Prof YEUNG, Chi Ho
楊志豪
教授

Professor |
Department of Science and Environmental Studies |

Dean of Students |
Student Affairs Office |

Contact

ORCiD

0000-0001-6207-7597

Phone

(852) 2948 8722

Fax

(852) 2948 6240

Email

chyeung@eduhk.hk

Address

10 Lo Ping Road, Tai Po, New Territories, Hong Kong

Scopus ID

24833763000

ResearcherID

H-9673-2013

Research Interests

- Statistical physics, physics of disordered systems
- Interdisciplinary applications of statistical physics
- Optimization
- Models of complex systems
- Transportation networks
- Complex and social networks
- Recommender systems
- Deep learning
- STEM education

Personal Profile

**Working experience:**

2024-Present Professor, Department of Science and Environmental Studies, EdUHK, Hong Kong

2019-2024 Associate Professor, Department of Science and Environmental Studies, EdUHK, Hong Kong

2016-2019 Assistant Professor, Department of Science and Environmental Studies, EdUHK, Hong Kong

2013-2016 Lecturer, Department of Science and Environmental Studies, EdUHK, Hong Kong

2013 Postdoctoral Research Fellow, Department of Physics, HKUST, Hong Kong

2011-2013 Postdoctoral Research Fellow, The Nonlinearity and Complexity Research Group, Department of Mathematics, Aston University, United Kingdom

2009-2011 Postdoctoral Research Fellow, Department of Physics, University of Fribourg, Switzerland

**Academic qualifications:**

2006-2009 PhD in Physics, HKUST, Hong Kong

2004-2006 MPhil in Physics, HKUST, Hong Kong

2001-2004 BSc in Physics and Mathematics, HKUST, Hong Kong

Research Interests

- Statistical physics, physics of disordered systems
- Interdisciplinary applications of statistical physics
- Optimization
- Models of complex systems
- Transportation networks
- Complex and social networks
- Recommender systems
- Deep learning
- STEM education

Research Outputs

Scholarly Books, Monographs and Chapters Chen, Y., Yeung, C. H., Luo, T., He, Q., So, W. M. W. (2023). Preparation of Teachers for STEM Education in Hong Kong.. Al-Balushi, S.M., Martin-Hansen, L., Song, Y. (Ed.), Reforming Science Teacher Education Programs in the STEM Era. Palgrave Studies on Leadership and Learning in Teacher Education (107-124). Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-031-27334-6_7Cheang, C. C., Cheung, T. Y., So, W. M. W., Cheng, N. Y. I., Fok, L., Yeung, C. H., Chow, C. F. (2019). Enhancing Pupils’ Pro-environmental Knowledge, Attitudes, and Behaviours toward Plastic Recycling: A Quasi-Experimental Study in Primary Schools. In So, W. M. W., Chow, C. F. & Lee, J. C. K. (Eds.), Environmental Sustainability and Education for Waste Management (125-143). Singapore: Springer Nature. 李揚津、陳文豪、陳偉康、霍年亨、郭炳偉、李凱雯、吳永水、曾耀輝、楊志豪 (2017)。 STEM教育－從理論到實踐。香港: 香港教育大學。 Irene Chung Man Lam, Chi Ho Yeung, & Yau Yuen Yeung (2016). Mobile learning in Hong Kong teacher education: Pilot implementation and evaluation. In M. Carmo (Ed.), Education Applications & Development II (Chapter 11) (112-122). Lisboa, Portugal: inScience Press. C. H. Yeung, Y.-C. Zhang (2009). Minority Games. Robert A. Meyers, Encyclopedia of Complexity and Systems Science (5588-5604). Berlin, Germany: Springer. |

