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List of Speakers:
, California Institute of Technology
Behnaam Aazhang, Rice University
On Network Modulation for Intractable Epilepsy
Abstract: Epilepsy affects three million patients in the United States. In many patients with pharmacologically refractory seizures, the only effective treatment is the neurosurgical resection of abnormally synchronized hyperexcitable brain regions—the seizure onset zone. Resection carries a risk of damaging important cognitive functions, and thus creating an effective non-resective option is critical to the welfare of millions of patients. It is now believed that the future of epilepsy research lies in building an implantable device that prevents the brain from going into a hyperactive state, similar to how a pacemaker controls abnormal heart rhythms. The implanted device should monitor the neural activity in real-time and then apply electrical stimulation designed to modulate the seizure network adaptively and selectively to prevent the network from seizing. In this presentation, we propose a paradigm to capture the dynamic, frequency dependent connectivity of the brain from real-time monitoring of the braiusing ECoG (i.e., ElectroCorticoGraphy) and then identifying the “optimal” stimulation parameters to modulate the connectivity with temporal and spatial precision. In particular, we will demonstrate how we apply directed information to identify seizure onset zone and to determine seizure timing information in order to develop ideal stimulation protocols as a first step on a roadmap for reparative therapies.
Biography: Behnaam Aazhang received his B.S. (with highest honors), M.S., and Ph.D. degrees in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in 1981, 1983, and 1986, respectively.From 1981 to 1985, he was a Research Assistant in the Coordinated Science Laboratory, University of Illinois. In August 1985, he joined the faculty of Rice University, Houston, Texas, where he is now the J.S. Abercrombie Professor in the Department of Electrical and Computer Engineering Professor and Director of Center on Neuro-Engineering, a multi-university research center in Houston, Texas. In addition, he holds an Academy of Finland Distinguished Visiting Professorship appointment (FiDiPro) at the Center for Wireless Communication (CWC) in the University of Oulu, Oulu, Finland. He served as the Chair of the Department of Electrical and Computer Engineering from 2004-2014, and served as the founding director of Rice’s Center for Multimedia Communications from 1998 till 2006.His research interests are in the areas of communication theory, information theory, signal processing, and their applications to wireless communication, wireless networks, and neuro-engineering with emphasis on closed-loop neuro-modulation and modeling of neuronal circuits connectivity and the impact of learning on connections in the circuits. Dr. Aazhang is a Fellow of IEEE and AAAS, a distinguished lecturer of IEEE Communication Society, and also a recipient of 2004 IEEE Communication Society’s Stephen O. Rice best paper award for a paper with A. Sendonaris and E. Erkip. In addition, Sendonaris, Erkip, and Aazhang received IEEE Communication Society’s 2013 Advances in Communication Award for the same paper. He has been listed in the Thomson-ISI Highly Cited Researchers and has been keynote and plenary speaker of several conferences.
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Elisa Franco, University of California, Riverside
Programming Dynamic Circuits and Materials with Nucleic Acids
Abstract: Cells have unique abilities to sense, process, and actuate based on environmental stimuli: their molecular components are constantly running many parallel programs that ensure correct growth, motion, reshaping, and repair in response to external inputs. How can modern engineers harness such powerful toolkit of DNA, RNA, and proteins to create the next generation of molecular computers and smart biomaterials? I will describe our efforts in this area, which are centered on the combination of nucleic acids nanotechnology and dynamical systems theory. First, I will summarize our efforts in the design and synthesis of synthetic molecular clocks, essential devices to synchronize events in molecular computers. Specifically, I will describe the challenges arising in scaling up clock-driven circuits. Second, I will outline our progress in the creation of advanced, dynamic biomaterials, inspired to cytoskeletal filaments in cells, using DNA nanostructures powered by oscillators.
Biography: Elisa Franco received her B.S. and M.S. (Laurea Degree) in Power Systems Engineering from the University of Trieste (Italy) in 2002, summa cum laude. In 2007, she received her Ph. D. in Automation from the same institution. In 2011, she completed her second Ph. D. at the California Institute of Technology, Pasadena, in Control and Dynamical Systems. Her research interests are in the areas of dynamic DNA/RNA nanotechnolgoy, biological feedback, and molecular oscillators. She received the NSF CAREER award in 2015, a Hellmann fellowship and a UC Regents Fellowship in 2013.
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Scott Fraser, University of Southern California
Imaging and Sensing the Molecular Signals that Pattern Development
Abstract: The challenge of modern embryology is to draw upon the growing body of high-throughput molecular data to better understand the underlying cellular and molecular mechanisms. Imaging offers a potential answer, but is challenged by tradeoffs between resolution and the photon budget. We are attempting to advance this tradeoff by constructing two-photon light-sheet microscopes, combining the deep penetration of two-photon microscopy and the speed of light sheet microscopy to generate images with more than ten-fold improvement in speed and sensitivity. This permits cell and molecular imaging with sufficient speed and resolution to generate unambiguous tracing of cells and signals in intact systems. In parallel, we are refining label-free molecular sensors, deploying a new technology for enhancing the weak signals inherent in single cell studies. Finally, we are deploying new tools for following receptor binding and mutlimerization in response to signals.
