Krzysztof J. Geras

Assistant Professor
Department of Radiology
NYU Grossman School of Medicine

(also affiliated at)
Center for Data Science, New York University
Department of Computer Science, Courant Institute of Mathematical Sciences, New York University

Contact

Email: k.j.geras@nyu.edu.
Address (at Radiology): Office 728, 227 E 30th Street, New York, NY 10016.
Address (at CDS): Office 643, 60 5th Avenue, New York, NY 10011.

Frequently Asked Questions

About me

I am an assistant professor at the NYU Grossman School of Medicine. I previously worked as a postdoctoral researcher at the NYU Center for Data Science with Kyunghyun Cho.

My main interests are unsupervised learning with neural networks, model compression, transfer learning and evaluation of machine learning models.

During my PhD studies at the University of Edinburgh I was supervised by Charles Sutton. My PhD was kindly sponsored by SICSA and EPSRC. Prior to studying towards a PhD in Machine Learning, I got my BSc and MSc degrees in Computer Science from the University of Warsaw. I worked on my MSc thesis as a visiting student at the University of Edinburgh under the supervision of Amos Storkey, my second supervisor was Andrzej Szałas. My thesis was on Machine Learning Markets. I also did industrial internships in Microsoft Research (Redmond, working with Rich Caruana and Abdel-rahman Mohamed), Amazon (Berlin, Ralf Herbrich's group), Microsoft (Bellevue) and J.P. Morgan (London).

Research group

Jungkyu Park (PhD student at NYU Grossman School of Medicine)
Taro Makino (PhD student at CDS, co-supervised with Kyunghyun Cho)
Yanqi Zhu (PhD student at CDS)
Jakub Chłędowski (visiting PhD student from Jagiellonian University)
Amelia Dai (MS student at CDS)
Pranav Singh (Research engineer at NYU Grossman School of Medicine, co-supervised with Miriam Bredella)

Papers

Google Scholar profile

Code

Meta-repository of screening mammography classifiers

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

Differences between human and machine perception in medical diagnosis

Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Classifier-agnostic saliency map extraction

Breast density classification with deep convolutional neural networks

High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks