Staff Profile

Biography

Hi! I'm Quinne Yan Lin, currently pursuing a Ph.D in Computer Science, working on multimodal learning (start in March 2025, the first-year PhD student now). My research is fully funded by Postgraduate Research Studentship (5-year package; covering full international tuition fees and a tax-free living UKRI stipend; equivalent in scope to a U.S. Teaching Assistantship).

I am working as a Postgraduate Teaching Assistant (regular fixed-term staff; contract is 5-year and co-terminous with the PhD; all staff leave, sick-pay and professional-development entitlements apply from day on) in the School of Computing. Duties: Lab demonstrator and Marker; Invigilator; IT maintenance technician; MSc & Mphil dissertation mentor; Open day ambassor etc. I am also completing the Associate Fellowship Pathway through the Newcastle Univeristy.

Current Research

My research interests centre around Large Language Models (LLMs), multimodal learning, and the development of LLM-based intelligent agents. I am particularly interested in the practical engineering and deployment of transformer-based models for tasks such as text generation, multi-step reasoning, multilingual comprehension, and instruction following.

Having transitioned from a background in finance and data science into computer science, I bring a strong applied perspective to my research. I am very interested in LLMs & Agents related potential collaboration opportunities.

About Me

Originally from China, I earned my BA in Finance and Economics in Mainland China and Taiwan. After that, I worked for three years in China's four largest state-owned banks and ten largest brokerage firms.

With my interest in math and programming, I changed my major to Master of Data Science at Durham University (2022-2023, average 78%). My Machine Learning course assignment received a 90% first place high score and my Critical Perspectives in Data Science course essay was chosen as the example for the next cohort. (Others: Introduction to Mathematics for Data Science - 92%, Programming for Data Science - 83%, Introduction to Statistics for Data Science - 82%, Data Exploration, Visualization and Unsupervised Learning - 80%, Introduction to Computer Science - 76%, etc). My Durham dissertation used an integrated neural network model to predict the outcome of The Hundred cricket matches, achieving a successful prediction rate of 76%. I had also served as a student representative and postgraduate ambassador at Durham University, and am a member of the GCR at Ustinov College, supporting sexual miniortiy.

Thanks to these Durham teachers of mine (Professor Camila Caiado[master's programme director] Dr Sarah Heaps [ISDS course lead, rl]; Dr Hailiang Du [ML course lead, rl] ; Professor Fred Worrall [dissertation supervisor]), I have been brave enough to pursue the PhD path. I have also attended OXML2024 (Representation Learning and Generative AI summer school at University of Oxford's Mathematical Institute) in Aug 2024. I am a student member of IEEE.

In the 2024 PhD application journey, I got 6 fully-funded international scholarships from Russell Group universities (QS 20–150), such as Durham University Business School Scholarship (2024-2027), Durham University joint PhD Scholarship - China Scholarship Council (2024-2027), Newcastle University 5-year Computing PhD Scholarship (2025-2030), and Queen Mary University of London 5-year MRes+PhD scholarship (2024-2029), etc.

Beyond academic research, I enjoy playing script-based games like murder mystery games, escape rooms, and watching detective series. I am a huge fan of Sherlock Holmes. I am proud to be a member of Women in STEM and regularly update my study notes to help non-technical individuals get started with AI.

I welcome any enquiries regarding my research, teaching, or potential collaboration opportunities (I am very interested in).

Please feel free to get in touch.

y.lin64@ncl.ac.uk (student account, research& collaboration enquiry)

quinne.lin@ncl.ac.uk (staff account, teaching enquiry)

Potential Collaborations: If you are interested in these topics, feel free to reach out for more information!

Interests：

• Reducing hallucination and improving factual grounding in LLMs
• Building LLM agents that can reason, plan, and act in open-ended environments
• Prompting, fine-tuning, and evaluation of LLMs for real-world applications
• Integrating LLMs into interactive systems and tools (e.g., RAG, tool-use, multi-agent collaboration)
• Exploring responsible and explainable AI, especially in multilingual and cross-cultural settings

I consider myself an LLM engineer and applied researcher, with a keen interest in aligning generative AI systems with human needs and real-world challenges.

Technical Toolkit:

NLP Toolkits: Huggingface, NLTK, spaCy, FairSeq, Stanford CoreNLP, AllenNLP, GenSim 

Deep Learning Toolkits: TensorFlow (1.X, 2.0), PyTorch, Keras, PaddlePaddle 

Machine Learning Toolkits: Scikit-learn, Numpy, Pandas

Data Analysis Toolkits: R/Rstudio, Python, Seaborn and Matplotlib (Visualization), PowerBI

Web Scraping: Selenium, BeautifulSoup, Scrapy

Automation & LLM Agents: LangChain, AutoGen, OpenAI function-calling, multi-agent frameworks

Programmings: Python, C/C++, Java/JavaScript, R/Rstudio, MySQL, Matlab

Econometrics, Finance & Quantitative Tools: Stata, Eviews, SPSS, Wind, Bloomberg Terminal

I enjoy teaching various subjects in the field of artificial intelligence and am passionate about explaining advanced AI concepts in clear and understandable language to people without a computer science background, especially young audiences.

Teaching at Newcastle University

CSC1035 Programming Portfolio 2 - Java

Undergraduate Lab Demonstator & Marker, School of Computing, 2024–25(Already finished), 2025-26 (Already assigned)

Supported teaching for first-year BSc Computer Science students during Semesters 2 and 3. Delivered weekly practical programming labs (contact hours: 88), conducted preparation sessions (33 hours), and marked assignments and final assessments (42 hours).

Total hours: 163

CSC1034 Programming Portfolio 1 - Java

Undergraduate Lab Demonstator & Marker, School of Computing, 2025-26 (Already assigned)

Additional Teaching Responsibilities

Lab Testing & Maintenance

Induction Week Support

Student Recruitment 

Exam Invigilation : Supplementary Exam Series Invigilator

MSc &Mphil Summer Dissertation Project Support (Jul. - Aug. 2025):

• Project and Dissertation for MSc in Computer Science - CSC8099
• Project and Dissertation for MSc in Cloud Computing - CSC8199
• Project and Dissertation for MSc in Advanced Computer Science - CSC8499