ABSTRACT: Autonomous systems are rapidly moving from research labs into the real world, powering drones, self-driving cars, and service robots. Yet, their widespread adoption hinges not only on performance, but on assurance—the ability to guarantee that robots do what they are intended to do, safely and reliably, even under…

ABSTRACT: The revolutionary capabilities of AI with machine learning have enabled an increasingly broad range of applications, which has brought many new challenges in ensuring the trustworthiness of AI applications. In this talk, I will present our research on trustworthy AI with verifiable guarantees. I will first introduce our…

ABSTRACT: Recent advancements in machine learning, while powerful, are often burdened by significant computational and memory requirements, limiting their deployment in resource-constrained settings. Hyperdimensional Computing (HDC) emerges as an alternative with its simplicity, lightweight operations, and robustness to errors in…

ABSTRACT: In an era of rapid AI progress, leveraging accelerated computing and big data has unlocked new possibilities to develop generalist AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create…

ABSTRACT: In this talk, I will overview my decadelong journey into understanding the implications of online platform manipulation. I'll start from detecting malicious bots and other forms of manipulation including troll accounts, coordinated campaigns, and disinformation operations. The impact of my work will be corroborated with…

ABSTRACT: Time-critical missions are among the most important classes of tasks yet to fully benefit from agile autonomous micro aerial vehicles. At the same time, such missions require developing algorithms that enable these robots to navigate safely, and make effective decisions on resource-constrained hardware. In this talk I…

ABSTRACT: In this lecture first we will discuss the current trends and challenges in Artificial Intelligence (AI) and Machine Learning (ML). In addition to the fundamentals of ML, we will demonstrate the importance of using Deep Learning (DL), Graph neural network (GNN) and explainable AI. While DL has been used very successfully…

ABSTRACT: Molecular dynamics (MD) simulations are widely used to study the mechanisms of biological processes at an atomistic resolution. Most physiological events, e.g., drug-target binding and protein folding, occur at timescales beyond milliseconds. But, we can simulate only up to a few microseconds at an affordable…

ABSTRACT:  Implicit neural representations (INRs) are a powerful family of continuous learned function approximators for signal data that are implemented using multilayer perceptron (MLP). INRs enable a non-linear signal representation of bases functions that are generated by scaling and shifting the activation function.…

ABSTRACT: In many real-world applications such as intelligent transportation, biosurveillance, climate science, and bioinformatics, statistical models and machine learning algorithms are applied to large-scale spatio-temporal data. Such data are typically high-dimensional and demonstrate complex nonlinear spatial and temporal…