Vladislav Isenbaev

Responsible for conducting SoTA research across multiple directions, including

RL-based planning

• Worked closely with ML infra team to define and implement RL training infrastructure
• Developed core off-policy RL training algorithm used by RL planner
• Researched multiple approaches to integrate safety constraints into RL planning system

Generative approach to behavior prediction and Imitation Learning

• Did comprehensive analysis of generative modeling techniques as an alternative to regression-based Imitation Learning.
• Developed and implemented diffusion-based generative behavior prediction model.
• Generalized diffusion-based prediction model to provide interaction-aware joint prediction, showed superior performance on internal dataset for joint prediction.
• Developed novel diffusion distillation approach to reduce inference latency of diffusion-based predictor.

Anomaly detection for behavior models

• Implemented EBM-based anomaly detection model and showed superior performance compared to heuristic-based method used previously.

Responsible for deployment of Deep Learning models for robotic perception (including various computer vision and sensor fusion models)

Responsible for ML infra and backend for Lookalike Targeting product