Journal Publications Woo, David James; Susanto, Hengky; Yeung, Chi Ho; Guo, Kai; Fung, April Ka Yeng (2024). Exploring AI-Generated text in student writing: How does AI help?. Language Learning and Technology, 28 (2), 183–209. https://doi.org//10125/73577Yeung, C.Y., Yeung, C.H., Sun, D*., & Looi, C-K. (2024). A Systematic Review of Drone Integrated STEM Education at Secondary Schools (2005-2023): Trends, Pedagogies, and Learning Outcomes. Computers and Education, xx, 0-0. https://doi.org/10.1016/j.compedu.2024.104999Li, B., & Yeung, C. H. (2023). Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of Multi-armed Bandits. Chaos: An Interdisciplinary Journal of Nonlinear Science Zhao, C., Zhang, J., Hou, X., Yeung, C. H., & Zeng, A. (2023). A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups. PLOS Computational Biology, 19(4), e1011083.Bo Li, David Saad, and Chi Ho Yeung (2022). Bilevel optimization in flow networks: A message-passing approach. Physical Review E, 106, L042301. https://doi.org/10.1103/PhysRevE.106.L042301Hao Liao, Qi-Xin Liu, Ze-Cheng Huang, Ke-Zhong Lu, Chi Ho Yeung and Yi-Cheng Zhang (2022). Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks. Journal of Computer Science and Technology, 37(4), 960–974. https://doi.org/10.1007/s11390-0471-4Tai, T. S., & Yeung, C. H. (2022). Adaptive strategies for route selection en-route in transportation networks. Chinese Journal of Physics, 77, 712-720. https://doi.org/10.1016/j.cjph.2021.07.024Xu, Y. Z., Po, H. F., Yeung, C. H., & Saad, D (2022). Scalable node-disjoint and edge-disjoint multiwavelength routing. Physical Review E, 105(4) https://doi.org/10.1103/PhysRevE.105.044316Tai, T. S. & Yeung, C. H. (2021). Optimally coordinated traffic diversion by statistical physics. Physical Review E, 104, 024311. Yeung, C. H., & Cheung K. L., & Lin X. Y. (2021). Using the Flipped Classroom Model for Student Pre-laboratory Preparation in a Science Course: An Action Research Study. Ubiquitous Learning: An International Journal, 14, 1. Zhu B., & Yeung C. H., & Liem R. P. (2021). The impact of common neighbor algorithm on individual friend choices and online social networks. Physica A: Statistical Mechanics and its Applications, 566, 125670. Po, H. F., & Yeung, C. H., & Saad, D., (2021). Futility of being selfish in optimized traffic. Physical Review E, 103, 022306. Zhao, C., & Zeng, A., & Yeung, C. H. (2021). Characteristics of human mobility patterns revealed by high-frequency cell-phone position data. EPJ Data Science, 10, 5. Tai, T. S. and Yeung, C. H. (2019). Global benefit of randomness in individual routing on transportation networks. Physical Review E, 100, 012311. Yeung, C. H. (2019). Coordinating dynamical routes with statistical physics on space-time networks. Physical Review E, 99, 042123. Xu Y.-Z., Yeung C. H., Zhou H.-J., & Saad D. (2018). Entropy Inflection and Invisible Low-Energy States: Defensive Alliance Example. Physical Review Letters, 121, 210602. J. Rocchi, D. Saad and C. H. Yeung (2018). Slow spin dynamics and self-sustained clusters in sparsely connected systems. Physical Review E, 97, 062154. Cheung, T. Y., Fok, L., Cheang, C.C., Yeung, C. H., So, W. M. W., Chow, C. F. (2018). University Halls Plastics Recycling: A Blended Intervention Study. International Journal of Sustainability in Higher Education, 19, 1038-1052. H. F. Po, C. H. Yeung, A. Zeng, and K. Y. M. Wong (2017). Evolving power grids with self-organized intermittent strain releases: An analogy with sandpile models and earthquakes. Physical Review E, 96, 052312. Rocchi, J., Saad, D. and Yeung, C. H. (2017). Self-sustained clusters as drivers of computational hardness in p-spin models. Physical Review B, 96 (2), 024415. X. Deng, Y. Zhong, L. Lü, N. Xiong, C. H. Yeung (2017). A general and effective diffusion-based recommendation scheme on coupled social networks. Information Sciences, 417, 420-434. Ji, S., Lv, L., Yeung, C. H. and Hu, Y. (2017). Effective spreading from multiple leaders identified by percolation in the Susceptible-Infected-Recovered (SIR) model. New Journal of Physics, 19 (7), 073020. Wong, K. Y. M., Saad, D. and Yeung, C. H. (2016). Distributed Optimization in Transportation and Logistics Networks. IEICE Transactions on Communications, E99-B (11), 2237-2246. Zeng, A., Yeung, C.H. (2016). Predicting the future trend of popularity by network diffusion. