POSTGRADUATE
PROGRAMS
PROGRAMS
Postgraduate Programs 2025/26
Master of Philosophy and Doctor of Philosophy Programs in Internet of Things
Award Title
Master of Philosophy in Internet of Things
Doctor of Philosophy in Internet of Things
Program Short Name
MPhil(IoT)
PhD(IoT)
Mode of Study
Both full- and part-time
Normative Program Duration
MPhil
Full-time: 2 years
Part-time: 4 years
PhD
Full-time: 3 years (with a relevant research master’s degree), 4 years (without a relevant research master’s degree)
Part-time: 6 years
Offering Unit
Internet of Things Thrust Area
Information Hub
Program Advisor
PG Programs Coordinator:
Prof Gareth TYSON, Assistant Professor of Internet of Things
Website
Enquiry
Internet of Things (IoT) is among the pillars in the “Internet Plus” action plan in China, presented in the Chinese Government’s work report in 2015. The Internet of Things Thrust Area under the Information Hub at HKUST(GZ) is established to grow HKUST’s strength in wireless communications and networking, and to contribute to the nation’s drive to become a world leader in IoT, covering research areas such as wireless communications, smart cities, digital healthcare, machine learning and optimization, web science, computer networking, social computing, privacy and security, and the wider societal aspects of IoT. The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Internet of Things are offered as an integral part of the endeavor to become a world-renowned IoT research center with the mission to advance applied research in IoT as well as fundamental research relevant to the application areas.
The MPhil Program aims to train students in conducting independent and interdisciplinary research in IoT. An MPhil graduate is expected to demonstrate sound knowledge in the discipline and the ability to synthesize and create new knowledge, and make a contribution to the field.
The PhD Program aims to train students in conducting original research in IoT, and to cultivate independent, interdisciplinary, and innovative thinking that is essential for a successful career in IoT-related industry and/or academia. A PhD graduate is expected to demonstrate mastery of knowledge in the chosen discipline and the ability to synthesize and create new knowledge, making an original and substantial scientific contribution to the discipline.
On successful completion of the MPhil program, graduates will be able to:
- Demonstrate sound knowledge of the literature and a comprehensive understanding of scientific methods and techniques relevant to IoT;
- Demonstrate practical skills in building IoT systems;
- Critically apply theories, methodologies, and knowledge to address fundamental questions in IoT;
- Independently pursue research and innovation of significance in IoT applications; and
- Demonstrate skills in oral and written communication sufficient for a professional career.
On successful completion of the PhD program, graduates will be able to:
- Demonstrate mastery of knowledge of the literature and a comprehensive understanding of scientific research relevant to IoT;
- Demonstrate theoretical skills in understanding IoT research;
- Critically evaluate theories, methodologies, and knowledge to address fundamental research questions in IoT;
- Independently conduct original research and make significant scientific contributions to the IoT discipline; and
- Demonstrate competent skills in oral and written communication for the dissemination of thesis results in the scientific community.
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Minimum Credit Requirement
MPhil: 15 credits
PhD: 21 credits -
Credit Transfer
Students who have taken equivalent courses at HKUST(GZ) or other recognized universities may be granted credit transfer on a case-by-case basis, up to a maximum of 3 credits for MPhil students, and 6 credits for PhD students.
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Cross-disciplinary Core Courses
2 credits
UCMP 6010
Cross-disciplinary Research Methods I
2 Credit(s)
UCMP 6020
Cross-disciplinary Research Methods II
2 Credit(s)
Description
This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems.
UCMP 6030
Cross-disciplinary Design Thinking I
2 Credit(s)
Description
This course focuses on user-collaborative design methods for generating inclusive product solutions that integrate stakeholder and product functionality perspectives. Students will create specified product/process/policy/protocol/plan (5P) concept models through the use of recursive user feedback engagement methods, experimental prototyping, and divergent and convergent ideation strategies. Featured topics include design thinking; stakeholder research; concept development, screening, and selection; and interaction design.
UCMP 6040
Cross-disciplinary Design Thinking II
2 Credit(s)
UCMP 6050
Project-driven Collaborative Design Thinking
2 Credit(s)
All MPhil students are required to complete UCMP 6050. All PhD students are required to complete either UCMP 6010 or UCMP 6030. Students may complete the remaining courses as part of the credit requirements, as requested by the Program Planning cum Thesis Supervision Committee.
