YQ
Unit: School of Engineering and Applied Science
Department: Department of Computer Science
Office location and address
Rice Hall, Room 503
85 Engineers Way
Charlottesville,
Virginia
22903
Education
B.S. Tsinghua University, Beijing, China, 2000
M.S. Carnegie Mellon University, Pittsburgh, USA, 2003
Ph.D. Carnegie Mellon University, Pittsburgh, USA, 2008
M.S. Carnegie Mellon University, Pittsburgh, USA, 2003
Ph.D. Carnegie Mellon University, Pittsburgh, USA, 2008
Biography
Yanjun Qi is an assistant professor of University of Virginia, Department of Computer Science since 2013. She was a senior researcher in the Machine Learning Department at NEC Labs American, Princeton, NJ from July 2008 to August 2013. Her research interests are within machine learning, data mining, and bioinformatics. She obtained her Ph.D. degree from School of Computer Science at Carnegie Mellon University in May 2008 and received her Bachelor degree with high honors from Computer Science Department at Tsinghua University, Beijing. She has served as PCs and reviewers for multiple reknowned international conferences/ journals, and has co-chaired the NIPS “Machine Learning for Computational Biology” workshop. Dr. Qi has received CAREER award from NSF and a Best Paper Award at International Conference of BodyNet.
Publications
Sponsored Awards
SHF: Medium: Rearchitecting Neural Networks for Verification
Source: U.S. National Science Foundation (NSF)
July 01, 2019 – June 30, 2023
DHHS IPA Assignment
Source: U.S. NIH Institute on Aging
January 04, 2021 – December 31, 2021
Robust and Resilient Deep Learning Systems
Source: Intel Corporation
December 01, 2017 – November 30, 2021
Machine Learning into Distributed Job Scheduling & Management
Source: Lancium LLC
September 01, 2019 – August 31, 2020
EN-CS CAREER: A Data-Driven Network Inference Framework for Context-Conditioned Protein Interaction Graphs
Source: U.S. NSF - Directorate Computer & Info. Sciences
August 15, 2015 – July 31, 2020
EN-CS-TWC: Small Automatic Techniques for Evaluating and Hardening Machine Leraning Classifiers in the Presence of Adversaries
Source: U.S. NSF - Directorate Computer & Info. Sciences
September 01, 2016 – August 31, 2019
EN-SE Deep Learning of Passage Structure for Scalable Semantic Discovery
Source: U.S. DOD - Navy - Office Of Naval Research (Onr)
June 10, 2015 – September 09, 2018
Courses
Credits: 1–3
Content varies annually, depending on instructor interests and the needs of the department. Similar to CS 5501 and CS 7501, but taught strictly at the undergraduate level. Prerequisite: Instructor permission; additional specific requirements vary with topics.
Credits: 3
An introduction to machine learning: the study of algorithms that improve their performance through experience. Covers both machine learning theory and algorithms. Introduces algorithms, theory, and applications related to both supervised and unsupervised learning, including regression, classification, and optimization and major algorithm families for each. Prerequisites: CS 2150; and either Math 3350 or APMA 3080; and one of APMA 3100, APMA 3110, MATH 3100, or equivalent.
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 with a grade of C- or higher
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.
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 with a grade of C- or higher and CS BA major status.
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.
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.
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.
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
Credits: 3
Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.
Credits: 3
Special Topics in Computer Science
Credits: 1–12
For master's students who are teaching assistants.
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.
Credits: 1–12
For doctoral students who are teaching assistants.
Credits: 1–12
Formal record of student commitment to doctoral research under the guidance of a faculty advisor. May be repeated as necessary.
Honors
CAREER award from NSF 2015
Best Paper Award at International Conference of BodyNet 2014