Khaled Saab

I am currently a Research Scientist at Google Research. I received my PhD and MS in Electrical Engineering from Stanford in 2023 and 2019, and BS in Computer Engineering from Georgia Tech in 2017. At Stanford, I was lucky to be advised by Daniel Rubin and Chris Ré.

My current research focuses on multimodal foundation models for healthcare.

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News

  • [November 2023] Study improving subgroup robustness for seizure onset detection from EEG is accepted at npj Digital Medicine.
  • [October 2023] I joined Google as a Research Scientist!
  • [September 2023] Submit my PhD dissertation at Stanford!

2023
Towards Trustworthy Seizure Onset Detection Using Workflow Notes [Code]
Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, and Daniel Rubin
npj Digital Medicine (Accepted).
A Case for Reframing Automated Medical Image Classification as Segmentation
Sarah Hooper, Mayee Chen, Khaled Saab, Kush Bhatia, Curt Langlotz, and Christopher Ré
Neural Information Processing Systems (NeurIPS), 2023.
Effectively Modeling Time Series with Simple Discrete State Spaces [Code]
Michael Zhang*, Khaled Saab*, Michael Poli, Tri Dao, Karan Goel, and Christopher Ré
International Conference on Learning Representations (ICLR), 2023.
Hungry Hungry Hippos: Towards Language Modeling with State Space Models [Blog] [Code]
Tri Dao*, Dan Fu*, Khaled Saab, Armin Thomas, Atri Rudra, and Christopher Ré
International Conference on Learning Representations (ICLR), 2023 (Spotlight).
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models [Code]
Siyi Tang, Jared Dunnmon, Liangqiong Qu, Khaled Saab, Tina Baykaner, Christopher Lee-Messer, and Daniel Rubin
Conference on Health, Infernece, and Learning (CHIL), 2023 (Best Paper).
2022
Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity [Code] [Video]
Khaled Saab, Sarah Hooper, Mayee Chen, Michael Zhang, Daniel Rubin, and Christopher Ré
Machine Learning for Healthcare (MLHC), 2022.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis [Code] [Video]
Siyi Tang, Jared Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, and Christopher Lee-Messer
International Conference on Learning Representations (ICLR), 2022.
Domino: Discovering Systematic Errors with Cross-Modal Embeddings [Code] [Blog] [Video]
Sabri Eyuboglu*, Maya Varma*, Khaled Saab*, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, and Christopher Ré
International Conference on Learning Representations (ICLR), 2022 (Oral).
ViLMedic: A Framework for Research at the Intersection of Vision and Language in Medical AI [Code]
Jean-Benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Alexander Dunnmon, Juan Manuel Zambrano, Akshay Chaudhari, and Curtis Langlotz
Association for Computational Linguistics (ACL) Demo Track, 2022.
A multivariate adaptive gradient algorithm with reduced tuning efforts
Samer Saab Jr, Khaled Saab, Shashi Phoha, Minghui Zhu, and Asok Ray
Neural Netowrks, 2022.
2021
Combining Recurrent, Convolutional, and Continuous-time Models with Structured Learned Linear State-Space Layers [Blog] [Code]
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré
Neural Information Processing Systems (NeurIPS), 2021.
Observational Supervision for Medical Image Classification Using Gaze Data [Code] [Video]
Khaled Saab, Sarah Hooper, Nimit Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared Dunnmon, Hongyang Zhang, Daniel Rubin, and Christopher Ré
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021 (Early Accept).
2020
Weak Supervision as an Efficient Approach for Automated Seizure Detection in Electroencephalography
Khaled Saab*, Jared Dunnmon*, Christopher Ré, Daniel Rubin, and Christopher Lee-Messer
npj Digital Medicine, 2020.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning [Code]
Jared Dunnmon, Alex Ratner, Khaled Saab, Nishit Khandwala, Matthew Markert, Hersh Sagreiya, Rodger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, and Christopher Ré
Cell Patterns, 2020.
2019
Doubly Weak Supervisioin of Deep Learning Models for Head CT
Khaled Saab, Jared Dunnmon, Roger Goldman, Alex Ratner, Hersh Sagreiya, Christopher Ré, and Daniel Rubin
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019 (Oral).
Improving Sample Complexity with Observational Supervision
Khaled Saab, Jared Dunnmon, Alex Ratner, Daniel Rubin, and Christopher Ré
ICLR Limited Labeled Data Workshop, 2019 (Spotlight).
Shuffled Linear Regression with Erroneous Observations
Samer Saab, Khaled Saab, and Samer Saab Jr.
IEEE Conference on Information Sciences and Systems, 2019.
2017
Protecting Bare-metal Embedded Systems with Privilege Overlays
Abraham Clements, Naif Almakhdhub, Khaled Saab, Prashast Srivastava, Jinkyu Koo, Saurabh Bagchi, and Mathias Payer
IEEE Symposium on Security and Privacy, 2017.
2016
A Stochastic Newton-Raphson Method with Noisy Function Measurements
Khaled Saab and Samer Saab Jr.
IEEE Signal Processing Letters, 2016.
Application of an Optimal Stochastic Newton-Raphson Technique to Triangulation-Based Localization Systems
Khaled Saab and Samer Saab Jr.
IEEE/ION Position, Location and Navigation Symposium, 2016.
Estimation of Cluster Centroids in Presence of Noisy Observations
Khaled Saab
IEEE MIT Undergraduate Research Technology Conference, 2016.
A Positioning System for Photodiode Device Using Collocated LEDs
Samer Saab Jr. and Khaled Saab
IEEE Photonics Journal, 2016.

( * Equal Contributors)