Faculty Directory
Jingling Li headshot
JL

Jingjing Li

Associate Professor
Unit: McIntire School of Commerce
Department: McIntire School of Commerce
Office location and address
Rouss & Robertson Halls, Room 326
125 Ruppel Dr
Charlottesville, Virginia 22904
Education
Ph.D., Management Information Systems, University of Colorado at Boulder
B.S., Management Information Systems, Beijing Jiaotong University
Biography

Professor Li’s research interests relate to machine learning and big data analytics, with applications in information extraction, text mining, social media, search and relevance, and recommender systems. Before joining the McIntire School, she was a Scientist at Microsoft, where she proposed and implemented several large-scale machine learning solutions for numerous Microsoft products such as Xbox One, Windows 8 Search Charm, Windows Phone App Store, Cortana, and Bing Entity Search.

Professor Li has presented her work at several national and international conferences, top IT companies, and leading business schools. While pursuing her Ph.D., she taught several courses pertaining to data mining and business analytics, and received a teaching award for her "Business Intelligence" course at the Leeds School of Business. She is a member of the Association for Information Systems and the IEEE (Institute of Electrical and Electronics Engineers).

Recent courses as listed in the Student Information System (SIS) Course Catalog:
COMM 4260: Business Analytics
Credits: 3
Business analytics leverages the vast data resources available today to identify trends and patterns that are critical to enhancing business performance. This course introduces students to contemporary business analytics methods, including predictive and descriptive analytics techniques, and demonstrates how to practically apply analytics to real-world business decisions. Prerequisite: 4th Year Commerce Student or Instructor Permission
COMM 4261: Big Data
Credits: 2
Course provides an overview of the 4Vs of Big Data:volume, variety, & veracity. Through a group project, labs,& individual exercises, students learn the important implications of the 4Vs for data in-rest & data in-motion. Students use Hadoop-based software packages such as IBM Infosphere to derive business insights from large quantities of search, clickstream,& social media content encompassing millions of structured & unstructructured documents.
COMM 4559: New Course in Commerce
Credits: 1–4
This course provides the opportunity to offer new topics in the subject of Commerce.
DS 6999: Independent Study
Credits: 1–12
Graduate-level independent study conducted under the supervision of a specific instructor(s)
GBAC 7216: Managing Big Data
Credits: 1
This course will introduce enterprise data management practices and techniques for big data environments. Key concepts covered include comparing and contrasting big data from traditional structured relational data and SQL versus NoSQL technologies. The course will also explore reference architectures that can facilitate development of data management infrastructures for analytics projects that leverage big data.
GCOM 7280: Big Data
Credits: 2
This course provides an overview of the characteristics of big data and introduces state-of-the-art NoSQL technologies for managing operational and analytical data. Students will learn techniques (e.g., Hadoop, columnar database, and python) for big data management, and design data architectures for big data-enabled applications, including search engines, recommender systems, and artificial intelligence.
GCOM 7993: Independent Study and Supervised Research
Credits: 1–9
Students taking this course will explore areas and issues of special interest that are not otherwise covered in the graduate curriculum. This course is offered at the discretion of the supervising professor.