Spring 2024 CS Course Listings

This file contains the Spring 2024 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP 5111
Course title: Fundamentals of Software Analysis
Instructor: Prof. Shing-Chi Cheung
Room: 2534
Telephone: 2358-7016
Email:
WWW Page: https://course.cse.ust.hk/comp5111/

Area in which course can be counted: Software Engineering and Programming Languages (SEPL)

Course description:
The goal of this course is to introduce how various analysis techniques can be used to manage software code quality. Students will acquire fundamental knowledge of fault analysis, test coverage, program instrumentation, test generation, fault localization, mutation analysis, program repair, pointer analysis, program abstraction, mining software repositories and vulnerability analysis. The course will also discuss how to carry out the empirical experimentation. Wherever applicable, concepts will be complemented by tools developed in academia and industry. This enables students to understand the maturity and limitations of various testing and analysis techniques. The course requires prior programming knowledge in Java.

Course objective:
Students will attain the following on completion of the course:
* knowledge of the software quality assurance;
* understanding of principles of software testing and analysis;
* ability to deploy software quality measures to real life projects;
* hands-on experience in applying software analysis and testing tools.

Course outline/content (by major topics):
Software Testing, Program Analysis, Fault Diagnosis, Software Tool Automation, Large Language Models, Vulnerability Analysis, Empirical Experimentation

Textbooks:

Reference books/materials:
* Conferences: Proceedings of ICSE, FSE, PLDI, OOPSLA, ISSTA and ASE.
* Journals: ACM TOSEM & IEEE TSE.
* Software Engineering, Ivan Marsic, Rutgers University, 2012.
* Introduction to Software Testing, Paul Ammann and Jeff Offutt, Cambridge University Press, 2008.
* Software Testing and Analysis: Process, Principles and Techniques, Mauro Pezze and Michal Young, John Wiley and Sons, 2007.
* How Google Tests Software, James A. Whittaker, Jason Arbon, Jeff Carollo, Addison-Wesley, 2012.
* Head First Java, Kathy Sierra and Bert Bates, O'Reilly Media, Inc.

Grading scheme:
Programming Assignments: 20%
Reading Assignment: Report, Presentation & Participation: 10%
Examination: 70%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5212
Course title: Machine Learning
Instructor: Dr. Junxian He
Room: 3512
Telephone: 2358-8765
Email:
WWW Page: https://jxhe.github.io/teaching/comp5212s24

Area in which course can be counted: Artificial Intelligence (AI)

Course description:
This course covers core and recent machine learning algorithms. Topics include supervised learning algorithms (linear regression, logistic regression, generative models for classification, support vector machines), unsupervised learning (K-Means, mixture models, expectation maximization), deep learning, and reinforcement learning (classic RL, deep RL). The course assumes students have a solid grasp of probabilities, linear algebra, and python programming. The assignments and final projects will require proficient programming skills.

Background:
Computer science: object-oriented programming and data structures, design and analysis of algorithms; Mathematics: multivariable calculus, linear algebra and matrix analysis, probability and statistics.

Course objective:
Upon successful completion of the proposed course, students will be able to:
* Gain an overview of Machine Learning as a subject of study;
* Gain an understanding of the fundamental issues and principles in machine learning;
* Gain an understanding of core and recent machine learning algorithms;
* Gain an ability to apply core and recent machine learning algorithms to solve real-world problems.

Course outline/content (by major topics):
* Introduction to Machine Learning
* Basics of Probability/Information Theory
* Supervised Learning
    * Polynomial Regression and Basic ML Issues
    * Logistic Regression and Basic Optimization
    * Generative Models and Naive Bayes
    * The Bias-Variance Decomposition
    * Support Vector Machines
* Deep Learning
    * Deep Feedforward Networks
    * Convolutional Neural Networks
    * Recurrent Neural Networks
    * Transformer and BERT
* Unsupervised Learning
    * K-Means clustering
    * Expectation maximization
    * Variational autoencoders
    * Generative adversarial networks
* Reinforcement Learning
    * Introduction to RL
    * Value-Based Deep RL
    * Policy-Based Deep RL
* Large Language Models
    * Language Modeling
    * Prompting
    * Application

Textbooks:

Reference books/materials:
o Andrew Ng. Lecture Notes on Machine Learning. Stanford. https://cs229.stanford.edu/syllabus.html
o I Goodfellow, Y Bengio, A Courville (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/

Workload and Grading:
o 4 Assignments: (40%)
o Final Group Project: (40%)
o Final Examination: (20%)

Available for final year UG students to enroll: PG students have higher priority to enroll in this course. UG students will be considered only if there are spare quota.

