Data-Driven Dynamic Networked Systems

One-day Workshop as part of the

1st Annual CROSS Research Symposium

Organizer: Ricardo Sanfelice

Abstract: The performance and robustness of a vast majority of systems of the future, such as autonomous aerial systems, self-driving cars, internet of things, and cloud computing, will rely on algorithms that efficiently process large amounts of data.  Such is the case when the algorithms are in charge of making decisions according to (dynamically changing) measurements obtained from networked sensors, mobile agents, and users.  In these systems, the storage and computing requirements can be quite expensive and a limiting factor in their deployment. The goal of this workshop is to showcase advances obtained by researchers from industry and academia that tackle key challenges in the development of such dynamic networked systems emerging in autonomous systems, internet of things, smart storage, and cloud computing research.

Date: October 24

Location: UCSC campus


8:15am - Registration and Continental Breakfast

8:45am - Welcome

9:00am - Keynote

9:30am - Presentation Preview Spotlight

Summary SlidesWorkshopSummary-CROSS-DDDNS2016.pdf

10:00am - Break and prepare for the start of workshops

Schedule for Workshop Data-Driven Dynamic Networked Systems


Speaker: Marco Pavone, Assistant Professor, Stanford

Title: Models, Algorithms, and Evaluation for Autonomous Mobility-On-Demand Systems

Abstract: In this talk I will discuss the operational and economic aspects of autonomous mobility-on-demand (AMoD) systems, a rapidly emerging mode of personal transportation wherein robotic, self-driving vehicles transport customers in a given environment. Specifically, I will discuss AMoD systems along three dimensions: (1) modeling -- analytical models capable of capturing the salient dynamic and stochastic features of customer demand, (2) control -- coordination algorithms for the vehicles aimed at throughput maximization, and (3) economic -- fleet sizing and financial analyses for case studies of New York City and Singapore. I will conclude the talk by presenting a number of directions for future research.

Bio: Dr. Marco Pavone is an Assistant Professor of Aeronautics and Astronautics at Stanford University, where he also holds courtesy appointments in the Department of Electrical Engineering, in the Institute for Computational and Mathematical Engineering, and in the Information Systems Laboratory. He is a Research Affiliate at the NASA Jet Propulsion Laboratory (JPL), California Institute of Technology. Before joining Stanford, he was a Research Technologist within the Robotics Section at JPL. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. Dr. Pavone’s areas of expertise lie in the fields of controls and robotics.


Speaker: John Musacchio, Professor, Technology and Information Management Program, UCSC

Title: Challenges of Implementing Incentive Mechanisms for Reducing Infrastructure Congestion

Abstract: Infrastructures of all kinds are under increasing pressures and a commensurate increase in capacity in many of these cases is not economically possible. New schemes are needed to reduce peak-time congestion and/or achieve so-called ``demand response’’ to periods of infrastructure saturation. Examples include: i) mobile data traffic, which has been exploding in the past few years while carriers struggle to keep up; ii) electric utilities faced with maintaining moment by moment balance of supply and demand with an increasing fraction of renewable, randomly fluctuating, energy sources; iii) road traffic that has far-outpaced the ability of governments to add capacity. The ability to collect fine-grained data about individual’s usage and to interact with individuals using smart-phone and web based applications opens up a large number of possibilities for developing more personalized and more congestion-adaptive incentive schemes to mitigate the problems. For instance lottery-based schemes have been proposed that give each user a reward proportional to his percentage contribution to the aggregate reduction in peak-time demand. Implementing such schemes pose technological challenges in collecting data and accounting for the various payments and rewards while addressing concerns about user privacy. Moreover the schemes and the data collection infrastructure need to be made robust to attempts to manipulate the schemes with false data.

Bio: John Musacchio is an associate professor with the new Technology and Information Management Program at the University of California Santa Cruz. Professor Musacchio’s research interests include network economics, game theory, stochastic modeling and control of queuing networks with applications in communications networks.

Slides: MusacchioCROSS-DDDNS2016.pdf


Speaker: Katia Obraczka, Professor, Computer Engineering, UCSC

Title: Towards the Internets of the Future

Abstract: In this talk, I will describe our research at UCSC's Internetworking Research Group (i-NRG) which is inspired and motivated by the internets of the future. Future internets pose many challenges that include not only their scale, but also high degree of heterogeneity and administrative decentralization.

Bio: Katia Obraczka is a Professor at UC Santa Cruz’s Jack Baskin School of Engineering. Her research and teaching interests include computer networks, distributed systems, Internet information systems, and operating systems. Prof. Obraczka received her B.S. and M.S. in electrical and computer engineering from the Federal University of Rio de Janeiro. She received her M.S and Ph.D. degrees in Computer Science from the University of Southern California in 1990 and 1994, respectively.

12:00pm (noon) - Lunch


Speaker: Stefano Carpin, Professor, Computer Science and Engineering, UCM

Title: From Distributed Robotics to Cloud Robotics

Abstract: “Cloud Robotics” is the natural evolution for distributed robotic systems, i.e., systems composed of multiple robots cooperatively working to solve a task, often in collaboration with humans or to support human decision making. In this talk I will present some of our recent work in industrial and service robotics and outline how the combination of pervasive connectivity and distributed sensing and computing is enabling a new generation of robotic applications, but also posing new computational and scientific challenges.

Bio: Stefano Carpin is Professor of Computer Science and Engineering at UC Merced. He received his “Laurea” and Ph.D. degrees in electrical engineering and computer science from the University of Padova (Italy) in 1999 and 2003. From 2003 to 2006 he held faculty positions with Jacobs University Bremen (Germany). Since 2007 he has been with UC Merced, where he established the UC Merced robotics laboratory. His research interests include mobile and cooperative robotics, and robot algorithms. He published more than 100 papers in leading journals and conferences, and serves in the editorial boards of the IEEE Transactions on Automation Science and Engineering and the IEEE Robotics and Automation Letters.



Speaker: Lise Getoor, Professor, Computer Science, UCSC

Title: Scalable Collective Reasoning over Network Data

Abstract: Graph data (e.g., communication data, financial transaction networks, data describing biological systems, collaboration networks, the Web, etc.) is ubiquitous. While this observational data is useful, it is usually noisy, often only partially observed, and only hints at the actual underlying social, scientific or technological structures that give rise to the interactions. For example, an email communication network provides useful insight, but is not the same as the “real” social network among individuals. In this talk, I introduce the problem of graph identification, i.e., the discovery of the true graph structure underlying an observed network.  This involves inferring the nodes, edges, and node labels of a hidden graph based on evidence provided by the observed graph.   I show how this can be cast as a collective probabilistic inference task, highlight the importance of performing this task before further analysis, mining, or querying is done, and describe a scalable approach to solving this problem.

Bio: Lise Getoor is a professor in the Computer Science Department at the University of California, Santa Cruz.  Her research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data. She has over 200 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the DARPA ISAT Study Group (2016-2019) and the board of the Computing Research Association (CRA), and was co-chair for ICML 2011.  She is a recipient of an NSF Career Award and eleven best paper and best student paper awards.  In 2014, she was recognized by KDD Nuggets as one of the emerging research leaders in data mining and data science based on citation and impact.   She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013.