Alican Bozkurt

AI Scientist

Professional Summary

I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.

Education

PhD in Machine Learning

2020

Northeastern University

MSc in Image Processing

2013

Bilkent University

BSc in Electrical and Electronics Engineering

2011

Bilkent University

Interests

Probabilistic Programming Deep Learning Biomedical Image Processing

Featured Publications

Computational Pathology

A Foundation Model for Clinical-Grade Computational Pathology and Rare Cancers Detection

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends …

eugene-vorontsov

• Jul 22, 2024 • 1 min read

Computational Pathology

Virchow: A Million-Slide Digital Pathology Foundation Model

The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the …

eugene-vorontsov

• Sep 14, 2023 • 1 min read

Rate-Regularization and Generalization in VAEs featured image

Rate-Regularization and Generalization in VAEs

Variational autoencoders (VAEs) optimize an objective that comprises a reconstruction loss (the distortion) and a KL term (the rate). The rate is an upper bound on the mutual …

Alican Bozkurt

• Jan 11, 2021 • 1 min read

Segmentation of Cellular Patterns in Confocal Images of Melanocytic Lesions in vivo via a Multiscale Encoder-Decoder Network (MED-Net) featured image

Segmentation of Cellular Patterns in Confocal Images of Melanocytic Lesions in vivo via a Multiscale Encoder-Decoder Network (MED-Net)

In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce …

kivanc-kose

• Jan 1, 2020 • 1 min read

Recent Publications

Eugene Vorontsov

Equal contribution

, Alican Bozkurt

Equal contribution

Equal contribution

, George Shaikovski

Equal contribution

, Michal Zelechowski

Equal contribution

, Kristen Severson

Equal contribution

, Eric Zimmermann, James Hall, Neil Tenenholtz, Nicolo Fusi, Ellen Yang, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan H. Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew Hanna, Hannah Wen, Juan A. Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David S. Klimstra, Brandon Rothrock, Siqi Liu, Thomas J. Fuchs. A Foundation Model for Clinical-Grade Computational Pathology and Rare Cancers Detection. In Nat Med, 2024.

Juan A. Retamero, Ege Gulturk, Alican Bozkurt, Siqi Liu, Mirela Gorgan, Lorena Moral, Mark Horton, Matthew Hanna, Philippe Mathieu, Brandon Rothrock, Thomas J. Fuchs. Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases. In Am J Surg Pathol, 2024.

Juan Retamero, Ege Gulturk, Jocelyn Sue, Alican Bozkurt, Julian Viret, Kevin Malfroid, Brandon Rothrock, Siqi Liu, Matthew C. H. Lee, Mirela Gorgan, Thomas J. Fuchs. A Deep Learning Artificial Intelligence Algorithm Helps Pathologists Improve Diagnostic Accuracy and Efficiency in the Detection of Lymph Node Metastases in Breast Cancer Patients. In Lab Invest, 2023.

See all publications

Contact

Email: alican@ece.neu.edu

Location: Paige AI, New York, NY