Spotlight on Excellence: Talks by the 2026 President’s Award and Exceptional Student Award Recipients

THE SPEAKERS 

Martin Poinsinet de Sivry, PhD, Postdoctoral Researcher - Amsterdam UMC
President's Award for Excellence, 2026

Dr. Martin Poinsinet de Sivry is a postdoctoral researcher in biomedical optics at Amsterdam UMC. He holds a PhD in Engineering Physics from Polytechnique Montréal, where he specialized in optical modelling, numerical simulation, and light scattering. His research focuses on flow cytometry, biomedical imaging, fibre optics, and particle characterization. He develops high-performance scientific software in Python and C++ and is the lead developer of several open-source projects, including PyMieSim, SuPyMode, and FlowCyPy. His current work explores new signal processing technique for flow-cytometry systems.

Ruby Hamilton, PhD Student - The University of Western Australia
Exceptional Student Award, 2026

Ruby Hamilton is a PhD student at the University of Western Australia. 

         
WEBINAR SUMMARY
This webinar will feature two 20-minute presentations by distinguished past award recipients. Each year at the CYTO meeting, early-career scientists compete for these highly regarded honors, which recognize outstanding scientific achievement and promise. The competitions are open to all eligible CYTO attendees, making these awards among the most competitive and prestigious recognitions within the community.

In addition to being honored at CYTO, award recipients are invited to present a CYTO U Webinar, providing the broader community with the opportunity to learn directly from their innovative research and insights.

To learn more about these awards, please visit the ISAC Awards page.

Beyond Coincidence: Leveraging Neural Networks to Overcome Coincidence in Flow-Cytometry – Presented by Martin Poinsinet de Sivry
In flow cytometry, increasing the particle rate improves measurement throughput but also increases the probability that multiple particles cross the interrogation region simultaneously. The resulting overlapping detector pulses, known as coincidence, can cause particles to be missed, merged, or assigned biased signal amplitudes. This webinar presents FLASH, a signal-processing pipeline designed to overcome this coincidence bottleneck in small-particle flow cytometry. FLASH combines a convolutional neural network for particle-event localization with a physics-based solver that reconstructs the amplitude of individual overlapping pulses. The approach is evaluated using digitized side-scatter signals from 300 nm polystyrene beads measured on a modified BD FACSCanto II across particle rates approaching one million particles per second. In the absence of direct ground truth, performance is assessed through physical consistency checks involving particle arrival-time statistics, amplitude distributions, and count scaling with concentration. Compared with conventional threshold-based detection, FLASH preserves plausible event timing, reduces coincidence-induced amplitude bias, and extends the usable throughput range by more than an order of magnitude. The webinar will discuss the underlying methodology, experimental implementation, validation strategy, current limitations, and prospects for real-time FPGA deployment and physics-informed analysis.

Imaging Flow Cytometry Detection of Cytogenetic Abnormalities in Circulating CD34+ Cells in Myelofibrosis - Presented by Ruby Hamilton
Patients with myelofibrosis can progress to acute myeloid leukemia (AML) following acquisition of new genetic changes that arise of cells in the bone marrow. Once patients progress to AML, they are typically refractory to treatment and have a median survival of less than 6 months. Evolution to AML is unpredictable and current tests lack the sensitivity and applicability needed to predict when transformation will occur. We aimed to assess prognostically important chromosomal abnormalities in circulating CD34/CD45-positive cells in myelofibrosis patients using an imaging flow cytometry method (immuno-flowFISH) that integrates immunophenotyping and fluorescence in situ hybridization. We also aimed to monitor changes in clonal evolution and burden throughout disease progression to see if we could identify these changes earlier than current methods. We propose that blood monitoring using immuno-flowFISH will enable earlier identification of high-risk clonal evolution, such as del(17p), providing a novel framework for disease surveillance which may improve prediction of leukemic transformation in myelofibrosis.

KEYWORDS: ISAC Webinar Featuring ESA and PAE Winners, Leukemia, Chromosomes, Imaging, Monitoring, Biomarkers, Flow Cytometry, Coincidence, Neural Networks, Signal Deconvolution, High-Throughput Analysis