Artsiom Hramyka

Centre for Cancer Genetic Epidemiology

University of Cambridge

Cambridge, United Kingdom

I’m a Postdoctoral Researcher and Principal Investigator at the Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, working at the intersection of machine learning and medicine. I hold a CRUK ACED Pathway Award to develop dynamic AI approaches for 5-year breast cancer risk prediction from longitudinal mammography, co-mentored by Prof. Nora Pashayan and Prof. Sue Astley, and collaborating with Prof. Fiona Gilbert’s group in the Department of Radiology.

My research focuses on cancer early detection, particularly methods for longitudinal medical imaging and Bayesian calibration of simulation models. Current and recent projects include sequence-aware vision transformers for risk-stratified breast screening, the Transformer Posterior Estimator (TPE) for amortised Bayesian calibration of cancer microsimulation models, and decoder-only GPT-style transformers for follicle distribution prediction during IVF.

I completed my PhD in Computer Science at the University of St Andrews (awarded December 2025) under the supervision of Prof. Tom Kelsey, with a thesis titled Bridging the Chasm: Improved Healthcare Using Both Established and Novel Analytical Frameworks. Before returning to academia I spent two years in industry — as a Software Engineer and then ML Engineer at ING Group in Amsterdam, and as Technical Lead at a high-growth startup.

I’m broadly interested in foundation models, generative AI, and AI for science — particularly applications where careful uncertainty quantification matters.

You can find my CV here. The best way to reach me is by email at ah2427 [at] cam.ac.uk.

news

May 01, 2026	I’ve officially started my CRUK ACED Pathway Award at the University of Cambridge — stepping into my first Principal Investigator role. Two years, £190k, building dynamic AI for 5-year breast cancer risk prediction from longitudinal mammography.
Mar 15, 2026	Returned to the University of St Andrews to guest-lecture for CS5014 (Machine Learning), covering sequence processing — RNNs, attention, and transformers. A full-circle moment.
Dec 15, 2025	Passed my PhD viva! Thesis title: Bridging the Chasm: Improved Healthcare Using Both Established and Novel Analytical Frameworks. Graduation ceremony scheduled for July 2026.
Sep 30, 2025	Spoke at the Cambridge AI in Medicine Seminar Series on Deep Learning-Based Follicle Growth Prediction using a Transformer Architecture — a decoder-only GPT-style model trained on 28k IVF/ICSI cycles.

latest posts

May 14, 2026	Welcome

selected publications

JCEM

Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence

Artsiom Hramyka, Thomas W. Kelsey, Simon Hanassab, Scott M. Nelson, Arthur C. Yeung, Sotirios Saravelos, Rehan Salim, Alexander N. Comninos, and 6 more authors

The Journal of Clinical Endocrinology & Metabolism, 2026

DOI Bib HTML

@article{hramyka2026_jcem_follicle,
  title = {Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence},
  author = {Hramyka, Artsiom and Kelsey, Thomas W. and Hanassab, Simon and Nelson, Scott M. and Yeung, Arthur C. and Saravelos, Sotirios and Salim, Rehan and Comninos, Alexander N. and Tsaneva-Atanasova, Krasimira and Voliotis, Margaritis and Trew, Geoffrey H. and Heinis, Thomas and Dhillo, Waljit S. and Abbara, Ali},
  journal = {The Journal of Clinical Endocrinology \& Metabolism},
  volume = {111},
  number = {3},
  pages = {615--621},
  year = {2026},
  doi = {10.1210/clinem/dgaf539},
}

NatComms

Explainable Artificial Intelligence to Identify Follicles that Optimize Clinical Outcomes During Assisted Conception

Simon Hanassab, Scott M. Nelson, Artur Akbarov, Arthur C. Yeung, Artsiom Hramyka, Toulin Alhamwi, Rehan Salim, Alexander N. Comninos, and 5 more authors

Nature Communications, 2025

DOI Bib HTML

@article{hanassab2025_xai,
  title = {Explainable Artificial Intelligence to Identify Follicles that Optimize Clinical Outcomes During Assisted Conception},
  author = {Hanassab, Simon and Nelson, Scott M. and Akbarov, Artur and Yeung, Arthur C. and Hramyka, Artsiom and Alhamwi, Toulin and Salim, Rehan and Comninos, Alexander N. and Trew, Geoffrey H. and Kelsey, Tom W. and Heinis, Thomas and Dhillo, Waljit S. and Abbara, Ali},
  journal = {Nature Communications},
  volume = {16},
  pages = {296},
  year = {2025},
  doi = {10.1038/s41467-024-55301-y},
}

EU Focus

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer

Sinan Khadhouri, Artsiom Hramyka, Kevin Gallagher, Alexander Light, Simona Ippoliti, Marie Edison, Cameron Alexander, Meghana Kulkarni, and 25 more authors

European Urology Focus, 2024

DOI Bib HTML

@article{khadhouri2024_identify,
  title = {Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer},
  author = {Khadhouri, Sinan and Hramyka, Artsiom and Gallagher, Kevin and Light, Alexander and Ippoliti, Simona and Edison, Marie and Alexander, Cameron and Kulkarni, Meghana and Zimmermann, Eleanor and Nathan, Arjun and Orecchia, Luca and Banthia, Ravi and Piazza, Pietro and Mak, David and Pyrgidis, Nikolaos and Narayan, Prabhat and Abad Lopez, Pablo and Nawaz, Faisal and Tran, Trung-Thanh and Claps, Francesco and Hogan, Donnacha and Gomez Rivas, Juan and Alonso, Santiago and Chibuzo, Ijeoma and Gutierrez Hidalgo, Beatriz and Whitburn, Jessica and Teoh, Jeremy and Marcq, Gautier and Szostek, Alexandra and Bondad, Jasper and Sountoulides, Petros and Kelsey, Tom and Kasivisvanathan, Veeru},
  journal = {European Urology Focus},
  volume = {10},
  number = {6},
  pages = {1034--1042},
  year = {2024},
  doi = {10.1016/j.euf.2024.06.004},
}

JCO GO

Machine Learning to Predict Interim Response in Paediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests

Jennifer A. Geel, Artsiom Hramyka, Jan Plessis, Yasmin Goga, Anel Zyl, Marc G. Hendricks, Elelwani Madzhia, Vhutshilo Netshituni, and 13 more authors

JCO Global Oncology, 2024

DOI Bib HTML

@article{geel2024_hodgkin_blood_tests,
  title = {Machine Learning to Predict Interim Response in Paediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests},
  author = {Geel, Jennifer A. and Hramyka, Artsiom and du Plessis, Jan and Goga, Yasmin and van Zyl, Anel and Hendricks, Marc G. and Madzhia, Elelwani and Netshituni, Vhutshilo and Naidoo, Thanushree and Du Plessis, Liezl and Mathew, Rema and Louw, Lizette and Carr, Amy and Neethling, Beverley and Schickerling, Tanya and Omar, Fareed and Eyal, Katherine and Ngcana, Thandeka V. Z. and Kelsey, Tom and Ballot, Daynia E. and Metzger, Monika L.},
  journal = {JCO Global Oncology},
  volume = {10},
  pages = {e2300435},
  year = {2024},
  doi = {10.1200/GO.23.00435},
}