Anthony Simeonov
Research Scientist at Boston Dynamics
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAnthony Simeonov is a research scientist specializing in humanoid robotics and machine learning, currently working on the Atlas team at Boston Dynamics with eight years of hands-on experience bridging mechanical engineering and control systems. He holds a PhD in Computer Science from MIT and a BS in Mechanical Engineering from UCSD, combining rigorous academic research with practical lab and industry experience at NVIDIA, Disney Research, and power-plant controls at LADWP. Anthony contributes to educational open-source robotics content—having enhanced MIT manipulation course notebooks with new exercises and interactive fixes—demonstrating a knack for making complex robotics concepts accessible. He excels at integrating mechanics, embedded electronics, and software, and has a track record of rapidly acquiring applied skills across controls, signal processing, and data pipelines. Based in Cambridge, MA, he brings both experimental muscle-actuator research and real-world systems engineering to physically intelligent robot design.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Massachusetts Institute of TechnologyUniversity of California San Diego
English
Github Skills (6)
jupyter-notebook10
robotics10
python10
drake10
numpy9
testing8
Programming languages (3)
Github contributions (5)
RussTedrake/manipulation
Sep 2022 - Nov 2022
Course notes for MIT manipulation class
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:3 reviews, 13 commits, 15 PRs in 1 month
Contributions summary:Anthony contributed to the MIT manipulation course notes repository by implementing and testing robotic manipulation exercises. They added new exercises, including direct joint control and workspace decomposition, and updated existing notebooks related to topics such as differential inverse kinematics and robot painting. The user also fixed bugs, such as an interactive mode bug in the joint control notebook, and updated deepnote links. Their work demonstrates a focus on creating and improving interactive educational content for robotics.
manipulationnotespybulletmitcourse-notes
mcubelab/mpalm_affordances
Oct 2019 - Apr 2021
Contributions:12 PRs, 261 pushes, 15 branches in 1 year 6 months
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