Abstract: In this talk, I will present a new formulation of the V-formation problem for migrating birds in terms of model predictive control (MPC). In this approach, to drive a flock towards a desired formation, an optimal velocity adjustment (acceleration) is performed at each time-step on each bird's current velocity using a model-based prediction window of T time-steps. I will present both centralized and distributed versions of this approach. The optimization criteria used is based on fitness metrics of candidate accelerations that V-formations are known to exhibit. These include velocity matching, clear view, and upwash benefit. This MPC-based approach is validated by showing that for a significant majority of simulation runs, the flock succeeds in forming the desired formation. These results help to better understand the emergent behavior of formation flight, and provide a control strategy for flocks of autonomous aerial vehicles. This talk represents joint work with Radu Grosu, Ashish Tiwari, and Junxing Yang.

Biography: Scott Smolka is a professor in the Department of Computer Science at the State University of New York, Stony Brook. He is also president and co-founder of Reactive Systems, Inc. He has been on the faculty at SUNY at Stony Brook since 1982. Smolka holds an A.B. and A.M. in Mathematics from Boston University and a Ph.D. in Computer Science from Brown University. His work focuses on analysis techniques for reactive systems and he also has extensive experience in building verification tools, including Winston and the Concurrency Factory.

Abstract: Acceptance of autonomous systems at scales at which they can make societal and economical impact hinges on factors including how capable they are in delivering complicated missions in uncertain and dynamic environments and how much we can trust that they will operate safely and correctly. In this talk, we present a series of algorithms recently developed to address this need. In particular, these algorithms are for the synthesis of control protocols that enable agents to learn from interactions with their environment and/or humans while verifiably satisfying given formal safety and other high-level mission specifications in nondeterministic and stochastic environments.

We take two complementing approaches. The first approach merges data efficiency notions from learning (e.g., so-called probably approximate correctness) with probabilistic temporal logic specifications. The second one leverages permissiveness in temporal-logic-constrained strategy synthesis with reinforcement learning.

Biography: Ufuk Topcu joined the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin as an assistant professor in Fall 2015. He received his Ph.D. degree from the University of California at Berkeley in 2008. Before joining The University of Texas, he was with the Department of Electrical and Systems Engineering at the University of Pennsylvania. He was a postdoctoral scholar at California Institute of Technology until 2012.

Abstract: This talk provides a retrospective look at the past, present, and future of speed scaling systems. Such systems have the ability to auto-scale their service capacity based on demand, which introduces many interesting tradeoffs between response time (a classic performance metric) and energy efficiency (a relatively recent performance metric of growing interest).

The talk highlights key results and observations from the past two decades of speed scaling research, which appears in both the theory and systems research communities. One theme in the talk is the dichotomy between the assumptions, approaches, and results in these two research communities. Another theme is that modern processors support surprisingly sophisticated speed scaling functionality, which is not yet well-harnessed by current algorithms or operating systems.

During the stroll, I will also share some insights and observations from our own work on speed scaling designs, including coupled, decoupled, and turbo-charged systems. This work includes analytical and simulation modeling, as well as empirical system measurements. The talk closes with thoughts about future opportunities in speed scaling research.

Biography: Carey Williamson is a Professor in the Department of Computer Science at the University of Calgary. His educational background includes a B.Sc.(Honours) degree in Computer Science from the University of Saskatchewan in 1985, and a Ph.D. in Computer Science from Stanford University in 1992. Dr. Williamson's research interests include Internet protocols, wireless networks, network traffic measurement, workload characterization, network simulation, and Web server performance. From 2001-2011, he held an iCORE (Informatics Circle of Research Excellence) Chair in "Broadband Wireless Networks, Protocols, Applications, and Performance", as well as an NSERC Industrial Research Chair in "Wireless Internet Traffic Modeling" from 2004-2009.

He is a member of ACM, IEEE Computer Society, and IFIP WG 7.3. He served as SIG Chair for ACM SIGMETRICS from 2007-2011, and is currently the co-Editor-in-Chief of the ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS).