In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Bayesian neural networks (BNN) and deep ensembles are principled approaches to
estimate the predictive uncertainty of a deep learning model. However their practicality in …

Deep neural networks are revolutionizing the way complex systems are designed.
Consequently, there is a pressing need for tools and techniques for network analysis and …

Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many
applications ranging from robustness certification of classifiers to stability analysis of closed …

Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …

Concolic testing combines program execution and symbolic analysis to explore the
execution paths of a software program. In this paper, we develop the first concolic testing …

High sensitivity of neural networks against malicious perturbations on inputs causes security
concerns. To take a steady step towards robust classifiers, we aim to create neural network …

The growing range of applications of Machine Learning (ML) in a multitude of settings
motivates the ability of computing small explanations for predictions made. Small …