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26, 063102. Zhang, Y.-X., Liao, H., Medo, M., Shang, M.-S., Yeung, C.H. (2016). Study of market model describing the contrary behaviors of informed and uninformed agents: Being minority and being majority. Physica A, 450, 486-496. Hu, F., Yeung, C.H., Yang, S., Wang, W., Zeng, A., (2016). Recovery of infrastructure networks after localised attacks. Scientific Reports, 6, 24522. Yeung, C.H. (2016). Do recommender systems benefit users? a modeling approach. Journal of Statistical Mechanics: Theory and Experiment, 4, 043401. You, Z.-Q., Han, X.-P., Lü, L., Yeung, C.H. (2015). Empirical studies on the network of social groups: the case of Tencent QQ. PLoS ONE, 10, e0130578. Zeng, A., Yeung, C.H., Medo, M. Zhang, Y-.C. (2015). Modeling mutual feedback between users and recommender systems. Journal of Statistical Mechanics: Theory and Experiment, 2015, P07020. Saad, D., Yeung, C.H., Rodolakis, G., Syrivelis, D., Koutsopoulos, I., Tassiulas, L., Urbanke, R., Giaccone, P., Leonardi E. (2014). Physics-inspired methods for networking and communications. IEEE Communications Magazine, 52 (11), 144-151. De Bacco, C., Franc, S., Saad, D., Yeung, C.H. (2014). Shortest node-disjoint paths on random graphs. Journal of Statistical Mechanics: Theory and Experiment, 2014, P07009. Yeung, C. H., Wong, K. Y. M., & Li, B. (2014). Coverage versus supply cost in facility location: Physics of frustrated spin systems. Physical Review E, 89, 062805. C. H. Yeung, D. Saad (2013). Self-sustained clusters and ergodicity breaking in spin models. Physical Review E, 88, 032132. C. H. Yeung, D. Saad, K. Y. M. Wong (2013). From the Physics of Interacting Polymers to Optimizing Routes on the London Underground. PNAS, 110, 13717-13722. C. H. Yeung, D. Saad (2013). Networking - A Statistical Physics Perspective. Journal of Physics A: Mathematical and General, 46, 103001. C. H. Yeung, D. Saad (2012). Competition for Shortest Paths on Sparse Graph. Physical Review Letters, 108, 208701. L. Lu, M. Medo, C. H. Yeung, Y.-C. Zhang, Z.-K. Zhang, T. Zhou (2012). Recommender Systems. Physics Reports, 519, 1-49. A. Zeng, C. H. Yeung, M.-S. Shang, Y.-C. Zhang (2012). The reinforcing influence of recommendations on global diversification. Europhysics Letters, 97, 18005. L. Lu, Y.-C. Zhang, C. H. Yeung, T. Zhou (2011). Leaders in social networks, the delicious case. PLoS ONE, 6, e21202. S. Gualdi, C. H. Yeung, Y.-C. Zhang (2011). Tracing the evolution of physics on the backbone of citation networks. Physical Review E, 84, 046104. C. Liu, C. H. Yeung, Z.-K. Zhang (2011). Self-organization in social tagging systems. Physical Review E, 83, 066104. A. Zeng, S.-W. Son, C. H. Yeung, Y. Fan, Z. Di (2011). Enhancing synchronization by directionality in complex networks. Physical Review E, 83, 045101. P. Wang, T. Lei, C. H. Yeung, B.-H. Wang (2011). Heterogenous Human Dynamics in Intra- and Inter-day Time Scales. Europhysics Letters, 94, 18005. P. Wang, X.-Y. Xie, C. H. Yeung, Bing-Hong Wang (2011). Heterogenous scaling in interevent time of on-line bookmarking. Physica A, 390, 2395-2400. C. H. Yeung, G. Cimini, C.-H. Jin (2011). Dynamics of movie competition and popularity spreading in recommender systems. Physical Review E, 83, 016105. C. H. Yeung, K. Y. M. Wong (2010). Optimal Location of Sources in Transportation Networks. Journal of Statistical Mechanics, 2010, P04017. C. H. Yeung, K. Y. M. Wong (2010). Self-Organization of Balanced Nodes in Random Networks with Transportation Bandwidths. European Physical Journal B, 74, 227. C. H. Yeung, K. Y. M. Wong (2010). Transitions in Transportation Networks with Nonlinearities. Physical Review E, 80, 021102. C. H. Yeung, K. Y. M. Wong (2009). Optimal Resource Allocation in Random Networks with Transportation Bandwidths. Journal of Statistical Mechanics, 2009, P03029. C. H. Yeung, K. Y. M. Wong (2009). Models of Financial Markets with Extensive Participation Incentives. Physical Review E, 77, 026107. C. H. Yeung, M. Medo, Y.-C. Zhang (2009). How to quantify the influence of correlations on investment diversification. International Review of Financial Analysis, 18, 34-39. C. H. Yeung, K. Y. M. Wong (2006). Temporal Effects of Agent Aggregation in the Dynamics of a Competing Population. Europhysics Letters, 75, 357. |

