DNA is used in countless applications that range from medical testing, forensics sciences, and population migration studies to diet and online dating recommenders. The simple to describe yet complex to understand DNA winding staircase sequences have deeply penetrated popular imagination, but very few have ever contemplated the use of DNA molecules as a solution for current data storage problems. It is hard to imagine DNA as a storage medium that can replace flash or hard disks, yet the last five years have shown that molecular storage is a completely viable, although costly, technology.
We review the basics of writing information in DNA, reading and randomly accessing specific content in DNA, and then proceed to describe several system implementations and accompanying coding and signal processing methods. We also outline ongoing efforts to combine DNA storage with molecular computing, construct in memory image processing platforms, and merge DNA and synthetic polymers for increased storage capacity.
Olgica Milenkovic is a professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC), and Research Professor at the Coordinated Science Laboratory. She obtained her Masters Degree in Mathematics in 2001 and PhD in Electrical Engineering in 2002, both from the University of Michigan, Ann Arbor. Prof. Milenkovic heads a group focused on addressing unique interdisciplinary research challenges spanning the areas of algorithm design and computing, bioinformatics, coding theory, machine learning and signal processing. Her scholarly contributions have been recognized by multiple awards, including the NSF Faculty Early Career Development (CAREER) Award, the DARPA Young Faculty Award, the Dean’s Excellence in Research Award, and several best paper awards. In 2013, she was elected a UIUC Center for Advanced Study Associate and Willett Scholar. In 2015, she became Distinguished Lecturer of the Information Theory Society. From 2007 until now, she has served as Associate Editor of the IEEE Transactions of Communications, the IEEE Transactions on Signal Processing, the IEEE Transactions on Information Theory and the IEEE Transactions on Molecular, Biological and Multi-Scale Communications. In 2009, she was the Guest Editor in Chief of a special issue of the IEEE Transactions on Information Theory on Molecular Biology and Neuroscience.
The next generation of wireless mobile networks is expected to provide connectivity services to a new suite of applications including augmented and virtual reality, tactile internet, driverless transportation, robotics, etc. Furthermore, the number of communicating devices is growing rapidly, as well as the amount of information exchanged. New types of service requirements are emerging like very low latency communication while at the same time computation and data storage/access move closer to the edge from the backend cloud. To meet these challenges, innovations are expected at all levels starting from device and physical layer to network and system level. In this presentation, we will concentrate on advances at the higher layers. Softwarization of network functionality is a key enabler for virtualizing network resources to provide flexible connectivity services. We will present our recent results on the controller placement of software defined wireless networks, including optimal controller replication strategies at the wireless mobile network periphery. Then we will focus on the issue of information availability and how to increase it. We will present our recent results on innovative caching approaches at the network edge that (i) leverage the broadcast nature of the wireless medium to serve concurrent requests for content and (ii) exploit the regularity of user mobility patterns to prefetch information at the base stations that are likely to be accessed. Finally, we will present a framework and associated mechanisms that facilitate exchange of resources among the constituents of a 5G ecosystem such that service virtualization is achieved while reciprocity is ensured among the participants.
Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering at Yale University. His research interests are in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, architectures and protocols of wireless systems, sensor networks, novel internet architectures and experimental platforms for network research. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the inaugural INFOCOM 2007 Achievement Award "for fundamental contributions to resource allocation in communication networks," several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991). He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park, University of Ioannina and University of Thessaly, Greece.
How much information is “leaked” in a wiretap channel, or a side channel more generally? Despite decades of work on these channels, including the development of many sophisticated mitigation mechanisms for specific side channels, the fundamental question of how to measure the key quantity of interest – leakage - has received surprisingly little attention. Many metrics have been used in the literature but these metrics either lack a cogent operational justification or mislabel systems that are obviously insecure as secure. Mutual information, in particular, while often used as a leakage measure, does not have a clear operational interpretation in the context of side channels.
We propose a new metric called “maximal leakage,” defined as the logarithm of the multiplicative increase, upon observing the public data, of the probability of correctly guessing a randomized function of the private information, maximized over all such randomized functions. We provide an operational justification for this definition, show how it can be computed in near-closed form, and discuss how it relates to existing metrics, including mutual information, differential privacy, and a certain under-appreciated metric in the computer science literature. We also present a solution to Shannon's cipher system under this metric, which can be applied to design optimal side channel mitigation strategies. Among other findings, we show that mutual information underestimates leakage while local differential privacy overestimates it.
Aaron Wagner is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University. He received the B.S. degree from the University of Michigan, Ann Arbor, and the M.S. and Ph.D. degrees from the University of California, Berkeley. During the 2005-2006 academic year, he was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor in the School of Electrical and Computer Engineering at Cornell.
He has received the NSF CAREER award, the David J. Sakrison Memorial Prize from the U.C. Berkeley EECS Dept., the Bernard Friedman Memorial Prize in Applied Mathematics from the U.C. Berkeley Dept. of Mathematics, the James L. Massey Research and Teaching Award for Young Scholars from the IEEE Information Theory Society, and teaching awards at the Department, College, and University level at Cornell.
The past decade of research on matrix completion has shown it is possible to leverage linear dependencies to impute missing values in a low-rank matrix. However, the corresponding assumption that the data lies in or near a low-dimensional linear subspace is not always met in practice. Extending matrix completion theory and algorithms to exploit low-dimensional nonlinear structure in data will allow missing data imputation in a far richer class of problems. In this talk, I will describe several models of low-dimensional nonlinear structure and how these models can be used for matrix completion. In particular, we will explore matrix completion in the context of three different nonlinear models: single index models, in which a latent subspace model is transformed by a nonlinear mapping; unions of subspaces, in which data points lie in or near one of several subspaces; and nonlinear algebraic varieties, a polynomial generalization of classical linear subspaces. In these settings, we will explore novel and efficient algorithms for imputing missing values and new bounds on the amount of missing data that can be accurately imputed. The proposed algorithms are able to recover synthetically generated data up to predicted sample complexity bounds and outperform standard low-rank matrix completion in experiments with real recommender system and motion capture data.
Rebecca Willett is an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. Willett received the National Science Foundation CAREER Award in 2007, is a member of the DARPA Computer Science Study Group, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Willett has also held visiting researcher or faculty positions at the University of Nice in 2015, the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Healthcare in 2002.