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Parth Paritosh

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PhD Candidate
Department of Mechanical and Aerospace Engineering
University of California, San Diego

E-mail: pparitos <at> ucsd <dot> edu
Office: Engineering Building Unit (EBU) I, Room 2111
Mailing Address:
Department of Mechanical and Aerospace Engineering
University of California, San Diego
9500 Gilman Drive, La Jolla, CA 92093-0411
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Brief Bio

I am currently pursuing PhD degree in Mechanical and Aerospace Engineering, with advisors Sonia Martinez and Nikolay Atanasov. Prior to this, I received my Master's degree from Purdue University ( 1, 2 ) , and Bachelor's degree from Indian Institute of Technology Guwahati. Some of my work with undergraduate students and interns at MURO lab is mentioned here.

Research

I am currently working on provably correct distributed estimation in networked systems, and applications to sensor networks. More recently, I am testing variational inference approach towards distributed estimation for general applications.

News

  • Sept 2023: Presented a poster on “Distributed Variational Inference for Online Supervised Learning” with a new Turtlebot4 team mapping implementation at SoCal Robotics Symposium 2023.

  • July 2023: Submitted our work on “Distributed Variational Inference for Online Supervised Learning” Read here.

  • Dec 2022: Presented our work on “Distributed Bayesian Estimation of Continuous Variables Over Time-Varying Directed Networks” at IEEE CDC 2022

  • Aug 2022: Our work titled “Distributed Bayesian Estimation of Continuous Variables Over Time-Varying Directed Networks” was accepted to IEEE Control Systems Letters

  • Mar 2022: Senate exam with a talk discussing “Scalable and Efficient Bayesian algorithms for Distributed Estimation and Inference”

Teaching

  • Teaching assistant for Cooperative Controls [MAE 247 syllabus], Spring 2023: Graded homework solutions and held office hours.

  • Additional teaching assistant MAE 242, Fall 2022: Discussion sessions and coding tutorials for motion planning.

  • Teaching assistant for Robot Motion Planning [MAE 242 syllabus], Spring 2022: Prepared homework solutions, created programming assignments, autograders and held office hours.

  • Teaching assistant for Introduction to Mathematical Analysis [MAE 289A syllabus], Fall 2021: Prepared homework solutions, and held office hours.