Minimum CGA required for UG students: N/A

Course code: COMP 5214
Course title: Advanced Deep Learning Architectures
Instructor: Dr. Qifeng Chen
Room: 3519
Telephone: 2358-8838
Email:
WWW Page:

Area in which course can be counted: Artificial Intelligence (AI)

Course description:
This course focuses on advanced deep learning architectures and their applications in various areas. Specifically, the topics include various deep neural network architectures with applications in computer vision, signal processing, graph analysis, and natural language processing. Different state-of-the-art neural network models will be introduced, including graph neural networks, normalizing flows, point cloud models, sparse convolutions, and neural architecture search. The students have the opportunities to implement deep learning models for some AI-related tasks such as visual perception, image processing and generation, graph processing, speech enhancement, sentiment classification, and novel view synthesis.

Course objective:
This course aims to achieve the following objectives:
* the students can have a broad knowledge of up-to-date advanced deep learning models in different areas;
* the students can utilize the deep learning architectures and techniques to solve the given programming assignments;
* the students can understand what problems can be addressed by deep learning models and solve a practical problem with deep learning through a course project;
* and the students can work in a team on a course project and present the work together.

Course outline/content (by major topics):
* Overview of deep learning: Basic architectures (CNN, RNN), Backpropagation, Loss functions
* Neural networks for image and video recognition tasks
* Neural networks for image and video processing tasks
* Deep 3D learning for point clouds, meshes, and volumetric data
* Deep 3D learning for stereo and multi-view data
* Graph neural networks for graph processing and analysis
* Sequential modeling and signal processing
* Deep generative models: Normalizing flow, GAN, Diffusion Models
* Efficient neural networks and Neural architecture search

Textbooks:

Reference books/materials:
o Zhang, A., Lipton, Z.C., Li, M. and Smola, A.J., 2019. Dive into deep learning. https://d2l.ai
o Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y., 2016. Deep learning (Vol. 1, No. 2). Cambridge: MIT press.

Workload and Grading:
o Presentation: 5%
o Homework: 30%
o Final project: 30%
o Midterm: 35%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5221
Course title: Natural Language Processing
Instructor: Dekai Wu
Room: 3556
Telephone: 2358-6989
Email:
WWW Page:

Area in which course can be counted: Artificial Intelligence (AI)

Course description:
Language modeling from basics to LLMs. Techniques for parsing, interpretation, context modeling, generation. How neural and statistical approaches interact with linguistic constraints. Applications include machine translation, dialogue chatbots, cognitive modeling, and knowledge acquisition.

Background: COMP 3211

Exclusion(s): COMP 4221

Course objective:
(TBA)

Course outline/content (by major topics):
(TBA)

Textbooks:
(TBA)

Reference books/materials:
(TBA)

Grading scheme:
30% - 3 to 4 assignments
5% - participation (in class and on forum)
15% - midterm
20% - final
30% - project

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5311
Course title: Database Architecture and Implementation
Instructor: Prof. Dimitris Papadias
Room: 3555
Telephone: 2358-6971
Email:
WWW Page: https://cse.hkust.edu.hk/~dimitris/5311/5311.html

Area in which course can be counted: Data, Knowledge and Information Management (DB)

Course description:
The course is divided in two parts: (i) background material taught by the instructor, (ii) specialized topics presented by students. In addition to their presentation, students have to submit a survey on the topic by the end of the semester.

Exclusion(s): COMP 3311

Course objective:
Introductory database class for graduate student that includes the relational model and SQL, disk and memory management, access methods, implementation of relational operators, query processing and optimization, transaction management and recovery. Moreover, students are expected to acquire presentation and writing skills.

Course outline/content (by major topics):
* E/R Model
* Relational Model and Algebra
* SQL
* Functional Dependencies and Relational Database Design
* File Systems
* Tree and Hash Indexes
* Query Processing and Implementation of Relational Operators
* Query Optimization
* Transactions

Textbooks:
Database System Concepts, A. Silberschatz, H. Korth, and S. Sudarshan.

