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Tal Schuster

PhD Student

CSAIL, MIT

Biography

I'm a Ph.D. student at MIT CSAIL and a member of the NLP and Learn To Cure groups. I'm fortunate to be advised by Prof. Regina Barzilay.

Currently, I focus on developing ML and NLP methods to improve the robustness of models, prevent undesired outcomes, and increase their performance in challenging and realistic scenarios.
I have also worked on developing deep learning screening-based methods to improve cancer detection and risk assessment.

Before coming to MIT, I was an MSc student in the Computer Science Dep. at Tel-Aviv University, advised by Prof. Lior Wolf.

Interests

  • Machine Learning
  • Natural Language Processing
  • Computer Vision

Education

  • PhD in Computer Science, in progress

    Massachusetts Institute of Technology

  • MSc in Computer Science, 2017

    Tel Aviv University

  • BSc in Mathematics and Computer Science

    Ben-Gurion University

Projects

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Fake News

Misinformation detection

Medical

Deep learning for cancer risk prediction

Metric Learning

Deep metric learning

Word Embeddings

Dense representations of words

More Publications

Distilling the Evidence to Augment Fact Verification Models

In Workshop on Fact Extraction and VERification (FEVER workshop at ACL), 2020.

PDF Project

Automatic Fact-guided Sentence Modification

In AAAI Conference on Artificial Intelligence (AAAI), 2019.

PDF Code Project

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

In Radiological Society of North America, 2019.

PDF Code Project

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

In Radiological Society of North America, 2019.

PDF Code Project

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

In American Journal of Roentgenology (AJR), 2019.

PDF Code Project

Mammographic breast density assessment using deep learning: clinical implementation

In Radiological Society of North America, 2018.

PDF Code Project

Teaching

TA: 6.883 - Modelling with Machine Learning

Syllabus