Hongpeng Zhou

Hongpeng Zhou is a Dame Kathleen Ollerenshaw Fellow at the Department of Computer Science, University of Manchester. His research focuses on interpretable machine learning for cancer research, Bayesian deep learning, and bioinformatics. Previously, he was a postdoctoral researcher at the same university, developing ML algorithms for single-cell transcriptomics data.

Education:

• PhD in Bayesian deep learning, Delft University of Technology (2018–2022)
• MEng in Control Science, Harbin Institute of Technology (2015–2017)
• BEng in Control Science, Harbin Institute of Technology (2011–2015)

Research Interests: Dr. Zhou specializes in creating interpretable ML models for biological and engineering systems. His work bridges deep learning, Bayesian methods, and domain-specific applications like materials science, geotechnical engineering, and cancer genomics. Core themes include neural network compression, physics-informed learning, and unsupervised exploration of complex datasets.

Publication Trends: Recent articles demonstrate interdisciplinary collaboration across computer science, bioinformatics, and mechanical engineering. Dominant themes include interpretable ML architectures, physics-informed neural networks, and applications in biomedicine/materials science, with emphasis on uncertainty quantification and real-world system optimization.

Awards:

• Dame Kathleen Ollerenshaw Fellowship

Projects & Advising: Leads the TICM R&D project on efficient 3D Gaussian splatting and co-investigates breast cancer prevention initiatives. Accepts PhD students and collaborates with groups like Prof. Magnus Rattray's bioinformatics team.