June 2018 Our paper on generative modeling of human behavior was accepted to IROS 2018!
June 2018 New paper on backward reachable curricula for robotic reinforcement learning uploaded to arXiv, with code!

About Me

I obtained my Master's in Computer Science from Stanford University in 2018, specializing in Artificial Intelligence (AI).

Prior to that, I obtained my Bachelor of Applied Science with High Honours in 2016 from the University of Toronto's rigorous Engineering Science program, majoring in Electrical and Computer Engineering with a Robotics/Mechatronics minor.

Resume/CV (updated June 29, 2018)


  • Commencing September 2018
    Stanford University
    Aeronautics and Astronautics - Doctor of Philosophy (PhD)
    Stanford, CA - USA
    • Conducting research at the intersection of robotics and deep learning under Prof. Marco Pavone.
  • September 2016 - June 2018
    Stanford University
    Computer Science - Master of Science (MS)
    Stanford, CA - USA
    • Conducted research in machine learning, computer vision, robotics, and data science.
    • I have had the pleasure of working in the following labs:
  • September 2012 - June 2016
    University of Toronto
    Engineering Science - Bachelor of Applied Science (BASc) with High Honours
    Toronto, ON - Canada
  • September 2003 - June 2012
    Bayview Glen
    Ontario Secondary School Diploma (OSSD)
    Toronto, ON - Canada
    • Top of Grade (highest overall average) for Grades 11 and 12.
    • Graduated as an Ontario Scholar and Governor General’s Academic Medal holder.
    • Graduated as an Advanced Placement Scholar with Honour.


  • BaRC: Backward Reachable Curriculum for Robotic Reinforcement Learning
    B. Ivanovic
    , J. Harrison, A. Sharma, M. Chen, M. Pavone
    [BibTex] [PDF] [arXiv] [code] arXiv 2018
International Peer-Reviewed Conference Proceedings
  • Generative Modeling of Multimodal Multi-Human Behavior
    B. Ivanovic
    , E. Schmerling, K. Leung, M. Pavone
    [BibTex] [PDF] [arXiv] [code] IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems (IROS) 2018
  • ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
    J. Harrison, A. Garg,
    B. Ivanovic
    , Y. Zhu, S. Savarese, L. Fei-Fei, M. Pavone
    [BibTex] [PDF] [arXiv] Int'l Symp. on Robotics Research (ISRR) 2017
Blog Posts

Work and Research Experience

  • June 2017 - September 2017
    Prime Air SDE Intern
    Amazon.com - Seattle, WA - USA
    • Worked with Principal Research Scientist Ishay Kamon in the Autonomy team.
    • Designed and implemented a novel state-of-the-art deep learning approach for a specific computer vision task within the team, outperforming existing models by 10x.
    • The project was completed successfully and a full-time Research Scientist return offer was extended.
  • April 2017 - June 2017
    CS231A Course Assistant
    Stanford University - Stanford, CA - USA
  • January 2017 - June 2017
    Independent Research Project
    Stanford University - Stanford, CA - USA
    • Worked in the Computer Vision and Geometry Lab (CVGL) and Autonomous Systems Lab (ASL) with Professors Silvio Savarese and Marco Pavone on making reinforcement learning more robust with control theory.
    • Tackled the problem of policy transfer in reinforcement learning, applying model-predictive control to provide safety guarantees when transferring a learned policy from one environment to another with different dynamics.
    • Our work was accepted to the International Symposium on Robotics Research (ISRR) 2017, held in Puerto Varas, Chile.
  • September 2016 - June 2017
    Research Assistant
    Stanford University - Stanford, CA - USA
    • Worked in the Stanford Network Analysis Project (SNAP) Lab with Professor Jure Leskovec on analyzing large-scale physical activity data with modern data science methods.
    • Efficiently cleaned, preprocessed, and distilled 3 TB of user physical activity data from over 2 million users of a mobile fitness app. Obtained scientific results with data visualization, statistical analyses (such as regressions and confidence metrics), and computational methods (including hierarchical bootstrapping).
    • Showed significant results relating a location’s walkability, weather, and climate to an individual’s physical activity. This work has very wide implications, as physical inactivity is a major global pandemic responsible for over 5 million deaths per year.
  • September 2016 - December 2016
    Independent Research Project
    Stanford University - Stanford, CA - USA
    • Worked with Professor Andrew Ng on neural language correction, a method of correcting grammar and spelling mistakes using deep sequence models.
    • Augmented state-of-the-art deep neural language models with subword units and experimentally verified resulting model performance.
  • May 2016 - August 2016
    Prime Air SDE Intern
    Amazon.com - Seattle, WA - USA
    • Worked with former NASA Astronaut Neil Woodward in the Flight Test team.
    • Designed and built fault-tolerant, scalable software and hardware to autonomously collect and process relevant flight test data from numerous locations for internal consumption.
    • The project was completed successfully and a return offer was extended.
  • January 2016 - May 2016
    CSC411 Teaching Assistant
    University of Toronto - Toronto, ON - Canada
  • September 2015 - May 2016
    Undergraduate Thesis
    University of Toronto - Toronto, ON - Canada