Reference books/materials:
Database Management Systems, Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
50% final exam, 20% presentation, 20% survey, 10% class participation

Available for final year UG students to enroll: No (UG students are encouraged to take COMP 3311)

Course code: COMP 5411
Course title: Advanced Computer Graphics
Instructor: Prof. Chiew-Lan Tai and Prof. Pedro Sander
Room: 3515 (Prof. Chiew-Lan Tai); 3504 (Prof. Pedro Sander)
Telephone: 2358-7020 (Prof. Chiew-Lan Tai); 2358-6983 (Prof. Pedro Sander)
Email: ,
WWW Page: https://cse.hkust.edu.hk/~taicl/ (for Prof. Chiew-Lan Tai) and https://cse.hkust.edu.hk/~psander/ (for Prof. Pedro Sander)

Area in which course can be counted: Vision and Graphics (VG)

Course description:
Computer Graphics studies the principles of generating and displaying 3D images on the computer display. This course consists of two parts. The first part covers advanced topics in modeling and processing geometric shapes, and the second part covers topics on geometry rendering, lighting, and shading, using latest generation graphics hardware.

Exclusion(s): CSIT 5400

Background: COMP 3711, Linear Algebra, Calculus

Course outline/content (by major topics):
* Basics of Computer Graphics
* Bezier and B-spline curves and surfaces
* Space-based and surface-based deformation
* Mesh simplification
* Mesh smoothing
* Graphics Processing Unit (GPU)
* Programmable Rendering Pipeline (Vertex, Geometry, and Pixel shaders)
* Surface lighting and shading
* Real-time shadow algorithms
* Global illumination
* Future trends on GPU computing

Textbooks:
Dave Shreiner. OpenGL Programming Guide. Seventh Edition. Adisson Wesley. (optional reference book)

Grading scheme:
Part 1 (Geometry) : Programming Assignment and Project (50%), Written Assignment (10%), Exam (40%)
Part 2 (Rendering) : Programming Assignment and Project (55%), Exam (45%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5421
Course title: Computer Vision
Instructor: Quan Long
Room: 3506
Telephone: 2358-7018
Email:
WWW Page: https://cse.hkust.edu.hk/~quan/

Area in which course can be counted: Vision and Graphics (VG)

Course description:
Introduction to techniques for automatically describing visual data and tools for image analysis; perception of spatial organization; models of general purpose vision systems; computational and psychological models of perception.

Background: COMP 3211, knowledge in linear algebra.

Course objective:
Same as listed in the course catalogue.

Course outline/content (by major topics):
1 Introduction
2 Image formation
3 Image filtering
4 Edge detection
5 Segmentation
6 Segmentation II
7 Texture
8 Projective geometry (handout)
9 Image warping
10 Stereo
11 Disparity by graph-cut
12 Surface from Stereo (Tensor voting)
13 Multiview stereo
14 Light
15 Photometric stereo
16 Optical flow
17 Structure from Motion

Textbooks:
Computer Vision : A Modern Approach, D. Forsyth and J. Ponce

Reference books/materials:
* Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
* Robot Vision, B.K.P. Horn, MIT Press, 1986 * A Guided Tour of Computer Vision, V. S. Nalwa, Addison Wesley, 1993
* Machine Perception, R. Nevatia, Prentice-Hall, 1982
* Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice-Hall, 2001
* Machine Vision, R. Jain, R. Kasturi, and B.G. Schunck, McGraw-Hill, 1995
* Computer and Robot Vision vol. 2, R. Haralick and L. Shapiro, Addison-Wesley, 1992
* Object Recognition by Computer - The Role of Geometric Constraints, W.E.L.Grimson, MIT Press, 1990
* The Eye, the Brain and the Computer, Fischler and Firschein, Addison-Wesley, 1987
* Computer Vision, D. Ballard and C. Brown, Prentice-Hall, 1982
* Vision, David Marr, Freeman, 1982
* Digital Picture Processing, A. Rosenfeld and A. Kak, Academic Press, 1982

Grading scheme:
The breakdown is subject to change as a whole and adjustments on a per-student basis in exceptional cases. This is the general breakdown we'll be using for Scheme 1:
Projects: 64%
Homeworks: 4%
Final Exam (Oral): 32%
Grading Scheme 2 targets at students in other research areas who need to fulfil the Vision/Graphics core requirement. The tentative breakdown for students signing up for Scheme 2 is as follow:
Project #1 and Papers Critique: 26%
Homeworks: 4%
Final Exam (Written): 70%
The two schemes will be described during the first and/or second lecture in September. Computer projects and papers critique will be done in teams up to two students (three-student team is not permitted).
Homeworks are to be completed individually. Though you may discuss the problems with others, your answers must be your own.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A- and permission of the instructor

