I am a scientist passionate about machine learning (ML) and its applications to impactful problems.
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
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]
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]
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons, NeurIPS Workshop on ML for Autonomous Driving (2022) [poster]
Rotation-equivariant Transformers for Behavior Prediction, Waymo Research (2021)
Hölder Bounds for Sensitivity Analysis in Causal Reasoning, ICML Workshop on Neglected Assumptions in Causal Inference (2021) [poster]
Quantization and Distillation for Biometric Recognition, Amazon Research (2020)
Counterfactual Representation Learning with Balancing Weights, AISTATS (2021) [poster]
Machine Learning of Continuous Wave Form Doppler Signals, Society for Clinical Vascular Surgery (2018) [link]
Introduction to PyTorch for machine learning (350+ attendees), Duke Winter Breakaway (2021) [link]
Introduction to PyTorch for deep learning, Duke +Data Science (2020) [link]
Crash course in do-calculus for causal inference (2019) [slides]
Image classification with Tensorflow, Duke Datathon (2018)
Keynote speech, Duke International Orientation (2018)
Zheng et al., "Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding" (2021) [slides]
Johansson et al., "Learning Weighted Representations for Generalization Across Designs" (2018) [slides]
Saito & Yasui, "Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models" (2020) [slides]
A. D'Amour, "On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives" (2019) [slides]
Louizos et al., "Causal Effect Inference with Deep Latent-Variable Models" (2017) [slides]
Johansson et al., "Learning Representations for Counterfactual Inference" (2016) [slides]
Shalit et al., "Estimating Individual Treatment Effect: generalization bounds and algorithms" (2017) [slides]
Kiros et al., "Skip-thought vectors" (2015) [slides]
Cer et al., "Universal Sentence Encoder" (2018) [slides]
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)