Biography: Scott E. Fraser is the Elizabeth Garrett Chair of Convergent Biosciences at USC. He has a long-standing commitment to quantitative biology, applying the tools of chemistry, engineering, and physics to problems in biology and medicine. His personal research centers on imaging and molecular analyses of intact biological systems, with an emphasis on early development, organogenesis, and medical diagnostics.After training in physics (BS, Harvey Mudd College, 1976) and biophysics (PhD, Johns Hopkins University, 1979), he joined the faculty at UC Irvine, and rose through the ranks to become Chair of the Department of Physiology and Biophysics. In 1990 he moved to Caltech to serve as the Anna L. Rosen Professor of Biology, and the Director of the Biological Imaging Center. He is deeply committed to interdisciplinary training and translational research, having helped found the Caltech Brain Imaging Center and the Kavli Institute of Nanoscience, as well as serving as the Director of the Rosen Center for Biological Engineering.In Fall 2012, he moved to USC to take a Provost Professorship in the Dornsife College of Letters Arts and Sciences, the Children’s Hospital Los Angeles, Keck School of Medicine and the Viterbi School of Engineering. He remains active in interdisciplinary research and serves as the Director of Science Initiatives for the USC campuses.
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Andrea Goldsmith, Stanford University
Exploiting the Theories of Communication and Information in Biology and Neuroscience
Abstract: This talk will describe some of our recent work in applying information and communication theory to the areas of biology and neuroscience. We first describe our use of directed mutual information to infer signaling structures in the brain: specifically to infer neuronal connections based on spike train data as well as predict from electrocorticogram (ECoG) data the precise brain region that is the focal point of an epileptic seizure. We will also discuss our work on applying multiuser spread-spectrum ideas to estimate the number, identity, and proportion of different cell-types in microarray data of heterogeneous tissue samples. Finally, we will describe the application of well-known communication principles to the design of chemical communication systems, including support for multiple users, multidimensional signal constellations, and mitigation of intersymbol interference. The overarching theme of the talk is that the tools of information and communication theory have an important role to play in advancing the brain and biological sciences and that, moreover, knowledge about these sciences can be used to engineer new communication systems.
Work is joint with Todd Coleman, Nariman Farsad, Peter Lee, Rui Ma, Yair Noam, Nima Soltani, and Neta Zuckerman.
Biography: Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and a professor of Electrical Engineering at Stanford University. She has also held positions in industry including co-founder and CTO of Wildfire.Exchange and Quantenna Communications, Inc. Dr. Goldsmith is a Fellow of the IEEE and of Stanford, and has received several awards for her work, including the IEEE ComSoc Edwin H. Armstrong Achievement Award as well as Technical Achievement Awards in Communications Theory and in Wireless Communications, the National Academy of Engineering Gilbreth Lecture Award, the IEEE ComSoc and Information Theory Society Joint Paper Award, the IEEE ComSoc Best Tutorial Paper Award, the Alfred P. Sloan Fellowship, the WICE Technical Achievement Award, and the Silicon Valley/San Jose Business Journal’s Women of Influence Award. She is author of the book “Wireless Communications” and co-author of the books “MIMO Wireless Communications” and “Principles of Cognitive Radio,” all published by Cambridge University Press, as well as an inventor on 28 patents. Dr. Goldsmith has served on the Steering Committee for the IEEE Transactions on Wireless Communications and as editor for the IEEE Transactions on Information Theory, the Journal on Foundations and Trends in Communications and Information Theory and in Networks, the IEEE Transactions on Communications, and the IEEE Wireless Communications Magazine. She participates actively in the IEEE Information Theory and Communications Societies and has served on the Board of Governors and as a Distinguished Lecturer for both societies. In addition, she served as President of the Information Theory Society and founded and chaired its student committee. She also chaired Comsoc’s Emerging Technology Committee. At Stanford she received the inaugural University Postdoc Mentoring Award, served as Chair of Stanford’s Faculty Senate in 2009 and currently serves on its Faculty Senate, Budget Group, and Task Force on Women and Leadership. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.
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Pablo Iglesias, Johns Hopkins University
Information-theoretic characterization of cell signaling networks
Abstract: To a great extent, proper cell function requires the ability to make informed decisions based on imprecise measurements of the external environment. Though filtering can help reduce the effect of random perturbations, even the best filtering may not sufficiently remove noise to allow adequate information about the environment to be transmitted to the cell. Reducing the effect of noise to improve the fidelity of decision-making comes at the expense of increased complexity, creating a tradeoff between performance and metabolic cost. We present a framework based on rate distortion theory, a branch of information theory, to quantify this tradeoff. We illustrate the framework’s power by considering two types of signaling systems: directed cell motion and binary decision systems.
