Georgia Institute of Technology College of Sciences

TBA

We will present a broad overview of UTRC’s research initiative in Autonomous and Intelligent Systems (AIS) that was created to conceive, develop and mature a broad range of intelligent mobile robotic systems and capabilities to enhance and support the diverse array of businesses that comprise the United Technologies Corporation. While initial efforts have been focused on Sikorsky Aircraft unmanned rotorcraft, the initiative is now expanding to include other aerospace and commercial applications, as well. The research, conducted by a diverse team of researchers in robotics, dynamical systems, control, applied mathematics, computer vision, and computer science (in partnership with several leading universities including CMU, MIT, UPenn, and UCB) includes: • Real-time algorithms for dynamic collision avoidance in an obstacle-rich environment using probabilistic roadmaps. • Navigation with imperfect and intermittent sensors in GPS degraded environments. • Multi-vehicle missions including efficient robotic search algorithms based on ergodic theory methods. • Collaborative motion planning for multiple aerial and ground robots in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. • Intelligent system design methodology including architectures for autonomy, human-machine systems, and formal verification. We will conclude with research problems of interest to UTRC and discuss existing and future career and internship opportunities in the broad area of autonomy and robotics.

The math we learn in school can seem like a dull set of rules, laid down by the ancients and not to be questioned. In How Not to Be Wrong, Jordan Ellenberg shows us how wrong this view is: Math touches everything we do, allowing us to see the hidden structures beneath the messy and chaotic surface of our daily lives. It’s a science of not being wrong, worked out through centuries of hard work and argument.

The syntax of theoretical physics and modern finance is deceptively similar, but the semantics is very different. I present a short introduction to the principles of modern finance, and compare and contrast the field to physics.