Samuel Schmidgall

I'm a PhD student in at Johns Hopkins University and a researcher at Google DeepMind. I broadly work on autonomous agents (LLMs and robotics).

Publications

Agent Laboratory: Using LLM Agents as Research Assistants

Agent Laboratory: Using LLM Agents as Research Assistants

Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Zicheng Liu, Emad Barsoum

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Trainees’ perspectives and recommendations for catalyzing the next generation of NeuroAI researchers

Andrea I. Luppi, Jascha Achterberg, Samuel Schmidgall, I. Bilgin, P. Herholz, Maximilian Sprang, Benjamin Fockter, A. S. Ham, Sushrut Thorat, Rojin Ziaei, Filip Milisav, Alexandra M Proca, Hanna M. Tolle, Laura E Suárez, Paul Scotti, Helena M Gellersen

Nature Communications 2024

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Evaluation and mitigation of cognitive biases in medical language models

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter M Abadir, Rama Chellappa

npj Digit. Medicine 2024

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Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

P. Henrich, Jiawei Liu, J. Ge, Samuel Schmidgall, Lauren Shepard, A. Ghazi, Franziska Mathis-Ullrich, Axel Krieger

IEEE/RJS International Conference on Intelligent RObots and Systems 2024

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SurGen: Text-Guided Diffusion Model for Surgical Video Generation

SurGen: Text-Guided Diffusion Model for Surgical Video Generation

Joseph Cho, Samuel Schmidgall, Cyril Zakka, Mrudang Mathur, R. Shad, W. Hiesinger

arXiv.org 2024

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GP-VLS: A general-purpose vision language model for surgery

GP-VLS: A general-purpose vision language model for surgery

Samuel Schmidgall, Joseph Cho, Cyril Zakka, W. Hiesinger

arXiv.org 2024

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Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks

Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks

Ji Woong Kim, Tony Zhao, Samuel Schmidgall, A. Deguet, Marin Kobilarov, Chelsea Finn, Axel Krieger

arXiv.org 2024

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AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor

arXiv.org 2024

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Robots learning to imitate surgeons - challenges and possibilities.

Samuel Schmidgall, Ji Woong Kim, Axel Krieger

Nature reviews. Urology 2024

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General surgery vision transformer: A video pre-trained foundation model for general surgery

General surgery vision transformer: A video pre-trained foundation model for general surgery

Samuel Schmidgall, Ji Woong Kim, Jeffery Jopling, Axel Krieger

arXiv.org 2024

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Addressing cognitive bias in medical language models

Addressing cognitive bias in medical language models

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter M Abadir, Rama Chellappa

arXiv.org 2024

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General-purpose foundation models for increased autonomy in robot-assisted surgery

General-purpose foundation models for increased autonomy in robot-assisted surgery

Samuel Schmidgall, Ji Woong Kim, Alan Kuntz, A. Ghazi, Axel Krieger

Nature Machine Intelligence 2024

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Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

Samuel Schmidgall, Axel Krieger, Jason Eshraghian

IEEE International Conference on Robotics and Automation 2023

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Language models are susceptible to incorrect patient self-diagnosis in medical applications

Language models are susceptible to incorrect patient self-diagnosis in medical applications

Rojin Ziaei, Samuel Schmidgall

arXiv.org 2023

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Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks

Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks

Samuel Schmidgall, Joe Hays

International Conference on Systems 2023

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Brain-inspired learning in artificial neural networks: a review

Brain-inspired learning in artificial neural networks: a review

Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. P. Hajiseyedrazi, Jason Eshraghian

APL Machine Learning 2023

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Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

Samuel Schmidgall, Joe Hays

Frontiers in Neuroscience 2023

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Biological connectomes as a representation for the Architecture of Artificial Neural Networks

Samuel Schmidgall, Catherine D. Schuman, Maryam Parsa

bioRxiv 2022

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Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

Samuel Schmidgall, Joe Hays

bioRxiv 2022

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Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks

Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks

Samuel Schmidgall, Joe Hays

Neuro Inspired Computational Elements Workshop 2021

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Self-Replicating Neural Programs

Samuel Schmidgall

arXiv.org 2021

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Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

Samuel Schmidgall, Joe Hays

arXiv.org 2021

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Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles

Anton Lukyanenko, Heath Camphire, Avery Austin, Samuel Schmidgall, D. Soudbakhsh

Conference on Control Technology and Applications 2021

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SpikePropamine: Differentiable Plasticity in Spiking Neural Networks

SpikePropamine: Differentiable Plasticity in Spiking Neural Networks

Samuel Schmidgall, Julia Ashkanazy, W. Lawson, Joe Hays

Frontiers in Neurorobotics 2021

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Self-Constructing Neural Networks Through Random Mutation

Self-Constructing Neural Networks Through Random Mutation

Samuel Schmidgall

arXiv.org 2021

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Locked fronts in a discrete time discrete space population model

Matt Holzer, Zachary Richey, Wyatt Rush, Samuel Schmidgall

Journal of Mathematical Biology 2020

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Adaptive reinforcement learning through evolving self-modifying neural networks

Adaptive reinforcement learning through evolving self-modifying neural networks

Samuel Schmidgall

GECCO Companion 2020

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