Course code: COMP 5422
Course title: Advanced Topics in 2D and 3D Deep Visual Scene Understanding
Instructor: Dr. Dan Xu
Room: 3509
Telephone: 2358-8837
Email: >
WWW Page: https://www.danxurgb.net/

Area in which course can be counted: Vision and Graphics (VG)

Course description:
Visual scene understanding is a fundamental field for advanced application scenarios such as autonomous driving and robotics. This course majorly focuses on delivering deep learning-based visual scene understanding techniques in both 2D and 3D perspectives. In the 2D part, we introduce topics including image and scene classification, semantic segmentation, object detection, and multi-task scene understanding. In the 3D part, we show how 3D scene understanding can be performed through learning from 2D inputs, involving topics such as scene depth estimation, camera pose prediction, Implicit Scene Representations, 3D scene reconstruction, and visual SLAM. Several representative deep scene understanding architectures and frameworks in supervised or self-supervised settings together with the 2D/3D tasks are also presented in the course.

Background:
Instructor's approval is required for undergrad students to register the course for credits. There are no strict prerequisites for this course. But basic knowledge about computer vision and deep learning fundamentals are beneficial and necessary.

Course objective:
The objectives of the course are to help the students:
(i) obtain basic knowledge of visual scene understanding techniques for various intelligent applications in autonomous driving and robotics.
(ii) learn fundamentals in deep learning-based architectures/frameworks for several important 2D and 3D visual scene understanding tasks.
(iii) gain a sense of current research and development trends in academia and industry in the domain of visual scene understanding.
(iv) learn skills of digging into and presenting for research papers published at top conferences/journals.

Course outline/content (by major topics):
Basic deep network architecture and learning, common 2D and 3D computer-vision-based scene understanding tasks including scene depth estimation, 2D/3D object detection, semantic segmentation, 3D scene reconstruction, visual SLAM etc., and the visual scene understanding techniques for real-world large-scale application scenarios such as autonomous driving and robotics.

Reference books/materials:
* Multi-View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman, Cambridge University Press, 2004.
* Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016.
* Computer Vision: Algorithms and Applications, Richard Szeliski, Springer.

Grading scheme:
Assignments 40%, Final project 30%, Final Exam 30%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: 3.7 and permission of the instructor

Course code: COMP 5423
Course title: Deep Learning for Medical Image Analysis
Instructor: Dr. Hao CHEN
Room: 3524
Telephone: 2358-8346
Email:
WWW Page: https://cse.hkust.edu.hk/~jhc/

Area in which course can be counted: Artificial Intelligence (AI) or Vision and Graphics (VG)

Course description:
Nowadays medical image analysis is rapidly growing and plays an indispensable role in healthcare. Recent advances of deep learning techniques have made significant breakthroughs in medical image analysis applications. This course will cover fundamental knowledge of medical imaging and various medical image analysis tasks, including computer-aided detection, segmentation, diagnosis and prognosis. Deep learning methods for solving these tasks will be introduced and state-of-the-art methods will be discussed. The remaining significant challenges and limitations will also be presented, including large-scale training of foundation models, deep learning with interpretation and generalization issues, multimodal data integration for precision oncology, etc. This course will equip students with practical knowledge of medical imaging and analysis with deep learning techniques.

Background:
Instructor's approval is required for undergraduate students to register the course for credits. Basic knowledge about image processing and machine learning are beneficial.

Course objective:
The objectives of the course are to help the students:
* Obtain the basic knowledge of medical imaging techniques and various medical image analysis tasks.
* Learn the fundamentals in deep learning methods for medical imaging and analysis.
* Master and apply the skills of deep learning technologies in medical image analysis tasks, including computer-aided detection, diagnosis and prognosis, etc.
* Gain the current research and development trends in both academia and industry in the domain of medical imaging and analysis.

Course outline/content (by major topics):
1. Introduction to Medical Image Analysis;
2. Fundamentals of Deep Learning;
3. Medical Image Classification;
4. Medical Image Segmentation;
5. Medical Image Registration;
6. Label-efficient Learning in MIA;
7. Anomaly Detection in MIA;
8. Attention Mechanism in MIA;
9. Interpretability in MIA;
10. Domain Generalization/Adaptation in MIA;
11. Federated Learning with Privacy-preserving;
12. Multimodal Learning for Precision Oncology;
13. Foundation models in MIA;
14. Advances and Applications.

