Alek Fröhlich

Ph.D. Student,

CSML, Italian Institute of Technology (IIT),

Genoa, Italy.

I am a-first year ELLIS Ph.D. student at the Italian Institute of Technology under the supervision of Massimiliano Pontil (IIT & UCL) and Karim Lounici (École Polytechnique).

Previously, I was a research fellow at the University of São Paulo Medical School working with Daniel Tiezzi.

I am broadly interested on bridging machine learning and medicine, where data is often scarce, models are commonly misspecified, and confounding factors abound. On the theoretical side, my interests lie in causality, uncertainty quantification, and developing rigorous guarantees for machine learning algorithms. On the applied side, I have been using machine learning and statistics to explore the vast amount of information contained in hematoxylin and eosin whole slide images and improving decision-making in clinical settings, particularly breast cancer care.

In my free time, I enjoy exploring mindfulness and thinking deeply about random things, especially while appreciating the beauty of nature or admiring architecture.

Publications

2025

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification

Alek Fröhlich, Thiago Ramos, Gustavo Cabello, and 3 more authors

In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2025

To appear

Bib HTML PDF

@inproceedings{Alek2025aaai,
  title = {PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification},
  author = {Fröhlich, Alek and Ramos, Thiago and Cabello, Gustavo and Buzatto, Isabela and Izbicki, Rafael and Tiezzi, Daniel},
  booktitle = {Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  note = {To appear},
  eprint = {2408.15458},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
}

2024

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features

Isabela Carlotti Buzatto, Sarah Abud Recife, Licerio Miguel, and 7 more authors

Breast Cancer Research and Treatment, Jul 2024

Bib HTML PDF

@article{Buzatto2024,
  title = {Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features},
  author = {Buzatto, Isabela Carlotti and Recife, Sarah Abud and Miguel, Licerio and Onari, Nilton and Faim, Ana Luiza Peloso and Bonini, Ruth Morais and Silvestre, Liliane and Carlotti, Danilo Panzeri and Fr\"{o}hlich, Alek and Tiezzi, Daniel Guimar{\~{a}}es},
  journal = {Breast Cancer Research and Treatment},
  year = {2024},
  month = jul,
  publisher = {Springer Science and Business Media LLC},
}

Molecular/genomic profile enhances prediction of response to target therapy in HER2-positive breast cancer

Daniel Tiezzi, Fabiana Buono, Alek Fröhlich, and 1 more author

In ESMO Open, Feb 2024

Bib HTML

@inproceedings{Buono2024,
  title = {Molecular/genomic profile enhances prediction of response to target therapy in HER2-positive breast cancer},
  author = {Tiezzi, Daniel and Buono, Fabiana and Fröhlich, Alek and Pagnotta, Stefano},
  booktitle = {ESMO Open},
  organization = {ESMO},
  year = {2024},
  month = feb,
}

2023

Computational pathology pipeline enables quantification of intratumor heterogeneity and tumor-infiltrating lymphocyte score

Daniel Tiezzi, Alek Fröhlich, Stefano Pagnotta, and 1 more author

In ESMO Immuno-Oncology Congress, Dec 2023

Bib HTML

@inproceedings{Alek2023,
  title = {Computational pathology pipeline enables quantification of intratumor heterogeneity and tumor-infiltrating lymphocyte score},
  author = {Tiezzi, Daniel and Fröhlich, Alek and Pagnotta, Stefano and Chahud, Fernando},
  booktitle = {ESMO Immuno-Oncology Congress},
  organization = {ESMO},
  year = {2023},
  month = dec,
}