Staff Profile

Hello, welcome to my information section! 

I’m currently working as a Senior Lecturer of the Scalable Computing Research Group. Before joining Newcastle University, I worked as a lecturer of the Department of Computer Science of University of Reading, research fellow at LIP6, Pierre et Marie Curie University and French National Center for Scientific Research (CNRS), Department of Computing and Information Systems of University of Melbourne, and Research School of Computer Science. I did my Ph.D study in Web Intelligence and Data Mining Group, QUT, Australia. I am a Fellow of The Higher Education Academy (HEA), UK. 

My research interests include: Data Mining, Machine Learning, Natural Language Processing, Recommender Systems, Personalisation. You can find more detailed information from my personal website. 

My current research lies in the areas of Data Science and Artificial Intelligence (Natural Language Processing). I try to answer these questions:

1) How to make better personalization and recommendations in the era of Big Data? For example, explainable Recommender Systems, conversational Recommender Systems, trust-worthy Recommender Systems.

2) How to discover new knowledge based on complex, large-scale, noisy, diverse, and dynamic data? For example, social data (e.g., social media posts in Twitter), pervasive data (e.g., exercises data in smart phones), text data, image data, environment data, graph-structured data.

3) What is intelligence and how to model, test, and apply findings and theories of biological learning to machines?

• Grants

1) £217,166 (PI) Innovate UK funding for a 2–year KTP project titled “To accelerate product innovation and increase business productivity by automating the construction of an enterprise-level knowledge management and discovery platform via the use of novel Generative AI, Large Language Models, and human-AI collaboration techniques”. Industry partner: P&G. 2025-2027

2) £30,912 (PI) funding from P&G for a 3 months project on “Probabilistic Modelling and Large Language Modelling”. 4/2025-8/2025. Industry partner: P&G

3) £10K (Co-I) award in a joint project in A Large Language Model based Dementia patient data collection chatbot from NIHR Newcastle BRC’s Dementia, Mental Health and Neurodegeneration Theme Accelerator Project Call

4) 05/2024-08/2024 (PI) P&G and Newcastle University co-funded project: Feasibility Study of constructing a Knowledge and data management platform based on Large Language Models and domain knowledge graphs. Project in total: £67,507, Co-I: Stephen McGough.

5) 08/2024 -08/2026 (Co-I) Innovate UK KTP project: Transforming Ryder Architecture: empowering business productivity and services with a novel Large Language Model and Artificial Intelligence System, project in total £189,212. PI: Prof. Mohamad Kassem, Engineering School.

6) 05/2023 – 09/2023 (PI) Innovate UK in Professional & Financial Service project: An AI-powered tool for enhancing consumer trust in financial promotions and marketing (Co-I: A Karim Aldohni). Industry partner: start-up company Deriskly

7) 1/1/2021-30/12/2022 (PI) Collaborative Innovation Fund of Royal Berkshire NHS Trust Foundation and University of Reading project: Improving preoperative diagnosis of thyroid nodules by developing ultrasound artificial intelligence (AI) decision support system. Collaborators: Royal Berkshire Hospital Trust Foundation, NHS,

8) 1/1/2019-31/12/2021 (Co-I) EIT Food/Horizon2020 project: Developing a Digital Toolkit to Enhance the Communication of Scientific Health Claims (Project in total €495,204 for phase 1, €708,622 for phase 2, €466,000 for phase 3), collaborators: Department of English Language and Applied Linguistics, Department of Design, the School of Agriculture, Policy and Development of University of Reading, Technical University of Munich (TUM), British Nutrition Foundation, start-up company Food Maestro, food company Maspex, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences.

• Vacancies/Scholarships

I have multiple Phd studentship in generative models (e.g., Large Language Models), NLP, recommender systems, knowledge graphs. Please contact me first (myfirstname.lastname@newcastle.ac.uk), before submitting the formal application form.

