Yu Zhang
PhD Researcher and Research Assistant · University of Surrey
Guildford, United Kingdom
School of Chemistry and Chemical Engineering
I develop data-efficient machine learning and digital-twin methods for engineering and healthcare. My work combines probabilistic modelling, Bayesian optimisation, active learning, uncertainty quantification and interpretable models to support decisions when experiments or observations are limited.
At Surrey, my PhD focuses on AI-enabled formulation design and drug delivery. Alongside my doctoral research, I work with Dr Xilu Wang as a Research Assistant on real-world clinical data in assisted reproduction (IVF), developing reproducible workflows for outcome modelling and patient-aware, time-aware validation.
Formulation research programme
Measure
IVRT and IVPT experiments generate time-resolved evidence about drug release and permeation from topical formulations.
Learn
Gaussian processes and hybrid models learn formulation-performance relationships while preserving predictive uncertainty.
Decide
Active learning, Bayesian optimisation and early stopping focus each experimental batch on the most useful decisions.
Explain
Domain-constrained symbolic regression and scientific LLMs connect predictions with mechanisms and literature evidence.
Current research directions
- Clinical machine learning - defining outcomes, constructing reliable features and developing patient-aware, time-aware validation for assisted-reproduction data. Research overview.
- Adaptive formulation optimisation - allocating small experimental batches between performance-seeking and information-seeking candidates.
- Early experimental decision-making - forecasting final outcomes from partial IVPT/IVRT trajectories and controlling false stops.
- Interpretable dynamic modelling - embedding physical and mathematical constraints into symbolic regression for drug-release kinetics.
- Scientific LLM systems - extracting traceable formulation and permeation evidence from literature and building reusable research datasets.
Background
I am pursuing a PhD in Chemical and Process Engineering at the University of Surrey, supervised by Professor Tao Chen and Dr Dimitrios Tsaoulidis. Expected thesis submission: late 2026 / early 2027.
My research experience includes placements at Jagiellonian University, Poland (June-August 2026), supported by COST STSM, the UK Turing Scheme and FEPS, and at West China Hospital, Sichuan University (January-March 2025).
Before Surrey, I completed an MEng in Control Science and Engineering at China University of Petroleum - Beijing and a BEng in Automation at Panzhihua University. My earlier work spans industrial process monitoring, fault diagnosis, data-driven analysis of reservoir-simulation outputs and energy-market software. This background informs my focus on reproducible models that remain useful under real experimental and data constraints.
news
| Oct 28, 2026 | Research update: our co-authored manuscript, A Spectral-Continuity-Guided Bayesian Learning Framework for Infrared Spectroscopy-Based Soft Sensor Modeling, is under review at Measurement. |
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| Oct 27, 2026 | SkinMiner: LLM-powered data mining to accelerate formulation design for skin products is available as an SSRN preprint. The manuscript is under review at International Journal of Pharmaceutics. Code |
| Oct 08, 2026 | Our co-authored review, “Optimisation of Topical and Transdermal Pharmaceutical Formulations: Methods, Challenges and Opportunities”, has been accepted by SmartSys. Publication details are forthcoming. |
| Aug 24, 2026 | Presented “LLM-powered multimodal agentic data mining for dermal formulation science” at the 28th International Congress of Chemical and Process Engineering (CHISA 2026) in Prague, Czech Republic. SkinMiner |