Rui Kuang

Teaching

Fall 2009 (CSCI 2011):
Discrete Structures of Computer Science
(description)
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.

Spring 2009 (CSCI 5461):
Functional Genomics, Systems Biology and Bioinformatics
(description)
Recent developments in biotechnology have enabled large-scale DNA sequencing and high-throughput measurement of several cellular phenomena including 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. Methods for effective data visualization will also be a recurring theme.

Fall 2008 (CSCI 5481):
Computational Techniques for Genomics
(description)
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.

Spring 2008 (CSCI 5980):
Functional Genomics, Systems Biology and Bioinformatics
(description)
Recent developments in bRecent developments in biotechnology have enabled large-scale DNA sequencing and high-throughput measurement of several cellular phenomena including 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. Methods for effective data visualization will also be a recurring theme.

Fall 2007 (CSCI 2011):
Discrete Structures of Computer Science
(description)
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 & 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.

Spring 2007 (CSCI 8980):
Applied Machine Learning in Computational Biology
(description)
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.