Category: Paper

We developed a new safety prediction pipeline that leverages a vector of anomaly scores to predict the system’s safety confidence. Somehow, it manages to generalize to unseen anomalies in sensing and dynamics. Citation:

As a starting point towards using bandit-style algorithms for autonomy, we have developed finite-sample bounds for efficient hypothesis testing while eliminating unlikely hypotheses. Citation:

We have released our shiny new TEACar platform for autonomous racing, inspired by DonkeyCar but with upgraded mechanical, hardware, and software components! Citation:

Ivan took part in a large many-university effort to summarize the state of resilient cyber-physical systems (CPS) and the outlook for future research in this area. Five themes have emerged. Citation:

In collaboration with ECE colleagues, we have put out a new variant of our unsupervised anomaly detection pipeline. This one uses a latent entropy loss to scramble the latent space, making anomalies harder to reconstruct (and hence easier to detect). No supervision (including a normal-only dataset) needed! Citation:

Our exploration of world models for system assurance resulted in a semi-predictable but currently unfashionable choice: removing randomness and uncertainty from the latent space made world models more verifiable (although a tiny bit less picture-perfect). More surprisingly, this step made the behaviors produced by them more relevant to the real world. As a result, we…

Our collaboration with RPI has yielded an extended and improved version of our NeuS’25 paper: combining conformal prediction for neural perception with reachability analysis for the dynamics and control. This problem required constructing a discrete abstraction of the perception neural net, which we did with a genetic algorithm. Citation:

We’ve put in a big effort to find, categorize, and analyze assumptions and guarantees in papers on cyber-physical systems since 2014. Now we’re happy to release the results! Citation: We are also sharing our database of analyzed papers and assumptions.