    Worked with Professors Raquel Urtasun and Sanja Fidler to:

    Create a semantically labelled 3D map of an outdoor environment from video recorded by a quadrocopter and augment that map with cartographic data (ie. from OpenStreetMap).

    More specifically, the goals of the project were:

    • Fly a quadcopter around an outdoor environment and, from video recorded during the flight, create a 3D reconstruction of the environment. The LSD-SLAM algorithm was used for this since the cameras available to us were monocular.
    • Design and implement a data annotation framework to support and automate the labeling of collected data.
    • Label objects within the 3D representation of the environment, such as people, trees, buildings, etc... For this I formulated a CRF, incorporating cartographic data.

    Project results and reports can be viewed at this Google Drive link.

  • May 2015 - August 2015
    Summer Research Intern
    ETH Zurich - Zurich - Switzerland
    • Worked with Professor Raffaello D’Andrea in the Institute for Dynamic Systems and Control, specifically the Flying Machine Arena.
    • Removed superfluous code from an open source motor controller and implemented new features such as motor calibration, emergency safety states, and a better motor startup routine in C.
    • Simulated dynamic motor and propeller system responses in Python.
    • Technology used: STM32 C Code, Motor Controller PCB Chips, Quadrotor Flying Vehicles.
  • May 2014 - July 2014
    SDE Intern
    Amazon.com - Seattle, WA - USA
    • Worked in the Demand Forecasting team creating a real-time simulation tool. The project was completed successfully and a return offer was extended.
    • Worked with Big Data, using the Hadoop framework (MapReduce, HDFS, etc.) to process large amounts of simulation data generated by a machine learning module.
    • Created a web service with Spring, designed and implemented a website UI with GWT, and used the AWS SDK to store and retrieve data from S3.
    • Gave a presentation to 100+ Amazon employees regarding my project and its design, implementation, and performance.
    • Concepts employed: Big Data, Highly Scalable Distributed Systems, and Data Mining.
  • May 2013 - August 2013
    Agile Engineer Co-op
    Xtreme Labs (now Pivotal) - Toronto, ON - Canada
    • Learned Agile development methodologies and applied them to real-world projects.
    • Provided input on and developed an app that has 1,000,000+ installs.
    • Technology used: Android SDK for apps and HTML/CSS3/PHP for website development.


  • May 2016
    Engineering Science Award of Excellence
    University of Toronto - Toronto, ON - Canada

    Received for maintaining a CGPA greater than 3.90.

  • May 2016
    Computer Science TA Award
    University of Toronto - Toronto, ON - Canada

    Received for being the best Computer Science TA in the Winter 2016 semester.

  • April 2016
    NSERC Master's Postgraduate Scholarship (CGS-M) (Declined)
    National Sciences and Engineering Research Council (NSERC) - Canada

    The CGS-M Program provides financial support to high-calibre scholars who are engaged in eligible master’s programs in Canada (more information here).

  • September 2012 - June 2016
    Dean’s Honour List
    University of Toronto - Toronto, ON - Canada

    Placed on the Dean’s Honour List for all undergraduate semesters.

  • September 2012
    University of Toronto Scholarship
    University of Toronto - Toronto, ON - Canada

    Received for being one of the top 300 entrants to the University of Toronto in 2012.