Serge Assaad

I'm currently a research scientist at Waymo, working on the Planner ML team. 

Before joining Waymo, I was a Ph.D. student studying deep learning at Duke University.

Through industry experience at Waymo & Amazon, I have designed scalable, fast, accurate, and robust ML models for autonomous driving, trajectory prediction, and biometric recognition. Through my Ph.D. research, I have experience in ML for causal inference and ML for digital pathology

Before my Ph.D. , I completed a B.S. in Biomedical Engineering and Electrical Engineering (Mathematics Minor) at Duke.

Email: sergea [at] google [dot] com


Professional Experience

Research Scientist, Waymo (Feb 2023 - Present)

Research Intern,  Waymo (September - December 2021)

Applied Scientist Intern,  Amazon (June - September 2021)

Applied Scientist Intern,  Amazon (May - August 2020)


S. Assaad, Ph.D. Dissertation: "Principled Deep Learning for Healthcare Applications", 2023 [dissertation][slides]

S. Assaad, D. Dov, R. Davis, S. Kovalsky, W.T. Lee, R. Kahmke, D. Rocke, J. Cohen, R. Henao, L. Carin, D.E. Range, "Thyroid cytopathology cancer diagnosis from smartphone images using machine learning", Modern Pathology, 2023 [paper]

S. Assaad, C. Downey, R. Al-Rfou, N. Nayakanti, B. Sapp, "VN-Transformer: Rotation-Equivariant Attention for Vector Neurons", Transactions on Machine Learning Research (TMLR), 2023 [paper][poster][talk]

(Previous version appeared at ML4AD Workshop @ NeurIPS, 2022)

S. Assaad, S. Zeng, H. Pfister, F. Li, L. Carin, "Hölder Bounds for Sensitivity Analysis in Causal Reasoning" ,  Workshop on the Neglected Assumptions in Causal Inference, International Conference on Machine Learning (ICML), 2021 [paper][poster]

D. Dov, S. Assaad, A. Syedibrahim, J. Bell, J. Huang, J. Madden, R. Bentley, S. McCall, R. Henao,  L. Carin, W. Foo, "A hybrid human-machine learning approach to screening prostate biopsies can improve clinical efficiency without compromising diagnostic accuracy",  Archives of Pathology & Laboratory Medicine, 2021 [paper]

D. Dov, S.Z. Kovalsky, Q. Feng, S. Assaad, J. Cohen, J. Bell, R. Henao, L. Carin, D.E. Range, ``Use of Machine-Learning-Based Software for the Screening of Thyroid Cytopathology Whole Slide Images'',  Archives of Pathology & Laboratory Medicine, 2021 [paper]

S. Assaad, S. Zeng, C. Tao, S. Datta, N. Mehta,  R. Henao, F. Li, L. Carin, "Counterfactual Representation Learning with Balancing Weights" ,  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 [paper][poster][talk]

P. Chapfuwa, S. Assaad, S. Zeng, M. Pencina, L. Carin. R. Henao, "Enabling Counterfactual Survival Analysis with Balanced Representations",  ACM Conference on Health, Inference, and Learning (ACM-CHIL), 2021 [paper][talk]

D. Dov, S. Assaad, S. Si, R. Wang, H. Xu, S. Kovalsky, J. Bell, D. Range, J. Cohen, R. Henao, L. Carin,  "Affinitention Nets: Kernel Perspective on Attention Architectures for Set Classification with Applications to Medical Text and Images",  ACM Conference on Health, Inference, and Learning (ACM-CHIL), 2021 [paper][talk]

D. Dov, S.Z. Kovalsky, S. Assaad, J. Cohen, D.E. Range, A.A. Pendse, R. Henao, L. Carin, "Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images",  Medical Image Analysis, 2020 [paper]

S. Zeng, S. Assaad, C. Tao, S. Datta, L. Carin, F. Li, "Double Robust Representation Learning for Counterfactual Prediction" (preprint) [arXiv]

Professional Services

Reviewer for NeurIPS (2022, Top 10% Reviewer), ICML (2023, 2022)AISTATS (2021)Neural Networks (2021) 


Research talks



Graduate TA Performance & Technology (2021), Vector Space Methods (2020), Data Science Seminar (2019)

Undergraduate TA Calculus (I, II, III) (2015), Linear Algebra (2015), Probability (2015), Data & Decision Sciences (2018), Introduction to Circuits (2017)

Tutor Boeing Grand Challenges Fellow (2018), NCAA Student-Athlete Tutor (2016)