Conference Papers Li, B., Wong, K.Y.M., Yeung, C.H. (2015, December). Optimal Facility Location with Message Passing Algorithm. 2015 International Symposium on Nonlinear Theory and its Applications (NOLTA2015), Hong Kong. Irene C.M. Lam, C.H. Yeung, and Y.Y. Yeung (2015, June). Mobile learning in Hong Kong teacher education: students’ level of readiness and receptivity.. Proceedings of the International Conference on Education and New Developments 2015 (pp. 539-541), Porto, Portugal. C. H. Yeung (2013, June). Efficient Algorithm for Routing Optimization vis Statistical Mechanics. Proceedings of IEEE ICC workshop Netstat 2013, Budapest, Hungary. C. H. Yeung, K. Y. M. Wong (2010, March). Clusters of Resource Consuming Nodes in Transportation Networks. International Workshop on Statistical-Mechanical Informatics 2010 (Published in Journal of Physics: Conference Series), Tokyo, Japan. C. H. Yeung, K. Y. M. Wong (2009, June). Emergence of algorithmically hard phases in transportation networks. 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, Seoul, Korea. C. H. Yeung, K. Y. M. Wong (2009, February). Self-organized balanced resources in random networks with transportation bandwidths. Complex 2009 (Published in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering), Shanghai, China. K. Y. M. Wong, D. Saad, C. H. Yeung (2008, March). Network Optimization: A statistical physics perspective. 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 2008, Berlin, Germany. K. Y. M. Wong, C. H. Yeung, D. Saad (2006, October). Message-passing for Inference and Optimization of Real Variables on Sparse Graphs. ICONIP 2006, 13th International conference on Neural Information Processing (Published in Lecture Notes of Computer Sciences), Hong Kong. C. H. Yeung, Y. P. Ma, K. Y. M. Wong (2006, July). Stable Aggregates in the Dynamics of a Competing Population. Dynamics Days Asia Pacific 2006 (Published in Journal of Korean Physical Society), Seoul, Korea. C. H. Yeung, Y. P. Ma, K. Y. M. Wong (2006, May). Epoch Lifetimes in the Dynamics of a Competing Population. International Conference on Frontiers of Nonlinear and Complex systems (Published in International Journal of Modern Physics B), Hong Kong, China. |

