Plenary Speakers

Abstract: [Coming soon]. 

Biological cells are complex dynamic systems in which sensing, control, and actuation are orchestrated by networks of active molecules with coupled functions. Cells not only detect and process information, but also leverage that information to direct the assembly of physical structures, such as scaffolds, membranes, and organelles. A productive approach to understanding the design principles governing these multicomponent systems involves the constructive, bottom-up synthesis of biomolecules capable of performing dynamic tasks. Many challenges emerge when building such systems, such as parametric uncertainty, unintended interactions, and the breakdown of signaling modularity, which can be better understood by synthesis efforts outside the cell. I will discuss these challenges in the context of in vitro synthetic biology, which offers a simplified environment for constructing coupled biochemical reaction networks and self-assembling systems. I will begin by describing methods to design artificial nucleic acids (DNA and RNA) that drive the creation of signal generators and components that self-assemble into a variety of structural elements, like filamentous crystals and amorphous condensates. Next, I will explore how these signaling networks and self-assembled structures can be interconnected via the design of nucleic acid sequences, using computational tools to program their interactions. I will then discuss how pulse generators and oscillator circuits can regulate the formation and dissolution of self-assembled physical structures. I will also describe how these techniques have enabled the creation of dynamic, artificial organelles within living cells. These advances contribute to our broader goal of controlling physical matter through biomolecular reactions, paving the way for the development of intelligent biological materials that can sense and make decisions through embedded control programs.

The rapid proliferation of artificial intelligence (A.I.) and large language models (LLMs) is revolutionizing our world. However, as these systems increasingly find real-world applications in controlling physical systems—such as autonomous robots, self-driving cars, and other critical infrastructure—their potential to cause harm has escalated dramatically. This is due to large error rates, lack of robustness, hallucinations, as well as a new LLM attack known as jailbreaking.  Ensuring safety in safety critical contexts requires a paradigm shift from traditional A.I. development toward robust safety mechanisms. In this talk, I will explore how ideas from control theory can provide rigorous tools and frameworks for developing safety filters tailored towards control systems with deep learning in the loop and LLM-controlled robots, including VLA-controlled robots. By leveraging tools such as integrated quadratic constraints, temporal logic synthesis, and control barrier functions, I will address how our community can play a crucial role in designing A.I. safety systems that effectively mitigate risks while preserving the utility and adaptability of A.I. in real world applications.

Abstract: Recent advances in large foundation models, such as large language models (LLMs) and diffusion models, have demonstrated impressive capabilities. However, to truly align these models with user feedback or maximize real-world objectives, it is crucial to exert control over the decoding processes, in order to steer the distribution of generated output. In this talk, we will explore methods and theory for controlled generation within LLMs and diffusion models. We will discuss various modalities or achieving this control, focusing on applications such as alignment of LLM, accelerated inference, transfer learning, and diffusion-based optimizer.