The tremendous amount of data presents an exciting opportunity for computer scientists, because computational models are necessary tools for interpreting and integrating the data to make inferences about cellular function. This course gives an introduction to various types of functional genomic data available and current computational and statistical methods used for analyzing the data to answer questions in functional genomics and systems biology. We will cover the analysis of gene expression data, proteomic data, and interaction data, with a special focus on how they can be used to understand and infer networks. We will end with a discussion of integrating multiple complementary data types, a powerful approach to build accurate descriptive and predictive models. These topics span a variety of computational methods from signal processing, machine learning and statistics including clustering, classification, pattern discovery, statistical tests and network inference. (Brief description)

• CSCI 2011 (Fall 2009, 2007):
  Discrete Structures of Computer Science
  
  In this course, we will study the necessary concepts and techniques in discrete mathematics that are centeral in the applications in computer science and engineering... (read more)
  In this course, we will study the necessary concepts and techniques in discrete mathematics that are centeral in the applications in computer science and engineering. Discrete mathematics is one of the most important fundations of a majority of computer science and engineering courses such as artificial intelligence, algorithms and data structure, programming languages, formal language and automata, cryptography, etc.. This course is required for admission to the CSci major and is a prerequisite for many more advanced CSci classes. We will cover propositional and predicate logic, proof methods, sets, functions, sequences, summation techniques, integer and matrix algorithms, asymptotic analysis and big-Oh notation, recursion and induction, counting methods, recurrences, discrete probabilities, and graphs. The expectation is that after taking the course students will understand how to think mathematically and logically, and use mathematical reasoning and other techniques to solve a variety real world problems. (Brief description)

• CSCI 8980 (Spring 2007):
  Applied Machine Learning in Computational Biology
  The objective of this course is to expose students to the cutting-edge research in computational biology... (read more)
  The objective of this course is to expose students to the cutting-edge research in computational biology. The exponential growth of biological data produced by new high-throughput experiments and sequencing technology has posed many challenging research problems in biology. Recent development of machine learning algorithms has had substantial impact on the computational biology research. These new learning techniques provide fast and effective solutions for analyzing these huge and noise datasets in practical applications. In this course, we will study several advanced machine learning algorithms that have been applied to biological data analysis, such as Support Vector Machines and Kernel Methods, Bayesian Networks, hidden Markov models, structured output learning algorithms, and network-based diffusion and clustering algorithms. In addition, the last part of this course will be dedicated to a special topic on the learning algorithms for integrating biological data, one of the most important challenges of the current computational biology research. Our lectures will span research problems in sequence analysis, structural genomics, functional genomics and proteomics. Students are expected to be able to apply learning algorithms to resolve challenging research problems in computational biology after taking the course. (Brief description)