Projects

Bridging Statistical Physics and Transportation Science – Optimal Route Coordination for Autonomous Vehicles in Mixed-Autonomy Traffic Project Start Year: 2024, Principal Investigator(s): YEUNG, Chi Ho |

From the Non-ergodicity in Physics to the Non-convexity in Optimization – How do They Manifest Themselves in the Variable Space? Implications and Applications Project Start Year: 2024, Principal Investigator(s): YEUNG, Chi Ho |

Leveraging Exploration and Exploitation in Hard Optimization Problems via Statistical Physics .. Project Start Year: 2021, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Optimizing the Sequence of Sampling with Statistical Physics .. Project Start Year: 2021, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Promoting AI Literacy and Effective Use of AI in Education The objectives of this project are twofold: firstly to prepare students with AI literacy and secondly to explore and develop AI tools for teaching and learning in a range of academic disciplines. The project will offer a good opportunity not only to develop the AI literacy of students, but also to promote effective use of AI in education as well as to prepare students to adopt AI-powered technology in their future teaching careers. Project Start Year: 2020, Principal Investigator(s): LI, Wai Keung 李偉強 (YEUNG, Chi Ho 楊志豪 as Co-Investigator) |

Resolving the Mystery of Deep Learning by Statistical Physics The rapid development of deep learning in the past decade has led to many remarkable applications, ranging from speech recognition, which achieved human-level performance, to Go-playing algorithms, which beat top professional players. Surprisingly, despite the increasing number of applications using deep learning, we only have a limited understanding of their remarkable performance. In particular, many deep learning applications apply deep neural networks (DNN) to infer the non-trivial input-output relations on labeled datasets. The internal representations in DNNs, the mechanism by which they arrive at good decisions, and how the over-parametrized DNNs avoid over-fitting and achieve high generalizability are not fully understood. This incomplete understanding may cause fatal dangers, as deep learning is now commonly applied to vital applications such as medical image analyses and self-driving cars. In the proposed project, we will apply tools in statistical physics to derive a fundamental understanding of DNNs, which can be applied to boost their performance and most importantly reduce any risks when DNNs are used in vital applications. We remark that statistical physics tools have already been applied to analyze shadow neural networks to obtain their macroscopic properties inaccessible by tools in other areas, but the understanding of DNNs via statistical physics is far from complete. Here, we aim to (1) develop an improved fundamental understanding on DNNs in terms of their loss landscape, training dynamics, and most importantly their remarkable generalization performance; (2) establish theoretical frameworks to analyze DNNs, by drawing an analogy with spin glasses and disordered systems; these frameworks will play a crucial role in the future theoretical studies and understanding of deep learning; (3) understand and leverage the dilemma of exploration and exploitation in training DNNs, and introduce new protocols to be used in combination with the state-of-the Project Start Year: 2020, Principal Investigator(s): YEUNG, Chi Ho |