PhD students who are HKUST(GZ) MPhil graduates and have completed UCMP 6010, UCMP 6030 or UCMP 6050 before may be exempted from this requirement, subject to prior approval of the Program Planning cum Thesis Supervision Committee.
- Hub Core Courses
4 Credits
Students are required to complete at least one Hub core course (2 credits) from the Information Hub and at least one Hub core course (2 credits) from other Hubs.
Information Hub Core Course
INFH 5000
Information Science and Technology: Essentials and Trends
2 Credit(s)
Description
This inquiry-based course aims to introduce students to the concepts and skills needed to drive digital transformation in the information age. Students will learn to conduct research, explore real-world applications, and discuss grand challenges in the four thrust areas of the Information hub, namely Artificial Intelligence, Data Science and Analytics, Internet of Things, and Computational Media and Arts. The course incorporates various teaching and learning formats including lectures, seminars, online courses, group discussions, and a term project.
Other Hub Core Courses
FUNH 5000
Introduction to Function Hub for Sustainable Future
2 Credit(s)
Description
This course covers background knowledge in the thrust areas of the Function Hub, including Advanced Materials, Sustainable Energy and Environment, Microelectronics, and Earth, Ocean and Atmospheric Sciences.
SOCH 5000
Technological Innovation and Social Entrepreneurship
2 Credit(s)
Description
This course discusses both opportunities and risks that technological breakthrough has brought to the human society. What would be the policy responses required to maximize its positive benefit and minimize its social costs? In particular, how could we utilize the technological advancement, entrepreneurial thinking to address the challenges our societies are facing, such as job loss/unemployment, income inequality and societal polarization, environmental degradation, health disparity, population aging, and among others. The course uses either case studies or cross-country and time-series data analyses to facilitate the discussion of various social issues and look for innovative solutions of in the real world.
SYSH 5000
Model-Based Systems Engineering
2 Credit(s)
Description
Model-based systems engineering (MBSE) is a contemporary systems engineering methodology that uses conceptual models for communication between system architects, designers, developers, and stakeholders. Object-Process Methodology (OPM) is an MBSE language and methodology for constructing domain-independent conceptual models of all kinds of systems. The course provides students with basic knowledge and tools for MBSE, focusing on conceptual modeling of systems, giving learners a competitive advantage over their peers.
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Courses on Domain Knowledge
MPhil: minimum 9 credits of coursework
PhD: minimum 15 credits of coursework
Under this requirement, each student is required to take one IOTA course and other elective courses to form an individualized curriculum relevant to the cross-disciplinary thesis research. Only one Independent Study course may be used to satisfy the course requirements.
To ensure that students will take appropriate courses to equip them with needed domain knowledge, each student has a Program Planning cum Thesis Supervision Committee to approve the courses to be taken soonest after program commencement and no later than the end of the first year. Depending on the approved curriculum, individual students may be required to complete additional credits beyond the minimal credit requirements.
Sample Course List
To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below.
IOTA 5001
Social and Web Computing
3 Credit(s)
Description
This course will introduce students to the fundamentals of Social and Web Computing, as well as providing detailed coverage of recent research in this space. It will consist of two major parts. First, students will learn about fundamental social computing theories, alongside computational methodologies that can be used to understand human interactions online. Second, students will be exposed to a range of recent applied research that has employed these methodologies. There will be an empirical focus in the course, and students will be exposed to a range of measurement research capturing how social and web systems work in-the-wild. Students will learn about social network analysis and relevant APIs, alongside related aspects of the Web, user privacy and online advertisement.
IOTA 5002
Data-driven Modeling: Learning from Sensor Data
3 Credit(s)
Description
Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab.
In-class lab demonstrations will also be provided.
In-class lab demonstrations will also be provided.
IOTA 5003
Wireless Connectivity for Mobile Autonomous Things
3 Credit(s)
Description
This course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.
