Dr Yunpeng Li

Lecturer in Artificial Intelligence
+44 (0)1483 682626
14 BB 02
Fridays 1pm - 3pm


Areas of specialism

Bayesian inference; Statistical machine learning; Monte Carlo sampling; High-dimensional statistics; Target tracking.

In the media

Malaria: Costs and Cures
BBC World Service: Business Daily


Research interests

Research projects


Postgraduate research supervision

Completed postgraduate research projects I have supervised

My teaching

My publications


Google Scholar maintains an update-to-date list of my publications.


Isupova Olga, Li Yunpeng, Kuzin Danil, Roberts Stephen J, Willis Katherine, Reece Steven (2018) BCCNet: Bayesian classifier combination neural
NIPS 2018 workshop proceedings
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to
quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-theart machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an
automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.
Li Yunpeng, Pal Soumyasundar, Coates Mark J. (2019) Invertible Particle-Flow-Based Sequential MCMC With Extension to Gaussian Mixture Noise Models,IEEE TRANSACTIONS ON SIGNAL PROCESSING 67 (9) pp. 2499-2512 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sequential state estimation in non-linear and non-Gaussian state spaces has a wide range of applications in statistics and signal processing. One of the most effective non-linear filtering approaches, particle filtering, suffers from weight degeneracy in high-dimensional filtering scenarios. Several avenues have been pursued to address high-dimensionality. Among these, particle flow particle filters construct effective proposal distributions by using invertible flow to migrate particles continuously from the prior distribution to the posterior, and sequential Markov chain Monte Carlo (SMCMC) methods use a Metropolis-Hastings (MH) accept-reject approach to improve filtering performance. In this
paper, we propose to combine the strengths of invertible particle flow and SMCMC by constructing a composite Metropolis-Hastings (MH) kernel within the SMCMC framework using invertible particle flow. In addition, we propose a Gaussian mixture model (GMM)-based particle flow algorithm to construct effective MH kernels for multi-modal distributions. Simulation results show that for high-dimensional state estimation example problems the proposed kernels significantly increase the acceptance rate with minimal additional computational overhead
and improve estimation accuracy compared with state-of-the-art filtering algorithms.