Sandeep Chinchali

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PhD Candidate,
Computer Science Department,
Stanford University
e-mail: csandeep [@] stanford [DOT] edu

About me

I am a Computer Science PhD student at Stanford University co-advised by Prof. Sachin Katti and Prof. Marco Pavone, where I work on cloud and networked robotics. My PhD thesis, titled Distributed Perception and Learning Between Robots and the Cloud, uses tools from deep learning, computer networks, and data-driven control.

Previously, I was the first principal data scientist at my adviser’s startup Uhana (acquired by VMware), where I built deep-learning-based models for cellular network optimization and control, some of which were successfully piloted in proof-of-concept trials by major network operators. Prior to Stanford and Uhana, I received a BS (Honors) in Electrical Engineering and a minor in Control and Dynamical Systems from Caltech. At Caltech, I was president of the Tau Beta Pi Engineering Honors Society and worked on formal verification for robotics with Professors Joel Burdick and Richard Murray.

News
  • I am on the academic job market.

  • Submitted a paper to ICRA 2020 on mining valuable training data to improve computer vision models from high-volume robot sensory streams. See our project website for pre-trained computer vision models and annotated videos.

  • Our paper on cloud robotics is a finalist for best student paper at Robotics: Science and Systems (RSS) 2019.

  • Co-organized a workshop on cloud robotics at Robotics: Science and Systems (RSS) 2019 with colleagues from UC Berkeley.

Selected Papers

A full list of publications and citations can be found here.

Research

My research applies deep reinforcement learning (RL) to a variety of scheduling and control problems for cellular networks, Internet-of-Things (IoT Devices), and robotics. My publications span both robotics/AI and systems venues:

Systems:

  • Scheduling Traffic from IoT Devices on Cellular Networks Using Deep RL (AAAI 2018)

  • Distributed Neural Network Inference Between Edge Devices and the Cloud (ACM HotNets 2018)

  • Decoupling Prediction and Control Across Data Boundaries

Robotics:

  • Formal Methods for Autonomous Car Safety Certification (ICRA 2016)

  • Towards Formal Methods for Dexterous Robotic Manipulation (ICRA 2012)

A full list of publications can be found here.

Invited Talks


I have given talks both in industry and academic conferences, including Google X, the ITU Workshop on “Machine Learning for 5G and beyond”, and Microsoft Research. Talk slides can be found here.

Selected Talks
  • October-December 2019: Robotics group talks at USC, Caltech, UW, UPenn, Columbia University, NYU-Poly

  • June 2019: Robotics: Science and Systems (RSS) 2019

  • November 2018: ACM HotNets 2018, Microsoft Research.

  • October 2018: Google X, Project Loon, Mountain View, California

  • August 2018: International Telecommunications Union (ITU) Workshop on “Machine Learning for 5G and beyond”, San Jose, California.

  • February 2018: Association for the Advancement of Artificial Intelligence (AAAI) 2018 Conference, New Orleans, Louisiana.

  • December 2017: International Symposium on Robotics Research (ISRR), Puerto Varas, Chile, 2017.

  • May 2016: IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016.

  • May 2012: IEEE International Conference on Robotics and Automation (ICRA), St. Paul, Minnesota, 2012.