CS734/834: Introduction to Information Retrieval

This class will explore the theory and engineering of information retrieval in the context of developing web-based search engines. The course will explore topics related to crawling, ranking, query processing, retrieval models, evaluation, clustering, and other aspects related to building search engines. The course will also cover recently established ranking algorithms that incorporate semantic similarities, machine learning, and neural network methods, such as learning to rank and neural information retrieval. The class will feature several hands-on development and coding using tools such as Google Custom Search, ElasticSearch, as well as a theoretical exploration of the existing literature on these topics. An external speaker will also be invited to give a talk on contemporary search engine and related topics (depending on availability). Students must be comfortable with self-directed learning appropriate for an advanced graduate class.

This course was offered by me at ODU CS in Fall 2021 (syllabus) and Spring 2019.

CS795/895: Mining Scholarly Big Data

One of the computer science subject areas that are the most impacted by artificial intelligence in the last decade is natural language processing (NLP). This technology further leads to advancements for machines to read, understand, and write textual content.

This seminar is designed to use textual content in scientific documents as an example to train graduate students effective and efficient ways to process text and extract statistical, syntactical, and semantic features from free text. The other half of the seminar will cover contemporary research topics in scholarly big data, an instance of big data, and more broadly text mining. The course will introduce commonly used machine learning (ML), NLP, and information retrieval (IR) tools as a preparation for a course project.

This course was offered by me at ODU CS in Fall 2020 (syllabus) and Fall 2018.

CS795/895: Deep Learning for Natural Language Understanding

Over the past two decades, with the advent and prevalence of GPUs and recently adopted TPUs, deep learning has made significant revolutionary advances, making remarkable progress on state-of-the-art tasks in traditional natural language processing (NLP) and computer vision (CV). In this background, a new subject field called natural language understanding (NLU) emerged out of and has received much attention by both academia and industrial researchers. The core task of NLU is to tackle fundamental challenges to train and test computer algorithms that effectively and efficiently represent human language by data structures that are processable by computers and to build artificial intelligent (AI) systems to mimic human’s ability to interpret and generate human languages.

The subject covers many emerging research topics. Some have made substantial progress over the past decade (such as building pre-trained language models) and some are still challenging (such as automatically generating coherent abstract summaries). This topical course is designed for graduate students to learn fundamental concepts and algorithms of deep learning and to explore important research topics in NLU including contextual representation models, grounded language understanding, natural language reference, supervised sentiment analysis, neural information retrieval, relation extraction with distant supervision, semantic parsing. The course will also introduce representative benchmark datasets and evaluation metrics.

This course is offered by me at ODU CS in Spring 2022 (syllabus).

IST210: Organization of Data

As the database management software becomes one of the critical components in modern IT applications and systems, a solid understanding of the fundamental knowledge on the design and management of data is required for virtually any IT professionals. In a business setting, such IT professionals should be able to talk to the clients to derive right requirements for database applications, ask the right questions about the nature of their entities and in-between relationships in their business scenarios, analyze and develop an effective and robust design to address business constraints, and react to the existing database designs as new needs arise. Solid understanding of the underlying data models and design issues in data applications are also critical for SRA (Security and Risk Analysis), Cyber-security students to ensure secure access to an intelligent analysis of data in complex business settings. Modern IT professionals should be able to guide a company in the best use of the diverse database-related technologies and applications for the “Big Data” era.

As such, IST 210 aims to prepare students for obtaining a fundamental understanding of the concepts and practical skills to analyze and implement a well-defined relational database design. In particular, IST 210 provides an introduction to physical database design, data modeling, relational model, logical database design, SQL query language, and instructors’ choices on database applications and advanced concepts. Students will learn to use a real-world commercial or open-source database management system, too. Upon taking IST 210, students should be able to understand the implications and future directions of databases and database technologies.

This course was offered by me at Penn State IST in Spring 2017, Fall 2017, Sprng 2018.

IST140: Introduction to Application Development

This is a first course in programming principles for application development. The course will focus on application development foundations including: fundamental programming concepts; basic data types and data structures; problem solving using programming; basic testing and debugging; basic computer organization and architecture; and fundamentals of operating systems. This is a hands-on course designed to help students learn to program a practical application using modern, high-level languages.

This course was offered by me at Penn State IST in Fall 2017, Sprng 2018.

IST441: Information Retrieval and Search Engines

This course is intended to prepare students to understand, design, develop and use information retrieval and search systems. The course will cover: organization, representation, and access to information; categorization, indexing, and content analysis; data structures for unstructured data; design and maintenance of such data structures, indexing and indexes, retrieval and classification schemes; use of codes, formats, and standards; analysis, construction and evaluation of search and navigation techniques; and search engines and how they relate to the above. Students will build a specialty web search engine using open source web tools and focused web crawling.

I co-taught this course with Dr. C. Lee Giles at Penn State IST in Sprng 2015, Spring 2016, Spring 2017. The official course page of IST441 is here.