I am a research scientist at self-driving car company nuTonomy working on the Behavior Modeling team. I work on modeling the behavior of the agents of interest present in the self-driving car's environment (e.g., vehicles, bicyclists, pedestrians, etc.). I create and build models of how agents behave on the road, focusing on applying machine learning techniques (including deep learning) to predict the trajectories and intentions of such agents.

Previously, I completed my Ph.D. in robotics at the Social Robotics Lab within the Department of Computer Science at Yale University. My advisor was Brian Scassellati.

During my Ph.D., I was interested in machine learning techniques applied to robotics, including hidden Markov models (HMMs) and reinforcement learning. I explored these in the context of human-robot collaboration, with the aim of developing robots that provide supportive behaviors to a person during a physical task. In this scenario, I regarded the human as an agent in the system, and the robot as a learner that needs to adapt to both the task and the human agent.

During the summer of 2017, I had a great time interning at Uber Advanced Technologies Group, in San Francisco. I worked on integrating temporal context into deep learning models for self-driving car perception.

Prior to my time at Yale, I had completed a Master of Engineering from the University of Bristol. Take a look at my CV for more details.

Elena Corina Grigore
Call me Corina!


Uber ATG

Uber ATG

Yale University

Yale University

Yale School of Engineering & Applied Science

Yale Engineering

University of Bristol

University of Bristol

University of California, San Diego

University of California, San Diego

Research Interests

My passion in research is to apply machine learning techniques to the field of robotics in order to create autonomous robots / vehicles capable of learning useful behaviors in dynamic environments. Whether applied to physical interactions between robots and people in human-robot collaboration scenarios, or to self-driving cars in complex environments where the autonomous vehicle needs to interact with human drivers on the road, we need to come up with dependable ways of applying learning techniques that make it possible for the robot to learn from both the people it interacts with and its environment.

Recently, I have been focusing on modeling the behavior of the agents that an autonomous vehicle interacts with on the road. Specifically, I have been looking at applying deep learning (including recurrent neural networks) to datasets involving temporal data about the agents present in a self-driving car's environment. Having high-quality predictions of intentions and future trajectories for these agents is crucial if we are to enable robust functioning of self-driving cars.


Here is my Ph.D. thesis: Learning Supportive Behaviors for Adaptive Robots in Human-Robot Collaboration.

Here is a list of my publications, in reverse chronological order (16 - 1):


As a graduate student at Yale, I have assisted in teaching the following courses:

Period Course Code Course Title Instructor Capacity
Spring 2017 CPSC 477/577 Natural Language Processing Dragomir Radev Teaching Fellow
Fall 2015 CPSC 202 Mathematical Tools for Computer Science Dana Angluin Teaching Fellow
Spring 2015 CPSC 472/572 Intelligent Robotics Brian Scassellati Teaching Fellow
Fall 2014 CPSC 202 Mathematical Tools for Computer Science Dana Angluin Teaching Fellow
Spring 2014 CPSC 473/573 Intelligent Robotics Lab Brian Scassellati Teaching Fellow
Fall 2013 CPSC 472/572 Intelligent Robotics Lab Brian Scassellati Teaching Fellow

Other teaching and mentoring:


Throughout my time at Yale, I have been involved with several outreach activities:


Programming Languages Software / IDEs Robotics Platforms


Contact Me

I am always happy to talk about research! Contact me at:

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