Optimizing Knowledge Flow in the Research Community through Statistical Physics In our proposed research, we aim to employ techniques in text mining and statistical inference, integrated with the Physics approaches of optimization, and the approaches for complex networks and the Science of Science, (1) to understand examples where knowledges are re-discovered independently due to ineffective knowledge flow, and then to derive tools to reduce future knowledge re-discovery and to save resources wasted on duplicated research; (2) to give a holistic picture of research development within and connecting different areas of research; (3) and ultimately, to apply all our findings to devise new search protocols which optimize literature search, to facilitate knowledge flow in the research community as a whole. We will first focus our study on the literatures in the different areas of Physics, of which the publication database is readily available, and extend the methods to other areas upon successful attempts. Project Start Year: 2019, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Optimizing Knowledge Flow in the Research Community through Statistical Physics and the Science of Science An efficient and accurate literature search is a crucial first step in research, but it is never easy; research ideas are often combinations of entities, concepts, theories and methodologies, related in a complex way, difficult to be searched for or identified by conventional keyword-based search engines. This is further complicated by the rapidly expanding literature and the increasingly cross-disciplinary nature of research. One may have to spend extensive time and effort, even with the aid of intuition and luck, to complete literature search but remain at risk of missing relevant information. Nevertheless, even with a very powerful search engine which outputs a list of the most relevant literature, there are non-trivial connections underlying these different research-papers which constitute a holistic picture of research development in the area. Such picture is not identified by search engines, and hence is not known to the searchers. As literature search over a comprehensive database is the major channel of knowledge flow within the research community, the ineffectiveness of search systems has rendered knowledge flow sub-optimal. In the long run, this impacts negatively on research development in every area. In our proposed research, we aim to employ techniques in text mining and statistical inference, integrated with the Physics approaches of optimization, and the approaches for complex networks and the Science of Science, to reveal the extent of effectiveness of knowledge flow in the research community. Project Start Year: 2019, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Black-box Optimization via Statistical Physics Black-box optimization corresponds to a class of optimization problems with a complicated or an unknown objective function, i.e. a “black-box” function, such that its output values at specific inputs can only be measured by expensive or time-consuming processes. They are very challenging and cannot be tackled by conventional optimization algorithms which are based on the knowledge of a known objective function. Nevertheless, they are crucial in a wide range of applications in science and engineering. Yet, unlike conventional “white-box” optimization problems where physicists have devoted decades of efforts in developing a fundamental understanding of their macroscopic properties which has led to insightful developments, the awareness of black-box problems in the physics community and the attempts to address them are very limited. This is partly because an unknown objective function is incompatible with conventional statistical physics tools. As a result, research on black-box optimization is dominated by algorithm-oriented approaches without a thorough understanding underlying black-box optimization problems. In the proposed research, we will overcome the obstacle of an unknown objective function and apply statistical physics tools to (1) develop a fundamental understanding of the nature of black-box optimization problems, (2) derive a macroscopic description of their behaviors and understand the effectiveness of their existing solution methods, and (3) apply these insights to improve existing solution methods, and devise new physics-inspired and understanding-driven algorithms for black-box problems. Specifically, we will establish a theoretical framework to study black-box optimization problems with statistical physics. We will apply tools from statistical physics to (a) understand the effectiveness of various sampling strategies in relation with the nature of black-box objective functions, (b) reveal the relation between the objective function fitting stage and the subsequent optimization stage in conventional modeling-fitting-optimizing approaches for black-box problems, (c) map black-box problems to spin glasses and disordered systems of noisy information retrieval and associate memory, (d) coarse-grain black-box problems and examine the validity of the coarse-grained systems as representatives of the original systems with reduced dimensionality, and finally (e) use all the above theoretical insights to improve and inform existing black-box solution methods, and devise new physics-inspired and understanding-driven algorithms for black-box optimization problems. Project Start Year: 2018, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Connecting Theoretical Statistical Physics with Practical Combinatorial Optimization Problems Optimization corresponds to the task to identify a configuration of variables to maximize or minimize an objective function. It is implicitly implemented in a wide range of daily activities, as well as numerous tasks in research, industry and commerce. Computer scientists, operations researchers and applied mathematicians have devoted great efforts to develop optimization algorithms to tackle specific tasks, and found that some optimization problems are more difficult to solve than the others. Yet, the origins of such difficulties are not fully understood and are not a major interest in conventional studies. Definitely, a clear understanding will lead to stimulating clues to improve optimization algorithms and their ability to tackle hard problems. Physicists play an important role to develop a fundamental understanding of optimization problems by drawing an analogy with physical systems which tend to achieve the state with the lowest energy, analogous to an optimal state. Nevertheless, physicists are interested in aspects of optimization problems that conventional optimization researchers find unrealistic, irrelevant or impractical. This leads to a limited recognition of the physics-based results among conventional optimization researchers and thus, isolated developments in the two individual areas. We propose to better integrate and bridge physics and combinatorial optimization problems, by (1) using physical tools to study aspects of optimization problems where optimization researchers find most practical, (2) improving methodologies in both areas instead of improving merely the methodologies from physics, and (3) converting the new understandings into new applications. Project Start Year: 2017, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Towards the Global Optimum in Dynamical Transportation Networks with Statistical Physics In the proposed research, we will apply statistical physics to understand the nature and the limitation of transportation optimization, and use these insights to derive practical optimization algorithms. Similar success by statistical physics has been demonstrated in other optimization problems, which has led to ground-breaking advances. Our objective is threefold. Firstly, we aim to reveal the dynamics and the interplay of routing strategies leading to user equilibriums. We then formulate a simple model to understand analytically the emergence of these sub-optimal states. Secondly, we aim to devise algorithms which coordinate the spatial-temporal routes of individuals, driving the system towards the global optimum. We will also reveal the density of sub-optimal states in the state space, which lead to insights into the intrinsic sub-optimality of transportation networks and thus the limitation of optimization algorithms. Finally, we aim to devise algorithms to optimally divert traffic in cases of disturbances, e.g. road blockage due car crashes, which are less explored but as important as recurrent traffic optimization. Project Start Year: 2016, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Student Network Groups as Change Agents The project has a key theme to engage students directly to take the role as “change agents” in the learning and teaching process through Student Network Group (SNG). Through the four sub-SNG projects, students work in groups and form SNGs to investigate their interested topics. The student leadership role may take different forms in the sub-project i.e. led by students, partnership with teachers or guided by teachers. As a whole, the project aims to improve the learning and teaching experience and promote a student-teacher collaboration environment on campus. Project Start Year: 2015, Principal Investigator(s): CHENG, May Hung May 鄭美紅 (YEUNG, Chi Ho 楊志豪 as Co-Investigator) |