IOTA 5004
Introduction to Physics-informed Machine Learning
3 Credit(s)
Description
Machine learning has emerged as a powerful tool for tackling various problems in engineering and science. Typically via the use of large volume of data, deep neural nets can be trained for this end. However, for engineering and science problems, big data is not enough, and is not always available. This course will introduce the newly emerged paradigm and research trend called “physics-informed machine learning”, where physical laws or physical prior knowledge can be enforced into the architecture of machine learning models, to boost the training and promote the trained models to be more physically consistent and generalizable. At the end of the course, students are expected to understand the principles and methods of physics-informed machine learning, and to have hands-on implementations with Python.
IOTA 5005
Introduction to Energy Harvesting Technology
3 Credit(s)
Description
The distributed power supply for millions and billions of IoTs node sensors will be a challenge. Harvesting sustainable energy from the ambient environment provides the possibility of designing battery-free IoT devices. This course will introduce the vibration energy harvesting technology developed in the past two decades to students. As the fundamentals, commonly used energy transduction mechanisms will be first introduced to the students. The students will also learn various modeling methods, including lumped parameter modeling, equivalent circuit modeling, and finite element modeling.
IOTA 5006
Distributed Systems: Concepts and Applications
3 Credit(s)
Description
This course teaches the design and implementation of efficient, scalable, and fault-tolerant distributed systems. Topics include models for distributed communication and computing, synchronization, and consensus algorithms. The course will also cover relevant applications, including platforms for distributed Machine Learning such as Ray, and large-scale data and stream processing systems such as Apache Flink and Google Dataflow.
IOTA 5007
Introduction to Data Processing in Array-structured Systems
3 Credit(s)
Description
This course provides a comprehensive overview of array processing techniques and their applications in various fields, including wireless communications, radio astronomy, biomedical imaging, and audio and speech signal processing. Students will explore topics such as wave propagation, data modeling, matrix decomposition, adaptive filtering, beamforming, direction finding, microphone and radar array processing, factor analysis, and biomedical array processing. The course emphasizes the interdisciplinary nature of array processing and its relevance in the context of IoT, preparing students to be equipped with the knowledge and skills necessary to analyze and process data from array-structured systems, contributing to advancements in array processing across diverse domains.
IOTA 5101
Fog/Edge/Cloud Computing for IoT
3 Credit(s)
Description
This course introduces students to the latest research on fog computing, mobile edge computing, and cloud computing. How IoT applications can benefit from the computation and caching resources provided at different parts of the Internet will be discussed and the tradeoff among different options will be analyzed. Challenges in deploying IoT applications will be discussed and proposed solutions will be explored.
IOTA 5102
Localization for IoT
3 Credit(s)
Description
This course introduces students to the fundamentals and latest research on localization for Internet of Things, including GPS, indoor positioning based on ultra-wideband communications, and simultaneous localization and communications in 5G/6G, et al. Apart from electromagnetic waves, the localization based on acoustic signal will also be introduced.
IOTA 5103
Emerging Wireless Technologies for IoT
3 Credit(s)
Description
With the proliferation of IoT devices and applications, successful delivery of latency-critical and energy-constrained services pose new challenges for the next-generation wireless communications. In this course, basic knowledge of wireless communications will first be introduced, and optimization/AI assisted techniques to combat these challenges will also be briefly covered, following introduction to the state-of-the-art beyond-5G (B5G) technologies, in which we will investigate sustainable, scalable and AI endogenous wireless solutions for IoT.
IOTA 5104
Fundamentals of Discrete-Time Signal Processing
3 Credit(s)
Description
This course introduces students to the fundamentals of discrete-time signal processing, for both linear time invariant (LTI) and non-LTI systems. For LTI systems, the topics include the sampling theorem, Fourier transform, convolution, and spectrum analysis, which lays the foundation for OFDM in wireless communications. Advanced topics will also be covered for time-variant systems, such as Heisenberg transform, Wigner distribution and Fractional Fourier transform, and their applications in radar, sonar and the OTFS modulation in wireless communications.
