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Lv, Qin (Christine) Professor

Positions

Research Areas research areas

Research

research overview

  • Qin Lv's research focuses on full-stack data analytics, which integrates systems, algorithms, and applications for effective and efficient data analytics in ubiquitous computing and scientific discovery. Topics of interest include mobile/wearable/IoT sensing, data fusion, spatial-temporal data analysis, anomaly detection, recommendation, behavior analysis, social networks, cybersafety. Her research is interdisciplinary in nature and interacts closely with a wide range of application domains including Earth sciences, transportation electrification, renewable and sustainable energy, environmental science, and information needs in people’s daily lives.

keywords

  • full-stack data analytics, integration of systems, algorithms & applications for effective & efficient data analytics in ubiquitous computing & scientific discovery, mobile/wearable/IoT sensing, data fusion, spatial-temporal data analysis, anomaly detection, recommendation, behavior analysis, social networks, cybersafety

Publications

selected publications

Teaching

courses taught

  • CSCI 4502 - Data Mining
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021
    Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as time-series, social networks, multimedia, and Web data. Same as CSCI 5502 and CSPB 4502.
  • CSCI 5000 - Introduction to the Computer Science Research-Based MS Program
    Primary Instructor - Fall 2020 / Fall 2021
    Instructs new research-based MS students in Computer Science how to become an effective member in terms of research, teaching, and presentation, and potentially advancing to the PhD program. Makes students aware of formal requirements, educational objectives, and research themes. Provides evaluative criteria and guidelines for all objectives to be achieved.
  • CSCI 5502 - Data Mining
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021
    Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as time-series, social networks, multimedia, and Web data. Same as CSCI 4502.
  • CSCI 6000 - Introduction to the Computer Science PhD Program
    Primary Instructor - Fall 2018 / Fall 2019
    Instructs new Ph.D students in Computer Science how to obtain a Ph.D and how to become an effective member of the computer science research community. Makes students aware of formal requirements, educational objectives, and research themes. Provides evaluative criteria and guidelines for all objectives to be achieved.
  • CSCI 6502 - Big Data Analytics: Systems, Algorithms, and Applications
    Primary Instructor - Spring 2020 / Spring 2021
    This course studies state-of-the-art practice and research on efficient and effective systems and algorithms design for managing and exploring massive amounts of digital data in various application domains. The course takes an integrated approach that studies all three aspects of big data analytics: systems, algorithms, and applications. Specifically, this course covers big data systems for MapReduce, NoSQL, stream processing, deep learning, mobile/wearable/IoT sensing, as well as practical use of indexing, sketching, recommendation, graph, and deep learning algorithms. Domain-specific data management and analysis, such as those in online social networks, scientific discovery, business intelligence, health informatics, urban computing, are also covered.
  • CSCI 7000 - Current Topics in Computer Science
    Primary Instructor - Spring 2018 / Spring 2019
    Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 8 total credit hours.
  • DTSA 5504 - Data Mining Pipeline
    Primary Instructor - Summer 2021 / Fall 2021
    This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehouse, data modeling, interpretation and evaluation, and real-world applications.
  • DTSA 5505 - Data Mining Methods
    Primary Instructor - Summer 2021 / Fall 2021
    This course covers core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier detection, as well as time-series mining and graph mining.
  • DTSA 5506 - Data Mining Project
    Primary Instructor - Fall 2021
    This course offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work.

Background

International Activities

global connections related to teaching and scholarly work (in recent years)

Other Profiles