Samuel Schmidgall

Samuel Schmidgall

PhD Electrical and Computer Engineering @ Johns Hopkins University | NSF GRFP Fellowship

Hello there, and welcome to my website! My name is Samuel Schmidgall and I’m a researcher and engineer focused on applying AI to the field of medicine and medical robotics.

I am currently a 1st year PhD student @ Johns Hopkins University in Electrical and Computer Engineering. I’m jointly advised by Rama Chellappa and Axel Krieger in the Artificial Intelligence for Engineering and Medicine Lab (AIEM) and the Intelligent Medical Robotic Systems and Equipment Lab (IMERSE) toward building autonomous surgical robots and medical language models. I’m very grateful to have received support from the NSF Graduate Research fellowship.

I’m currently doing an internship (April - July) at Stanford University School of Medicine with Hiesinger Lab. If you’re in the bay area and want to talk about anything research related let me know!

Prior to starting my PhD, I was a Research Scientist in the Space Robotics Department at the US NRL. My past research focused on machine learning approaches for robotic learning with legged and articulated robotic systems. I was also working toward studying intelligence in brain organoids in the Center for Alternatives in Animal Testing (CAAT) at the Johns Hopkins University School of Public Health. I was also a research affiliate at the University of California, Santa Cruz Neuromorphic Computing Laboratory working on reinforcement learning for surgical robotics. I recieved my B.S. in Computer Science at George Mason University in 2021. During my time as an undergraduate, I worked on a variety of projects in mathematics, robotics, computational neuroscience, and artificial intelligence. These topics include: multi-agent motion planning, wave propagation dynamics, meta-, and continual learning via synaptic plasticity. For more information see my CV.

Outside of research, I like to travel, play tennis, write, and most of all spend time with my wonderful wife ❤️

Awards

  • National Science Foundation Graduate Research Fellowship (NSF GRFP), ~$147,000 September 2023-May 2026
  • 2022 Alan Berman Research Publication Award for work “SpikePropamine: Differentiable Plasticity in Spiking Neural Networks”
  • Best Poster Award MEGL Symposium Poster Presentation Spring 2020 – Outstanding Poster Award Joint Mathematics Meeting Conference Spring 2019

Publications

Journal

  • Schmidgall, S., Kim, JW., Krieger, A. (2024). Can robots imitate surgeon demonstrations? Nature Reviews Urology.

  • Schmidgall, S., Ziaei, R., Jascha, A., Louis, K., Hajiseyedrazi, T., Eshraghian, J., (2024). Brain-inspired learning in artificial neural networks. Applied Physics Letters Machine Learning.

  • Schmidgall, S., Hays, J. (2023). Meta-SpikePropamine: Learning to learn with synaptic plasticity in spiking neural networks. Frontiers in Neuroscience.

  • Holzer, M., Richey, Z., Rush, W., Schmidgall, S., (2022). Locked fronts in a discrete time discrete space population model. Journal of Mathematical Biology.

  • Schmidgall, S., Ashkanazy, J., Lawson, W., Hays, J, (2021). SpikePropamine: Differentiable Plasticity in Spiking Neural Networks. Frontiers in Neurorobotics

Conference

  • Schmidgall, S., Krieger, A., Eshraghian, J. (2024). Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots. International Conference on Robotics and Automation (ICRA 2024).

  • Ziaei, R., Schmidgall, S., Language models are susceptible to incorrect patient self-diagnosis in clinical applications (2023). Conference on Neural Information Processing Systems (NeurIPS 2023), Workshop on Deep Generative Models for Health.

  • Kim, JW., Schmidgall, S., Krieger, A., Kobilarov, M., (2023). Learning a Library of Surgical Manipulation Skills for Robotic Surgery. Conference on Robotic Learning (CoRL 2023), CRL WS Workshop

  • Schmidgall, S., Hays, J., (2023). SMA: A three-factor learning rule for synaptic motor adaptation in spiking neural networks. International Conference on Neuromorphic Systems (ICONS).

  • Schmidgall, S., Parsa, M., Schuman, C. (2023). Biological connectomes as a representation for the architecture of artificial neural networks. Proceedings of the 2023 AAAI Conference on Artificial Intelligence “Systems Neuroscience Approach to General Intelligence” Workshop.

  • Schmidgall, S., Hays, J., (2022). Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks. Neuro-Inspired Computational Elements (NICE).

  • Lukyanenko, A., Camphire, H., Austin, A., Schmidgall, S., Soudbakhsh, D., (2021). Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles. 5th Conference on Control Technology and Applications (CCTA).

  • Schmidgall, S. (2021). Self-constructing Neural Networks through Random Mutation. International Conference on Learning Representations (ICLR).

  • Schmidgall, S., (2020). Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks. The 2020 Genetic and Evolutionary Computation Conference (GECCO).