AZ

Aidong Zhang

Professor
Unit: School of Engineering and Applied Science
Department: Department of Computer Science
Office location and address
85 Engineers Way
Charlottesville, Virginia 22903
IIS: Medium: Knowledge-Guided Meta-Learning for Multi-Omics Survival Analysis
Source: U.S. National Science Foundation (NSF)
October 01, 2021 – September 30, 2024
Collaborative Research: III: Medium: Mining and Leveraging Knowledge Hypercubes for Complex Applications
Source: U.S. National Science Foundation (NSF)
October 01, 2020 – September 30, 2023
III: Small: Multimodal Machine Learning for Data with Incomplete Modalities
Source: U.S. National Science Foundation (NSF)
October 01, 2020 – September 30, 2023
EAGER: Toward Interpretation of Pairwise Learning
Source: U.S. National Science Foundation (NSF)
September 01, 2019 – August 31, 2022
Collaborative Research: Knowledge Guided Machine Learning: A Framework for Accelerating Scientific Discovery
Source: U.S. National Science Foundation (NSF)
September 01, 2019 – August 31, 2022
III: Medium: High-Dimensional Interaction Analysis in Bio-Data Sets
Source: U.S. National Science Foundation (NSF)
January 03, 2019 – July 31, 2020
CS 4980: Capstone Research
Credits: 1–3
This course is one option in the CS fourth-year thesis track. Students will seek out a faculty member as an advisor, and do an independent project with said advisor. Instructors can give the 3 credits across multiple semesters, if desired. This course is designed for students who are doing research, and want to use that research for their senior thesis. Note that this track could also be an implementation project, including a group-based project. Prerequisite: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major
CS 4993: Independent Study
Credits: 1–3
In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty. The study is often either a thorough analysis of an abstract computer science problem or the design, implementation, and analysis of a computer system (software or hardware). Prerequisite: Instructor permission.
CS 4998: Distinguished BA Majors Research
Credits: 3
Required for Distinguished Majors completing the Bachelor of Arts degree in the College of Arts and Sciences. An introduction to computer science research and the writing of a Distinguished Majors thesis. Prerequisites: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major
SYS 6018: Data Mining
Credits: 3
Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining. Prerequisite: SYS 6021, SYS 4021, or STAT 5120.
CS 6190: Computer Science Perspectives
Credits: 1–3
This 'acclimation' seminar helps new graduate students become productive researchers. Faculty and visitors speak on a wide variety of research topics, as well as on tools available to researchers, including library resources, various operating systems, UNIX power tools, programming languages, software development and version control systems, debugging tools, user interface toolkits, word processors, publishing systems, HTML, JAVA, browsers, Web tools, and personal time management. Prerequisite: CS graduate student or instructor permission.
CS 6316: Machine Learning
Credits: 3
This is a graduate-level machine learning course. Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory. Prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills.
CS 6501: Special Topics in Computer Science
Credits: 3
Course content varies by section and is selected to fill timely and special interests and needs of students. See CS 7501 for example topics. May be repeated for credit when topic varies. Prerequisite: Instructor permission.
BME 6550: Special Topics in Biomedical Engineering
Credits: 1–3
Applies engineering science, design methods, and system analysis to developing areas and current problems in biomedical engineering. Topics vary by semester.
CS 6890: Industrial Applications
Credits: 1
A graduate student returning from Curricular Practical Training can use this course to claim one credit hour of academic credit after successfully reporting, orally and in writing, a summary of the CPT experience to his/her academic advisor.
CS 6993: Independent Study
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
CS 7993: Independent Study
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
CS 7995: Supervised Project Research
Credits: 3
Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.
CS 8897: Graduate Teaching Instruction
Credits: 1–12
For master's students who are teaching assistants.
CS 8999: Thesis
Credits: 1–12
Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.
CS 9897: Graduate Teaching Instruction
Credits: 1–12
For doctoral students who are teaching assistants.
CS 9999: Dissertation
Credits: 1–12
Formal record of student commitment to doctoral research under the guidance of a faculty advisor. May be repeated as necessary.