Dr Giuseppe Di Caprio

Strathclyde Chancellor's Fellow

Biomedical Engineering

Personal statement

I am an interdisciplinary biophysicist interested in Microscopy, Quantitative Bioimage Analysis, Microfluidics, Organs-On-Chip, and Machine Learning. My doctoral studies were in novel technologies for imaging, as part of a joint program between the University of Naples Federico II and the Université Libre de Bruxelles. Following this, I spent 11 years at Harvard University, beginning in the Schonbrun Lab at the Rowland Institute, where I developed microscopy systems for imaging flow cytometry. Subsequently, I transitioned to the Kirchhausen Lab as junior faculty at the Harvard Medical School - Boston Children's Hospital, focusing on implementing computational solutions for quantifying biological data from 4D fluorescent- and 3D electron-microscopy. In 2023, I was appointed as Senior Lecturer and Chancellor's Fellow at the University of Strathclyde.

For more information, please visit our lab website.

Expertise

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Prize And Awards

Strathclyde Chancellor's Fellow: Recipient; 11/4/2023
Instructor in Pediatrics (Research Assistant Professor) - Harvard Medical School: Recipient; 1/2/2020
Research Fellow in Cell Biology - Harvard Medical School: Recipient; 1/9/2015
Post-doctoral Research Fellow - Harvard University: Recipient; 21/6/2012

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Publications

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Publications

AI-enabled lyophilization of advanced biologics : process intelligence, quality assessment, and formulation opportunities: Liu Xiangjian, Di Caprio Giuseppe, Du Yanan; International Journal of Pharmaceutics Vol 702 (2026); https://doi.org/10.1016/j.ijpharm.2026.127227
Fourier ptychography microscopy for digital pathology: Eadie Fraser, Copeland Laura, Di Caprio Giuseppe, McConnell Gail, Kallepalli Akhil; Journal of Microscopy Vol 300, pp. 260-285 (2025); https://doi.org/10.1111/jmi.70001
Polarised Fourier ptychography for volumetric imaging of anisotropic specimens through deep learning enhancements: Eadie Fraser, Di Caprio Giuseppe, Kallepalli Akhil; Microscience Microscopy Congress 2025 incorporating EMAG 2025 (2025)
Genetic reversal of the globin switch concurrently modulates both fetal and sickle hemoglobin and reduces red cell sickling: De Souza Daniel C, Hebert Nicolas, Esrick Erica B, Ciuculescu M Felicia, Archer Natasha M, Armant Myriam, Audureau Étienne, Brendel Christian, Di Caprio Giuseppe, Galactéros Frédéric, Liu Donghui, McCabe Amanda, Morris Emily, Schonbrun Ethan, Williams Dillon, Wood David K, Williams David A, Bartolucci Pablo, Higgins John M; Nature Communications Vol 14 (2023); https://doi.org/10.1038/s41467-023-40923-5
Deep neural network automated segmentation of cellular structures in volume electron microscopy: Gallusser Benjamin, Maltese Giorgio, Di Caprio Giuseppe, Vadakkan Tegy John, Sanyal Anwesha, Somerville Elliott, Sahasrabudhe Mihir, O’connor Justin, Weigert Martin, Kirchhausen Tom; Journal of Cell Biology Vol 222 (2023); https://doi.org/10.1083/jcb.202208005
Inherited nuclear pore substructures template post-mitotic pore assembly: Chou Yi Ying, Upadhyayula Srigokul, Houser Justin, He Kangmin, Skillern Wesley, Scanavachi Gustavo, Dang Song, Sanyal Anwesha, Ohashi Kazuka G, Di Caprio Giuseppe, Kreutzberger Alex JB, Vadakkan Tegy John, Kirchhausen Tom; Developmental Cell Vol 56 (2021); https://doi.org/10.1016/j.devcel.2021.05.015

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Research

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Research Interests

In the Smart Microscopy Lab we focus on understanding how to connect bioimage analysis with computer-controlled microscopy to generate automated and adaptive imaging workflows and to enable quantitative and statistically meaningful results in complex biological systems. This approach brings new challenges, such as performing real-time image processing, accessing and controlling instrument hardware via suitable software, designing efficient workflows, and handling large data sets during operation. The possibilities to combine different imaging technologies, to trigger imaging modalities based on the real-time image analysis results, and to synchronize image acquisition with external devices for sample manipulation (i.e., liquid handling or cell manipulation) enable the automation of complex workflows. Such workflows allow researchers to acquire images in a less biased, more reproducible, and faster way than manual imaging by means of experiments otherwise hardly feasible.

AI-powered Smart Microscopy for Stem-Cell Engineering

Cell engineering is predicted to be part of a regenerative medicine focused future. The human Mesenchymal Stem Cells (hMSC) are a cell type of major interest that are available from bone marrow, adipose tissue, and umbilical cord sources. Over 900 hMSC clinical trials have been conducted since 2004 and thousands of academic publications on potential hMSC treatments are published yearly. Despite this significant investigative effort, only few therapeutic products containing hMSCs or their derivative cells are approved yearly, mainly due to hMSC therapies requiring extensive safety testing by an independent contract research organization, sterile cold chain, and increased surgical time.