From Statistical Physics towards a New Understanding and Paradigm of Optimization Algorithms Optimization problems correspond to the tasks to optimize a set of variables to extremize an objective function. They are at the center of a wide range of applications ranging from timetable scheduling and delivery of goods to sophisticated optimization processes in industry and commerce. Computer scientists and mathematicians have long been deriving optimization algorithms to identify the optimal solution for specific problems, but their conventional methodologies do not allow them to understand why optimization problems become difficult to solve in some parameter regimes. They leave the task to physicists, who developed a fundamental understanding on optimization problems by drawing analogy with physical systems. They identified parameter regimes where solutions exist but are difficult to find, and these understandings lead to new optimization algorithms which work beyond the limit of conventional algorithms. Nevertheless, these findings are not fully recognized by computer scientists and mathematicians as there are still missing connections between the theory and the practical optimization problems. In the proposed research, we will apply statistical physics to improve our understanding on optimization problems, and to apply these findings to derive innovative optimization algorithms readily applicable to a wide range of applications. Both real and state space, small and large systems will be studied. Project Start Year: 2015, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Teaching and learning of municipal solid wastes using an evidence-based approach The project aims to build the capacity of secondary school science and liberal studies teachers to develop teaching and learning activities with reference to MSW management issue by equipping them current knowledge and relevant problem solving techniques. Emphasis will be placed upon the current status and key areas of debate in MSW management strategies in both local and international contexts, accounting for both technical and socio-political aspects. The programme will adopt evidence-based, inquiry-based, field-based and service-based teaching and learning approaches. Rather than telling teachers what to do, the proposed programme empowers teachers by fulfilling their information needs, thus enabling them to aid student to make their own informed decisions. Project Start Year: 2015, Principal Investigator(s): CHENG, Nga Yee Irene 鄭雅儀 (YEUNG, Chi Ho 楊志豪 as Team Member) |