Reference books/materials:
Toennies, Klaus D. Guide to medical image analysis. Springer London, 2017.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Dhawan, Atam P. Medical image analysis. Vol. 31. John Wiley & Sons, 2011.
Zhou, S. Kevin, Hayit Greenspan, and Dinggang Shen, eds. Deep learning for medical image analysis. Academic Press, 2017.

Grading scheme:
Assignments 20%; Final project 60%; Final Exam 20%.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: B

Course code: COMP 5631
Course title: Cryptography and Security
Instructor: Prof. Cunsheng Ding
Room: 2533
Telephone: 2358-7021
Email:
WWW Page: https://cse.hkust.edu.hk/faculty/cding/

Area in which course can be counted: Software Engineering and Programming Languages (SEPL)

Course description:
This course gives an in depth coverage of the theory and applications of cryptography, and system security. In the part about cryptography, basic tools for building security systems are introduced. The system security part includes electronic mail security, IP security, Web security, VPNs, and firewalls.

Course objective:
After completion of this course, students will display a breadth of knowledge of both the principles and practice of cryptography and systems security, master basic tools for building security systems, and get familiar with real-world security systems.

Course outline/content (by major topics):
History of cryptography, classical ciphers, design and analysis of block ciphers and stream ciphers, public-key cryptography, hash functions, keyed hash functions, digital signature schemes, user and data origin authentication, data integrity, nonrepudiation, Key management, public key infrastructure, cryptographic protocols, email security, Web security, network security, Secure Shell, VPNs, distributed systems security.

Textbooks:
No textbook, but lecture slides will be posted online.

Reference books/materials:
* W. Stallings, Cryptography Theory and Network Security, Fourth/Fifth Edition, Pearson Education.

Grading scheme:
Assignments and final examination.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A-

Course code: COMP 5713
Course title: Computational Geometry
Instructor: Prof. David Mount
Room: 3533
Telephone: 2358-8340
Email:
WWW Page:

Area in which course can be counted: Theoretical Computer Science (TH)

Course description:
An introductory course in Computational Geometry. Algorithms for manipulating geometric objects. Topics include Convex Hulls, Voronoi Diagrams, Point Location, Triangulations, Randomized Algorithms, Point-Line Duality.

Background: COMP 3711

Course objective:
To introduce postgraduate students to the area of computational geometry, the fundamental results and algorithms in the area.

Course outline/content (by major topics):
(1) Convex hulls,
(2) plane sweep,
(3) polygon triangulation,
(3) linear programming,
(4) trapezoidal maps and planar point location,
(5) Delaunay triangulations,
(6) Voronoi diagrams,
(7) point-line duality and line arrangements,
(7) geometric data structures and range searching,
(8) well-separated pair decompositions,
(9) VC-dimension and epsilon-nets, and
(10) motion planning.

Reference books/materials:
Computational Geometry: Algorithms and Applications (3rd Edition), M. de Berg, O.
Cheong, M. van Kreveld, and M. Overmars, Springer, 2008.

Grading scheme:
* 35% written assignments
* 25% midterm exam
* 40% final exam

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 5911 (co-list with COMP 4911/ENTR 4911)
Course title: Entrepreneurial Me
Instructor: Prof. Gary Chan
Room: 2539
Telephone: 2358-6990
Email:
WWW Page: https://course.cse.ust.hk/comp4911/

Area in which course can be counted: Networking and Computer Systems (NE)

Course description:
While entrepreneurship is a career choice, its mindset is for everyone. This is a course covering the mindset and elements of founding new and innovative business ventures in information technology sector. Topics include the entrepreneurial risk-taking value-creation mindset, market identification and go-to-market strategies, business models and development, business plan, fundraising and investment, role and protection of intellectual properties, technology-market gap and product-market fit, and growth and exit strategies. Case studies of successful and unsuccessful ventures will be discussed. In-class student participation and presentation are expected. Business and non-engineering students interested in starting IT-related companies are also welcome. Research postgraduate students are encouraged to develop proof-of-concept prototypes and business plans based on their research findings.

Background:
Any postgraduate students from all schools who are interested in starting up IT-related ventures. RPGs (MPhil and PhD candidates) of any majors with research topics are especially welcome.

Course objective:
After successful completion of the course students should know what is required to start a successful IT business. They will learn how to write a business plan to attract funding, the elements to form a winning team of people, understand and follow legal requirements, the steps required to move the company from the start-up phase to operating phase, and the factors to consider to exit and reap the rewards. They will also learn how to make succinct presentations of their ideas and communicate effectively in a team setting.

