Note: the registration fee is waived for this Workshop, and it has limited capacity
Organizers:
Abstract: This workshop will invite participants to engage in a confidential, respectful environment in which to learn about the concept of implicit biases and they ways in which those biases operate.
Abstract: Computation is an essential component of implementing any real-world control system, but the details of how to make this work are often either left to the individual contributors to figure out or handed off to turn-key vendors.
Note: the registration fee is waived for this Workshop, and it has limited capacity
Organizers:
Abstract: This interactive session describes different types of exclusionary behaviors that affect recruitment and retention into STEM, the academic practices and institutional structures that allow for exclusionary behaviors to persist and provides training in personal intervention strategies to protect and support targets of exclusionary behaviors.
Abstract: The workshop's primary objective is to create a platform for connecting various communities, fostering collaboration for the advancement of a compositional theory of decision making.
Abstract: Although connected and autonomous vehicle (CAV) technologies have enormous potential to enhance network mobility and sustainability in modern traffic networks, their deployment on roads shared with human vehicles requires careful consideration to avoid unintended outcomes in the resulting mixed-autonomy system.
Abstract: Cyber-physical-human systems (CPHS) represent a class of networked humans and control systems with vast and promising applications, such as smart cities, distributed sensing and control based on Internet-of-Things (IoT) devices, and utility networks. Such systems are expected to provide outstanding functionalities and positively influence our life and society.
Abstract: Battery management systems (BMS) are essential to the efficient, safe, and reliable operation of battery energy storage systems, particularly in applications such as electric vehicles, renewable energy systems, and consumer electronics.
Abstract: In recent years, learning-based control has gained significant attention for its potential to improve control design by leveraging advances in machine learning (ML) and reinforcement learning (RL).
Abstract: This workshop will highlight the importance of managerial decision-making and how it can profitably be viewed as an application for control science and engineering—a challenging, fascinating, and important avenue for control scientists and engineers to exploit their expertise and to benefit society.
Abstract: As autonomous systems are increasingly integrated into safety-critical applications, ensuring their reliable performance under uncertainty is crucial. Traditional control methods often face difficulties balancing high performance with risk management, especially in dynamic environments with incomplete or evolving data.
Abstract: Given the growing ubiquity of things like autonomous vehicles, robots, and arrays of edge computing devices and sensors (collectively, known as the Internet of Things), the path forward in AI involves a progression from systems capable of addressing tasks within narrow, specialized domains—referred to as artificial narrow intelligence (ANI)—to systems with the ability to solve problems across multiple domains at or beyond human capability, known as artificial general intelligence (AGI) and artificial superintelligence (ASI).
Abstract: This workshop will showcase cutting edge of research on the effect of communication constraints on learning, control, and estimation in dynamical systems. Corruption and omission of data due to communication constraint, disruption, and delay, presents a daunting challenge for real-time learning and control of complex systems due to its uncertainty, environmental dependence, and hardware limitation.
Abstract: In recent years, research at the intersection of machine learning and classical engineering domains has grown exponentially. Machine learning is increasingly being utilized to develop novel data-driven approaches for modeling and controlling dynamical systems, which were traditionally dominated by physics-based models and scientific computing solvers.
Abstract: Neural networks are becoming more prevalent in an increasing number of safety-critical autonomy applications. It is important to ensure that these data-driven autonomous systems are safe given challenges associated with deployment in the real world.
Abstract: Our workshop on the “Capabilities of Large Language Models (LLMs) in Control Design and Decision Making” focuses on a timely and rapidly evolving intersection of artificial intelligence (AI) and control systems. Recent advancements in LLMs have significantly expanded the scope of what AI can accomplish in complex problem-solving, driving applications across domains such as coding, reasoning, mathematics, and science.