1) an EPSRC DTP Phd studentship, deadline: 15 Feb 2024. Full scholarship, international students can apply. https://www.ncl.ac.uk/computing/study/postgraduate-research/phd-studentships/, choose project "Dialogue Agents with Human Level Attributes - Huizhi Liang"

2) an EPSRC CASE Phd studentship, deadline: end of Feb 2024. Full scholarship, international students can apply: Mitigating Hallucination of Large Language Models with Knowledge graph based recommendation techniques. https://www.ncl.ac.uk/postgraduate/fees-funding/search-funding/?code=comp2157

3) a home studentship, deadline: end of Feb 2024. International students also can apply, but need to apply for a university level scholarship (https://www.ncl.ac.uk/postgraduate/fees-funding/search-funding/?code=nuors24) to bridging the fee gap between home and international student. Or need to self-fund for the fee gap (About 6.5K tuition fee + living expense). Multi-modality conversational recommender systems. Apply: https://www.ncl.ac.uk/postgraduate/fees-funding/search-funding/?code=comp2156

4) competitive GTA studentship, Deadline: 8 Feb 2024. Full scholarship, international students can apply: https://jobs.ncl.ac.uk/job/Newcastle-Postgraduate-Teaching-Assistant/1023830501/

5) CSC phd studentship for Chinese student. Deadline: end of Jan 2024. Full scholarship. Apply: https://www.ncl.ac.uk/postgraduate/fees-funding/search-funding/?code=cscnu24

• Available PhD Student Projects

User Profiling for Personalisation. This project is to develop scalable and effective explicit and implicit user profiling approaches to discover new knowledge about users’ individual interests, preferences, emotional status, and information needs. It will use advanced natural language understanding, Large Language Models, generative models, reinforcement learning, and deep learning techniques to construct user profiles based on both explicit and implicit multi-modality (images, text, audio, graph, temporal data) user behaviour data. The proposed user profiling techniques will be applied to recommender systems to make personalised recommendations.

Hashing Techniques for High Dimensional Data. Hashing is a key technique to analyse big data. It has been popularly used for dimensionality reduction and data size reduction. This project is to develop novel hashing algorithms to sample, compress, and index big data such as multi-modality data (images, text, audio, graph, temporal data) to facilitate effective and efficient information retrieval and recommendation. This project will also explore machine learning based hashing techniques. This project will contribute to new solutions to make better usage and processing of big data.

Other directions such as Responsible Recommender Systems (e.g., trustworthy, safe), Conversational Recommender Systems, Dialogue agents for mental health, Scalable Recommender Systems are also available. Note: Good programming skills and theoretical modelling are required for all projects.

Fellow of The Higher Education Academy (HEA), UK

• CSC8645 Advanced AI 2024-2025
• CSC8637 Deep Learning 2024-2-25
• CSC8111, 8635, 8630, 8644 Machine Learning 2022-2023
• CSC8633 Group Project in Data Science 2021-2022
• CSC8639 Project and Dissertation in Data Science 2021-2022

• Articles

  • Zhu T, Duan S, Liang H, Li F, Zhang W. Multiangle correlation feature extraction and disease prediction model construction for patients with post-stroke dysarthria. IEEE Transactions on Neural Systems & Rehabilitation Engineering 2025, 33, 587-597.
  • Zhang H, Wang D, Sun Z, Li Y, Sun Y, Liang H, Wang W. KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?. ACM Transactions on Information Systems 2025, 43(3), 1-36.
  • Li H, Zhang X, Duan S, Liang H. Speech emotion recognition based on bi-directional acoustic–articulatory conversion. Knowledge-Based Systems 2024, 299, 112123.
  • Markchom T, Liang H, Ferryman J. Scalable and Explainable Visually-Aware Recommender Systems. Knowledge-Based Systems 2023, 263, 110258.
  • Markchom T, Liang H, Ferryman J. Explainable Meta-Path Based Recommender Systems. ACM Transactions on Recommender Systems 2023, 3(2), 16.
  • Liang H, Markchom T. TNE: a general time-aware network representation learning framework for temporal applications. Knowledge-Based Systems 2022, 240, 108050.
  • Liang H. DRprofiling: deep reinforcement user profiling for recommendations in heterogenous information networks. IEEE Transactions on Knowledge and Data Engineering 2022, 34(4), 1723-1734.
  • Liang H, Ganeshbabu U, Thorne T. A dynamic Bayesian network approach for analysing topic-sentiment evolution. IEEE Access 2020, 8, 54164-54174.
  • Wang Q, Cui M, Liang H. Semantic-aware blocking for entity resolution. Transactions on Knowledge and Data Engineering 2016, 28(1), 166-180.
  • Ramadan B, Christen P, Liang H, Gayler R. Dynamic sorted neighborhood indexing for real-time entity resolution. Journal of Data and Information Quality 2015, 6(4), 15.

