Jacob Springer


Machine Learning PhD student @ CMU


Hello! I am a PhD student in the Machine Learning department at Carnegie Mellon University where I am especially fortunate to be advised by Aditi Raghunathan.

I’m interested in understanding how and what neural networks learn, especially as you scale up. I have also spent a lot of time thinking about robustness in neural networks and how we can take insights from neuroscience to improve upon machine learning. Previously, I was an undergrad at Swarthmore College, and I have spent time as a research intern at Cold Spring Harbor Laboratory, MIT, and Los Alamos National Laboratory, where I worked with many lovely people. Please reach out if you want to chat about anything (I do love talking about research)!

Publications and Manuscripts

  1. If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness. Jones, Haydn T; Springer, Jacob M; Kenyon, Garrett T; Moore, Juston S. UAI 2022. (Oral)
  2. A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks. Springer, Jacob M; Mitchell, Melanie; Kenyon, Garrett T. NeurIPS 2021.
  3. Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers. Springer, Jacob M; Mitchell, Melanie; Kenyon, Garrett T. 2021.
  4. STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks for Models of Code. Springer, Jacob M; Reinstadler, Bryn Marie; O’Reilly, Una-May. 2020. 3rd Workshop on Adversarial Learning Methods for Machine Learning and Data Mining @ KDD. 2021.
  5. It’s Hard for Neural Networks To Learn the Game of Life. Springer, Jacob M; Kenyon, Garrett T. 2020. International Joint Conference on Neural Networks (IJCNN). 2021.
  6. Sparse MP4. Wang, Daniel A; Strauss, Charles MS; Springer, Jacob M; Thresher, Austin; Pritchard, Howard; Kenyon, Garrett T. IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). 2020.
  7. Classifiers based on deep sparse coding architectures are robust to deep learning transferable examples. Springer, Jacob M; Strauss, Charles S; Thresher, Austin M; Kim, Edward; Kenyon, Garrett T. 2018.
  8. Teaching with angr: A Symbolic Execution Curriculum and CTF. Springer, Jacob M; Feng, Wu-chang. USENIX Workshop on Advances in Security Education (ASE 18). 2018.