Our research focuses on core foundational challenges integrating mathematical tools with real-world objectives to advance the state-of-the-art. We pursue ambitious use-inspired research, targeting frontier perceptual tasks in video, imaging and navigation.
We create fast, provably efficient tools for training neural networks and searching parameter spaces. We develop new theories to rigorously explain successful heuristics.
Since datasets are constantly evolving, we research new algorithms and models that can incorporate context and changes at training and test time, including robustness to perturbations.
What characteristics of a dataset help with training and inference? We define and uncover rich mathematical structures in datasets to improve downstream modeling and optimization.
We develop principled methods for automatically satisfying complex constraints and handling interactive feedback from users in real-world situations as is needed for safe robot navigation.
Use Inspired Applications
The foundational research thrusts of IFML all have broad potential impact and feed directly into real-world applications. We selected three use-inspired research areas: video, imaging, and navigation. We work with industrial partners to redesign the whole video pipeline from recognition, compression/decompression to training and model design; we collaborate to improve the imaging pipeline and create novel priors for MRI and circuit quality control; and we develop new methods for autonomous navigation in highly unstructured environments while maintaining safe operation with high confidence.
Scalable Multiagent Driving Policies For Reducing Traffic Congestion.
Jiaxun Cui, William Macke, Harel Yedidsion, Aastha Goyal, Daniel Urieli, and Peter Stone
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar
Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle
Yu-Sian Jiang, Garrett Warnell, and Peter Stone