Professional development on innovative use of mobile technology for effective teaching and learning of science and environmental studies While mobile devices have been commonly used for communication and entertainment purpose in almost everyone’s daily-life and the Hong Kong Chief Executive’s Policy Address 2014 indicates the government’s determination to broadly implement e-learning in schools in the next few years, many schools and their teachers are not yet ready and confident enough (in terms of their teaching methods, strategies and approaches) to adopt mobile devices in their classroom activities. Being a prominent teacher education institution in the Asia-Pacific region, we should go far beyond the elementary practice of BYOD and need to take a pioneer’s role to help the education sector by offering relevant training to our pre-service student-teachers and in-service teachers as based on our own research, teaching development and knowledge transfer projects which involve mobile software/apps and hardware (e.g. data-logging system, digital cameras, smart phones, tablets and their built-in sensors like GPS, light and sound sensors etc.). In order to achieve that important educational goal, we will first empower our own capacity for mastering the new mobile technology for innovative design of experiments (at normally affordable cost) and project work (with much more different topics) for intensive engagement of students’ in scientific investigation and field trip activities (such as on-site hands-on measurement of indoor and outdoor air quality, noise, light and water pollution in rivers and seas, carbon dioxide level and UV intensity etc.) so as to induce students’ more in-depth understanding and concern of major environmental issues. Through a series of sharing workshops, we will develop new pedagogies with classroom implementation for the effective use of selected or newly designed mobile apps to promote the culture and practice for students’ self-regulated learning, collaborative learning and educationally meaningful sharing of information and ideas (in form of different multimedia materials) through the mobile devices. Project Start Year: 2014, Principal Investigator(s): YEUNG, Yau Yuen 楊友源 (YEUNG, Chi Ho 楊志豪 as Team Member) |

From Traffic Coordination to Failure Adaptation in Transportation Networks Traffic congestions are common in global cities. While road expansion is not always feasible and is not a sustainable approach, optimizing and coordinating traffic flows become the only solution. Unlike existing navigation methods which suggest several alternative routes for individual users to choose, a traffic coordination system will assign a path to each individual such that a global objective, e.g. congestion mitigation, is achieved. It is a computationally difficult task since the routes of all vehicles have to be determined and coordinated simultaneously. In this research project, we will tackle the problem through statistical physics. Simple models of transportation network will be constructed, simulated and analyzed, and existing static path coordination methods will be applied. A new algorithm for dynamical path coordination will be derived by considering repeated network adaptation. The algorithms will be tested on real datasets to examine its effectiveness in real applications. We will also reveal the impact of failures on transportation networks, and adaptation at individual and system levels. Unlike conventional heuristics approaches, laws governing traffic dynamics will be identified and then developed into useful applications. The results will have important environmental, social and economical impacts, and will contribute to the sustainability of existing infrastructures. Project Start Year: 2014, Principal Investigator(s): YEUNG, Chi Ho 楊志豪 |

Innovative ways of using mobile devices for supporting e-learning in science and environmental studies within and outside the classroom environment For nearly two dozen e-Learning devices which have been or are being acquired by the SES Department, there are a number of built-in sensors (e.g. microphone, light sensor, gravity sensor, gyroscope, magnetic sensor and GPS etc.) and external sensors (e.g. CO and CO2 sensors, temperature and pressure sensors, infrared temperature sensor etc) and corresponding Apps which could be innovatively used for carrying out various scientific experiments in various science-related courses within and outside the classroom environment (especially for field trips). This proposal aims to develop some exemplary scientific investigation experiments (with worksheets and guiding questions) as based on those e-Learning devices and sensors for pilot implementation in selected courses. Project Start Year: 2014, Principal Investigator(s): YEUNG, Yau Yuen 楊友源 (YEUNG, Chi Ho 楊志豪 as Team Member) |