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…

ABSTRACT: Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate as a team through effective communication and coordination to enhance task success, safety, and efficiency. These bring a few significant…

ABSTRACT: Integrating artificial intelligence (AI) and machine learning (ML) into computational modeling frameworks is revolutionizing pharmacokinetic evaluation, offering transformative insights for both small-molecule drugs and the complex field of nanomedicine. This presentation highlights the pivotal role of AI-assisted…

ABSTRACT: Sensing is a universal task in science and engineering. Downstream tasks from sensing include learning dynamical models, inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy…

ABSTRACT:  Large language models (LLMs) like Claude Sonnet 3.7, GPT-4, Llama 3.1, and Gemini 2.5 have shown promise in AI-assisted coding, transforming natural language descriptions into code. While LLMs perform well on simple benchmarks like LeetCode, MBPP, and HumanEval, their success rate remains low in real-world…

ABSTRACT:  Anomaly Detection (AD) focuses on identifying anomalies by learning exclusively from normal samples, as collecting anomalous data is often costly and limited due to its long-tailed distribution. Given its practical significance, AD has been widely deployed in applications such as industrial defect inspection and…

ABSTRACT:  Embodied AI systems are cyber-physical systems that employ advanced machine learning techniques to perceive, analyze, and interact with their environment. As these systems become more prevalent in fields such as self-driving cars and robotics, ensuring their safety and robustness has become a critical and yet…