IOTA 5105
Fundamentals of Wireless Communications
3 Credit(s)
Description
This course lays theoretical foundation for students with general EE background to pursue research advances involving wireless communication-based systems, e.g., 4G/5G mobile communication, IoT and WLAN. In this course, concepts of wireless channels, its modelling as well as channel capacity for point-to-point/multi-user/MMO communications will be systematically introduced. Along with understanding of these theories, the principles and state-of-the-art technologies to combat fading and interference including general diversity techniques, OFDM, and multiple access schemes will be conveyed. Finally, a few advanced topics for 5G-and beyond (B5G) communication system design will be briefly introduced, such as massive MIMO and reconfigurable intelligent surface (RIS).
IOTA 5106
Communication Networks: Theory, Models, and Protocols
3 Credit(s)
Description
The course focuses on the performance analysis and optimization of communication networks, including:1) Theory: queueing theory, linear programming, convex optimization, fluid-flow analysis; 2) Models: Markov chains, queueing networks, network optimization, multimedia networks; 3) Protocols: error control, flow control,medium access control, routing, congestion control, packet scheduling.
IOTA 5107
Advanced Networked Systems
3 Credit(s)
Description
This course focuses on relevant current research topics in Networked Systems such as Data-Center Networks, Software-Defined Networking, Dataplane Programmability, Information-Centric Networking, Quantum Internet Communication, Privacy Preserving Communication, Constrained (IoT) Networks.
IOTA 5108
Incremental Learning and Adaptive Signal Processing
3 Credit(s)
Description
This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.
IOTA 5109
Wireless Sensor and Actuator Networks Toward Swarm Intelligence
3 Credit(s)
Description
This course teaches the basic concepts of wireless sensor and actuator networks (WSANs), and how swarm intelligence is applied over WSAN. The course content includes the typical architecture of the hardware WSAN devices, the general communication mechanism and examples of applications using WSANs to realize swarm intelligence.
IOTA 5201
Reinforcement Learning for Intelligent Decision Making in Cyber-Physical Systems
3 Credit(s)
Description
This course focuses on applying reinforcement learning (RL) to cyber-physical systems (CPS), which integrate computation, networking, and physical processes. It covers the fundamentals of RL and its application in designing intelligent decision-making algorithms for CPS. Topics include model-based and model-free RL approaches, safe RL practices to ensure the safety of CPS, and the integration of RL with other techniques, such as model predictive control in CPS. The course will also explore different applications of RL in CPS.
IOTA 5202
Efficient Machine Learning for Resource Constrained Environments
3 Credit(s)
Description
This course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance.
IOTA 5501
Convex and Nonconvex Optimization I
3 Credit(s)
Description
This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.
IOTA 5502
Convex and Nonconvex Optimization II
3 Credit(s)
Description
This course covers advanced theory, algorithms, and applications for convex and nonconvex optimization, including: subgradient methods, localization methods, decomposition methods, proximal methods, alternating direction methods of multipliers, conjugate direction methods, successive approximation methods, convex-cardinality problems, low-rank optimization problems, neural networks, semidefinite programming and relaxation, robust optimization, discrete optimization, stochastic optimization, etc.
IOTA 5503
Systems Security and Privacy: Theory and Applications
3 Credit(s)
Description
This course covers fundamental and applied aspects of privacy and security in the Internet of Things (IoT). The course will teach students about the basics of cryptography, equip students with the abilities to rigorously understand and analyse the security of information systems, and get familiar with practical security technologies like privacy/public key encryption and message authentication. This course will then explain how these technologies are used and deployed in IoT environments, before exploring how recent attacks have discovered new vulnerabilities in real IoT deployments. The course will emphasise the value of empirical observations and give students insight into how these vulnerabilities can be measured in-the-wild. By the end of the module, students will: (1) Develop a systematic understanding of notion of security in information systems (2) Understand and be able to evaluate the fundamentals of cryptographic technologies; (3) Have a solid grasp of how these technologies are deployed, and how we can measure their efficacy in real deployments of IoT systems; and (4) Understand a set of case study vulnerabilities (and defenses) that have been discovered in-the-wild.
IOTA 5504
Approximate Computing - Introduction to Numerical Analysis
3 Credit(s)
Description
This course is an introduction to the algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis. The course will help students to develop the basic understanding of numerical algorithms and the skills to implement algorithms to solve mathematical problems on the computer.
