About me

Hello! I am a PhD Candidate at the University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab, and pseudo-advised by Roberto Calandra at Facebook AI Research!

Hello! I am a PhD Candidate at the University of California, Berkeley studying the intersection of robotics and machine learning. I am a member of the Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab, and pseudo-advised by Roberto Calandra at Facebook AI Research! I am actively involved in outreach and inclusion efforts and an advocate for mental health -- he is the EEGSA wellness chair and founder of the UC Berkeley Equal Access to Application Assistance program.

Prior to UC Berkeley, I was a proud member of Cornell Electrical and Computer Engineering 2017 where I learned to do research with the Lab of Plasma Studies and the SonicMEMs Lab. I bring my research foundation in hardware, models, and physics to the data-driven world of machine learning. At Cornell, I was a part of Cornell Lightweight Rowing. I did an internship with Tesla Motors Battery Engineering in 2015.

I am happy to be a product of The Ocean State.

Nathan Lambert is  a PhD Candidate at the University of California, Berkeley working at the intersection of machine learning and robotics. He is a member of the Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab. Nathan has worked extensively with Roberto Calandra at Facebook AI Research and is joining DeepMind Robotics remotely for the summer of 2021. During his Ph.D., he was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism.

  • I am always looking to work with and promote under-represented groups in STEM fields. If you relate to this, please email me directly if you're curious in my work or trying to find a project in AI at UC Berkeley (I have mentored and advised multiple students through teaching at Berkeley).
  • Online mentor at Polygence (currently only taking scholarship-based students from under-represented groups).
  • Creator of Democratizing Automation newsletter & blog on making the future of AI and robotics equitable.

I like to try and have fun between my many projects. You can find me on Strava, I also happen to be a brand ambassador for Picky Bars. I actively track my health, cook, and read (recipe and book pages in construction).

News

  • Summer 2021: I will be interning with Martin Riedmiller's team at DeepMind.
  • April 2021 Update: Research Foundations.
  • 27 April 2021: I was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism. [link]
  • 25 April 2021: I had another paper on sociotechnics in AI published in IEEE Transactions on Technology and Society. [link]
  • 21 April 2021: We released an open-source library for model-based reinforcement learning. [link]

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I think about...

Robotics

Intelligent & novel devices to interact with the physical world.

Machine Learning

The science of using data to decide in the presence of uncertainty.

Society

Making sure the stakeholders of automation are in the conversation.

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Blogos

Debugging Deep Model-based Reinforcement Learning Systems

Things I have learned in 3 years of a young, and generally tricky research field.

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All grad students (should) study graphic design

People judge your papers by their cover. You can trick them into believing your science with pretty pictures.

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Exploitation Exploration (in MBRL)

A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric.

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More musings →

Papers

Axes for Sociotechnical Inquiry in AI Research

We present a concise set of directions for understanding the societal risks of new directions of AI research.

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

An open-source PyTorch repository designed from the bottom up for model-based reinforcement learning research.

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

Learn more.
We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.
More papers →