• Conference Proceedings (inc. Abstracts)

  • Rusnachenko N, Liang H. Personality Profiling for Literary Character Dialogue Agents with Human Level Attributes. In: 10th International Conference on Machine Learning, Optimization and Data Science. 2025, Tuscany, Italy: Springer-Nature.
  • Alqattan D, Sun R, Liang H, Nicosia G, Snasel V, Ranjan R, Ojha V. Security Assessment of Hierarchical Federated Deep Learning. In: 33rd International Conference on Artificial Neural Networks (ICANN 2024). 2024, Lugano-Viganello, Switzerland: European Neural Network Society.
  • Zheng Z, Liang H, Snasel V, Latora V, Pardalos P, Nicosia G, Ojha V. On Learnable Parameters of Optimal and Suboptimal Deep Learning Models. In: 38th International Conference on Neural Information Processing. 2024, Vancouver Convention Center: ACM. In Press.
  • Kalathil S, Li T, Hang D, Liang H. HTEKG: A Human-Trait-Enhanced Literary Knowledge Graph with Language Model Evaluation. In: 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management KEOD. 2024, Porto: SciTePress.
  • Liu Z, Liang H. Cross-Metapath based Hashing for Recommendation Systems. In: 10th International Conference on machine Learning, Optimization & Data science – LOD 2024. 2024, Castiglione della Pescaia, Italy: Springer-Nature.
  • Rusnachenko Nicolay, Liang Huizhi, Kolomeets Maxim, Shi Lei. ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction. In: The 46th European Conference on Information Retrieval (ECIR). 2024, Glasgow, Scotland: Springer. In Press.
  • Morris J, Liu Z, Liang H, Nagala S, Hong X. ThyExp: An explainable AI-assisted Decision Making Toolkit for Thyroid Nodule Diagnosis based on Ultra-sound Images. In: 32nd ACM International Conference on Information & Knowledge Management (CIKM '23). 2023, Birmingham, UK.
  • Wang Y, Liang H, Zhai B. Temporal Neighborhood based Self-supervised Pre-training Model for Sleep Stages Classification. In: 2023 15th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2023). 2023, Xi'an, China: Association for Computing Machinery - New York United States.
  • Zhao J, Liang H, Rusnachenko N. Dialogue Agents with Literary Character Personality Traits. In: 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). 2023, Venice, Italy: IEEE.
  • Liang H, Liu Z, Markchom T. Relation-aware Blocking for Scalable Recommendation Systems. In: 31st ACM International Conference on Information & Knowledge Management (CIKM '22). 2022, Atlanta, Georgia, USA: ACM.
  • Li X, Liang H, Nagala S, Chen J. Improving Ultrasound Image Classification With Local Texture Quantisation. In: The International Conference on Acoustics, Speech, & Signal Processing (ICASSP 2022). 2022, Singapore: IEEE.
  • Li X, Liang H, Ryder C, Jones R, Liu Z. Attractiveness Analysis for Health claims on Food Packages. In: 20th Australasian Conference Data Mining (AusDM 2022). 2022, Western Sydney, Australia: Springer.
  • Li X, Liang H, Liu Z. Health claims unpacked: a toolkit to enhance the communication of health claims for food. In: CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, Queensland, Australia (Online): ACM.
  • Markchom T, Liang H. Augmenting visual information in knowledge graphs for recommendations. In: 26th International Conference on Intelligent User Interfaces (IUI '21). 2021, College Station, TX, USA: ACM.