IOTA 5505
Statistics for Inference, Learning and Data Processing
3 Credit(s)
Description
This course introduces the fundamentals and advanced topics of statistics from its modeling, analysis and inference that covers multivariate distribution along with dimension-reduction techniques, concentration inequalities, classification/clustering, to applications in statistical signal processing, including estimation theory and methods, detection theory and methods, and advanced algorithms such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM), etc.
IOTA 6900
Independent Study
1-3 Credit(s)
Description
An independent research project carried out under the supervision of a faculty member on an Internet of Things topic.
IOTA 6910
Special Topics in Internet of Things
3 Credit(s)
Description
Advanced topics in Internet of Things(IoT): IoT in finance; IoT in manufacturing; IoT in healthcare; IoT in security and privacy; IoT in digital society; ethical issues in IoT and digital society ethics; modeling and optimization for IoT; signal processing for IoT. The course may be repeated for credit if different topics are studied.
AIAA 5024
Advanced Deep Learning
3 Credit(s)
Description
This course covers recent developments in deep learning. Topics include meta learning, model compression, federated learning, representation learning, explainable AI, adversarial attack and defense, and advances in deep learning theory.
AIAA 5025
Deep Reinforcement Learning
3 Credit(s)
Description
This course covers recent developments in deep reinforcement learning. Topics include reinforcement learning basics, deep Q-learning, policy gradients, actor-critic algorithms, model-based reinforcement learning, imitation learning, inverse reinforcement learning, hierarchical reinforcement learning, and multi-agent reinforcement learning.
AIAA 5030
Foundations of Data Mining
3 Credit(s)
Description
This course will introduce the fundamental principles, uses, and technical details of data mining techniques by lectures and real-world case studies. The emphasis is on understanding the basic data mining techniques and their applications. We will discuss the mechanics of how data analytics techniques work as is necessary to understand the fundamental concepts and real-world applications.
DSAA 5002
Data Mining and Knowledge Discovery in Data Science
3 Credit(s)
Description
With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI.
DSAA 5020
Foundation of Data Science and Analytics
3 Credit(s)
Description
This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.
DSAA 5024
Data Exploration and Visualization
3 Credit(s)
Description
This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.
IPEN 5500
Science, Technology and Innovation Policy
3 Credit(s)
Description
The course introduces the conceptualizations of innovation policy and its instruments. It also develops evaluation methods to analyze the effects of these policy instruments and policy mixes. Cases of conceptual and empirical studies focus on the issues of innovation funding schemes and publicly funded science systems.
UGOD 5010
Science of Cities
3 Credit(s)
Description
The course aims to provide a comprehensive understanding of the city and the system of cities, the challenges faced by cities, especially the rapidly-developing large cities, and the key tools for interventions in response to critical pressures linked to economic development, urbanization, globalization, migration, social inclusion, climate change, resource efficiency, technology etc.
UGOD 5060
Urban Data Analytics
3 Credit(s)
Description
Over recent years, the way data are used to understand urban system has changed dramatically. Cities are constantly adapting to incorporate new technology, and urban social life increasingly occurs in digital environments and continues to be mediated by digital systems, producing urban data not only in volume but also in form (i.e. text, image, audio, and video). This course delves into the challenges and opportunities of using new and emerging forms of data to study cities.
- Additional Foundation Courses
Individual students may be required to take foundation courses to strengthen their academic background and research capacity in related areas, which will be specified by the Program Planning cum Thesis Supervision Committee. The credits earned cannot be counted toward the credit requirements.
- Graduate Teaching Assistant Training
PDEV 6800
Introduction to Teaching and Learning in Higher Education
0 Credit(s)
Description
The course is designed to strengthen students’ competence in teaching. It comprises 2 parts: Part 1 aims to equip all full-time research postgraduate (RPg) students with basic teaching skills before assuming teaching assistant duties for the department. Good teaching skills can be acquired through learning and practice. This 10-hour mandatory training course provides all graduate teaching assistants (GTA) with the necessary theoretical knowledge with practical opportunities to apply and build up their knowledge, skills and confidence in taking up their teaching duties. At the end of the course, GTAs should be able to (1) facilitate teaching in tutorials and laboratory settings; (2) provide meaningful feedback to their students; and (3) design an active learning environment to engage their students. In Part 2, students are required to perform instructional delivery assigned by their respective departments to complete this course. MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. Graded PP, P or F.