Our goal is to introduce a completely new paradigm in stem cell culturing where the standard trial and error approach to identify a suitable recipe is substituted by quantitative and continuous optimization. We intend to implement a next-generation bio-manufacturing process in which the stimulation is finely tuned in real time, based on the specific dynamics of the population, to overcome the issues associated with biological diversity and patient-specific conditions. The possibilities to combine different imaging technologies, to trigger imaging modalities based on the real-time image analysis results, and to synchronize image acquisition with external devices for sample manipulation (i.e., liquid handling or cell manipulation) enable the automation of complex workflows.

Quantitative Absorption Microscopy - A Circulatory System on Chip

This project is dedicated to the development of material technologies for mimicking the environment of the Circulatory System on Chip. In addition to the organ-on-chip fabrication, I am interested in performing quantitative measurements of other physical characteristics of cells, such as volume, protein mass, and oxygen tension. Resolving the distribution of different measured parameters enables better understanding of not only mean values of a cell population but also subtle differences in the response of each cell to its environment, genetic differences, and exposure to small molecules.

Professional Activities

University Of Strathclyde (Organisational unit): Member; 28/8/2026
Smart Microscopy for AI-Driven Cell Engineering and Quantitative Analysis of Cells in Flow: Speaker; 6/8/2026
6th International Symposium for Mechanobiology (ISMB) 2026: Member of programme committee; 12/7/2026
Lead – Artificial Intelligence & Data-Driven Discovery, Centre for the Cellular Microenvironment (External organisation): Advisor; 1/7/2026
Faculty Of Engineering (Organisational unit): Member; 1/6/2026
Faculty Unit Engineering (Organisational unit): Member; 1/6/2026

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Projects

PhD Research Visit - University of Gottingen (Philip Graemer): Di Caprio, Giuseppe (Principal Investigator) Graemer, Philip (Principal Investigator); Research visit to the University of Göttingen to collaborate with Prof. Philip Graemer’s group and the laboratories of Carmelo Ferrari and Constantin Pape. The visit focuses on analysing experimental data related to mechanobiology with Carmelo Ferrari and developing new research projects in biomedical image segmentation and computational analysis with Constantin Pape.; 30-Jan-2026 - 30-Jan-2026
Carnegie Vacation Scholarships for Undergraduates - Wen Lin: Di Caprio, Giuseppe (Principal Investigator); This project investigates model distillation to develop lightweight AI models for biomedical image segmentation, using MicroSAM, a microscopy-adapted version of the Segment Anything Model (SAM), as a teacher model. The aim is to produce smaller models that retain high segmentation accuracy while substantially reducing memory and computational requirements, enabling deployment on portable and point-of-care imaging systems. Image segmentation identifies meaningful structures such as individual cells, tissues, organs, or disease-related features, enabling quantitative measurements that support biomedical research, diagnosis, and treatment planning. Although the project focuses on microscopy, the approach has broader potential across biomedical imaging, including MRI and CT. The resulting models are evaluated in terms of accuracy, speed, and memory usage, with the work primarily conducted using existing microscopy datasets and deep-learning frameworks such as Python and PyTorch.; 15-Jan-2026 - 05-Jan-2026
KU Leuven - PhD Research Visit (Swabra Farzia): Di Caprio, Giuseppe (Principal Investigator) Farzia, Swabra (Principal Investigator); As part of her PhD research, Swabra Farzia undertakes a research visit to the laboratory of Maturilio Sampeolesi in Leuven to gain expertise in cardiac cell culture protocols, cardiac organoid engineering, and the application of paracrine therapies. The visit provides practical training and supports the development of experimental approaches that complement her doctoral research.; 05-Jan-2026 - 17-Jan-2026
AI-Enhanced Polarization-Sensitive Holotomography for Lipid Droplet Analysis in Cancer Cell: Synthetic Data Generation and Vision Transformer-Based Classification: Di Caprio, Giuseppe (Principal Investigator); 01-Jan-2025 - 30-Jan-2027
BBSRC NorthWest Biosciences DTP 2025 | Renata Cia Sanches Loberto: Kallepalli, Akhil (Principal Investigator) Di Caprio, Giuseppe (Co-investigator) Cia Sanches Loberto, Renata (Research Co-investigator); Understanding glioblastoma with one pixel: Imaging into the brain using single-pixel techniques for multi-dimensional investigations; 01-Jan-2025 - 28-Jan-2029
EPSRC Vacation Internship | Engineering Conditional Diffusion Models for Scalable Synthetic Cancer Cell Imaging in Biomedical AI: Di Caprio, Giuseppe (Principal Investigator); EPSRC Summer stundentship. Intern: Grant McClure; 02-Jan-2025 - 17-Jan-2025