Course outline/content (by major topics):
* The entrepreneurial risk-taking value-creation mindset
* Business analysis and plan
* Business model development
* The role and protection of innovations
* VC, financing, fund raising and share structuring
* From 0 to exit: Building up your company
* Pitching and marketing your products
* Case studies and discussions

Reference books/materials:
N/A

Grading scheme:
3 Group projects (50%)
Lecture attendance (16%, attending 16 out of the 20 eligible lectures),
Class participation (8%),
Presentation voting (3%),
Seminar/Talk reports and/or competitions (12%), and
In-class written exam (11%).

Available for final year UG students to enroll: No. UG should enroll into COMP 4911/ENTR 4911

Minimum CGA required for UG students: N/A

Course code: COMP 6411C
Course title: Advanced Topics in Multimodal Machine Learning
Instructor: Dr. Long Chen
Room: 3508
Telephone: 2358-8836
Email:
WWW Page: https://cse.hkust.edu.hk/~longchen/

Area in which course can be counted: Vision and Graphics (VG)

Course description:
This course provides a comprehensive introduction to recent advances in multimodal machine learning, with a focus on vision-language research. Major topics include multimodal translation, multimodal reasoning, multimodal alignment, multimodal information extraction, and recent deep learning techniques in multimodal research (such as graph convolution network, Transformer architecture, deep reinforcement learning, and causal inference). The course structure will primarily consist of instructor presentation, student presentation, in-class discussion, and a course final project.

Background:
Machine learning basics, deep learning basics, computer vision basics

Course objective:
After completion of this course, students will understand mainstream multimodal topics and tasks, and develop their critical thinking and problem solving, such as identifying and explaining the state-of-the-art approaches for multimodal applications.

Course outline/content (by major topics):
Unimodal Representation for Vision and Text
Multimodal Translation
Multimodal Reasoning
Multimodal Alignment
Multimodal Information Extraction
Recent Architectures in Multimodal Research
Recent Techniques in Multimodal Research
Research Trends in Multimodal Research

Reference books/materials:
Conferences: Proceedings of CVPR/ICCV/ECCV, ICLR/ICML/NeurIPS, ACL/EMNLP/ACM Multimedia
Book: Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.

Grading scheme:
Class attendance and in-class discussion: 20%
Project presentation: 30%
Final project report: 50%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor

Course code: COMP 6511B
Course title: Advanced Software Testing
Instructor: Dr. Dongdong She
Room: 3505
Telephone: 2358-7029
Email:
WWW Page: https://cse.hkust.edu.hk/~dongdong/

Area in which course can be counted: Software Engineering and Programming Languages (SEPL)

Course description:
Software vulnerabilities profoundly impact our daily lives, from global ransomware attacks to various sensitive information leakage. Software testing is a program analysis technique to discover these vulnerabilities. This course will cover classic software techniques such as fuzzing, symbolic execution, and formal methods. The latest trend of leveraging machine learning (i.e., LLM, ChatGPT) to assist software testing and neural-symbolic software testing are also included.

Background:
COMP 3633 Principles of Cybersecurity, COMP 4211 Machine Learning (optional), or equivalents of these two courses.

Course objective:
The general goal of this course is to help you gain a solid understanding of software testing techniques such as fuzzing, symbolic execution, and formal methods. Then, students can learn the most recent works of software testing techniques and ML-based software testing.

Course outline/content (by major topics):
This course will center around paper readings, presentations, discussions, and a final project. The course readings include a list of research papers selected from top security, software engineering, systems, and programming language conferences. We will discuss roughly two papers every class meeting. For the in-depth discussions to be possible, you will have to read the papers carefully before class.

Topics covered in this course:
1. Basics of program analysis
2. Static analysis & Abstract interpretation
3. Symbolic execution
4. ML-assisted symbolic execution
5. Fuzzing
6. ML-assisted fuzzing
7. LLM-assisted fuzzing
8. Formal method
9. Neural-symbolic program analysis

Reference books/materials:
This course will focus on paper readings, and no textbook is required. The paper list will be updated on the course page.

Grading scheme:
40%: Class participation. To encourage in-depth discussion, 40% of the grade will be assigned to in-class participation (20%) and paper presentation (20%).
60%: Final project. Project proposal (15%), midterm demo (15%), final report (30%)

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: N/A

Please visit Class Schedule & Quota (Spring 2024) for the timetable and quota.

Archive of past courses

Last modified on 2024-01-22.