All full-time RPg students are required to complete PDEV 6800. The course is composed of a 10-hour training offered by the Institute of Educational Innovation and Practice (IEIP), and session(s) of instructional delivery to be assigned by the respective Thrusts/Base. Upon satisfactory completion of the training conducted by IEIP, MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. The instructional delivery will be formally assessed.
- Professional Development Course Requirement
PDEV 6770
Professional Development for Research Postgraduate Students
1 Credit(s)
Description
This course aims at equipping research postgraduate students with transferrable skills conducive to their professional development. Students are required to attend 3 hours of mandatory training on Professional Conduct, and complete 12 hours of workshops, at their own choice, under the themes of Communication Skills, Research Competency, Entrepreneurship, Self‐Management, and Career Development. Graded PP, P or F.
Students are required to complete PDEV 6770. The 1 credit earned from PDEV 6770 cannot be counted toward the credit requirements.
PhD students who are HKUST MPhil graduates and have completed PDEV 6770 or other professional development courses offered by the University before may be exempted from taking PDEV 6770, subject to prior approval of the Program Planning cum Thesis Supervision Committee.
INFH 6780
Career Development for Information Hub Students
1 Credit(s)
Description
This course aims at equipping RPg students of the Information Hub with the skills conducive to their professional career development. Students are required to attend the 3 hours' training focusing on personality self-exploration and discipline-specific training at the thrust level, and another 10 hours' training at the hub level, at their own choice. Graded PP, P or F.
Students are required to complete INFH 6780. The 1 credit earned from INFH 6780 cannot be counted toward the credit requirements.
PhD students who are HKUST MPhil graduates and have completed INFH 6780 or other professional development courses offered by the University before may be exempted from taking INFH 6780, subject to prior approval of the Program Planning cum Thesis Supervision Committee.
- English Language Requirement
LANG 5000
Foundation in Listening & Speaking for Postgraduate Students
1 Credit(s)
Description
For students whose level of spoken English is lower than ELPA Level 4 (Speaking) when they enter the University. The course addresses the immediate linguistic needs of research postgraduate students for oral communication on campus using English. To complete the course, students are required to attain at least ELPA Level 4 (Speaking). Graded P or F.
Full-time RPg students are required to take an English Language Proficiency Assessment (ELPA) Speaking Test administered by the Division of Language Education before the start of their first term of study. Students whose ELPA Speaking Test score is below Level 4, or who failed to take the test in their first term of study, are required to take LANG 5000 until they pass the course by attaining at least Level 4 in the ELPA Speaking Test before graduation. The 1 credit earned from LANG 5000 cannot be counted toward the credit requirements.
PLED 5001
Communicating Research in English
1 Credit(s)
Students are required to take PLED 5001. The credit earned cannot be counted toward the credit requirements. Students can be exempted from taking this course with the approval of the Program Planning cum Thesis Supervision Committee.
- Postgraduate Seminar
IOTA 6101
Internet of Things Seminar I
0 Credit(s)
Description
A series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Graded P or F.
IOTA 6102
Internet of Things Seminar II
1 Credit(s)
Description
A series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Continuation of IOTA 6101. Graded P or F.
Students are required to complete IOTA 6101 and IOTA 6102 in two terms. The credit earned cannot be counted toward the credit requirements.
- PhD Qualifying Examination
PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy.
- Thesis Research
IOTA 6990
MPhil Thesis Research
0 Credit(s)
Description
Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
IOTA 7990
Doctoral Thesis Research
0 Credit(s)
Description
Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
MPhil:
- Registration in IOTA 6990; and
- Presentation and oral defense of the MPhil thesis.
PhD:
- Registration in IOTA 7990; and
- Presentation and oral defense of the PhD thesis.
Last Update: 1 July 2024
To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.
1. General Admission Requirements of the University
Please refer to Admission Requirements.
2. English Language Admission Requirements
Please refer to Admission Requirements.
Admission to HKUST(GZ)
Apply online before the application deadlines.
Application Fee
RMB150
* All international students are required to obtain a student visa (X visa) for studying in China’s mainland. For details on student visa (X visa) requirements, please click here.