Biography: Pablo A. Iglesias was born in Caracas, Venezuela. He received the B.A.Sc. degree in Engineering Science from the University of Toronto in 1987, and the Ph.D. degree in Control Engineering from Cambridge University in 1991. Since then he has been on the faculty of the Johns Hopkins University, where he is currently the Edward J. Schaefer Professor of Electrical Engineering. He also holds appointments in the Departments of Biomedical Engineering, and Applied Mathematics & Statistics as well as the Department of Cell Biology in the Johns Hopkins School of Medicine. He has had visiting appointments at Lund University (Automatic Control), The Weizmann Institute of Science (Mathematics), the California Institute of Technology (Control and Dynamical Systems), and the Max-Planck Institute for the Physics of Complex Systems in Dresden, Germany.
Dr. Iglesias’s research focuses on the use of control and information theory to study biological signal transduction pathways.
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Andrew Mugler, Purdue University
Physical Limits to Sensing by Communicating Cells
Abstract: Single cells sense their environment with remarkable precision. At the same time, cells have evolved diverse mechanisms for communicating. How are sensing and communication related? In this talk I will describe a system in which connected epithelial cells, by communicating, can detect shallower chemical gradients than single cells can alone. I will present a minimal model of the system, from which fundamental limits on the precision of communication-aided sensing emerge. The work demonstrates that known sensory limits are altered when communication is accounted for, and thus extends the study of cellular sensing and information processing to collective ensembles.
Biography: Andrew Mugler works on sensing and information processing in cells. He is particularly known for work demonstrating that spatial effects at the molecular level, such as protein clustering, can alter sensing and computation at the cellular level. His future research will involve combining the analysis of single-cell sensing with cell-cell communication to develop a theory of collective sensing, applicable to multicellular processes such as cancer metastasis.
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Gregory Puleo, University of Illinois at Urbana-Champaign
Correlation Clustering in Bioinformatics
Abstract: In the most basic form of the correlation clustering model, one is given a set of objects and, for some pairs of objects, one is also given an assessment as to whether the objects are “similar” or “dissimilar”. This information is described using a graph G with labeled edges: each object is represented by a vertex of the graph, and the assessments are represented by edges labeled with either a + (for similar objects) or a – (for dissimilar objects). The goal is to partition the objects into clusters so that the edges within clusters are mostly positive and the edges between clusters are mostly negative. Unlike many other clustering models, such as k-means, the number of clusters is not fixed ahead of time. Instead, finding the optimal number of clusters is part of the problem. For this reason, correlation clustering has been used in machine learning as a model of “agnostic learning.”
We introduce a number of new correlation clustering techniques, such as clustering with bounded cluster sizes, minimax clustering and biclustering, and spectral correlation clustering and describe the applications of these methods for driver gene discovery and HiC data analysis.
This is a joint work with Olgica Milenkovic, Amin Emad, Jack Hou, and Jian Ma.
Biography: Gregory Puleo received his PhD in Mathematics from the University of Illinois at Urbana-Champaign in 2014, advised by Douglas B. West. He is currently part of the research group of Olgica Milenkovic at UIUC’s Coordinated Science Lab, working on problems related to clustering, coding theory, and extremal graph theory.
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Lulu Qian, California Institute of Technology
Creating Programmable Disorder in Self-Assembled DNA Nanostructures
Abstract: Molecular engineering approaches guided by simple and powerful mathematical principles have revolutionized technology and deepened our understanding of natural algorithms in many ways. A critical challenge is to scale up the complexity of nanoscale structures by taking advantage of the inherent stochasticity of molecular systems, while maintaining sufficient control with embedded deterministic rules. Here we show that the principle of non-deterministic Truchet tiling can be applied to provide a simple solution for creating complex nanoscale patterns that have combinatorial diversity and programmable features. As an example, we constructed patterns of random mazes with distinct emergent properties and with sizes of up to several microns, each self-assembled from thousands of square DNA origami tiles that are labeled with simple local patterns. We further demonstrated precise control of maze complexity by creating DNA origami arrays with unprecedented yield and designed sizes ranging from 4 to 25 tiles in each assembly, and showed the generality of our approach using arrays of triangular DNA origami tiles. The nanoscale mazes that we created are examples of “programmable disorder” in two-dimensional molecular structures. These structures could be used to test the robustness of molecular machines against a variety of operating environments with increasing complexity. Broadly speaking, by attaching proteins, metal nanoparticles, and organic dyes to origami arrays with combinatorial patterns of programmable features, our approach could enable efficient screening of functional molecular devices and advance nanoscale fabrication. Importantly, our work highlights the need for better understanding of programmable disorder and how it can be more generally applied in engineered molecular systems to enable solutions for problems that simultaneously demand complexity, diversity, and efficiency — much like the algorithms we see in nature that exploit a sophisticated blend of deterministic and random processes.
Biography: Lulu Qian is an Assistant Professor of Bioengineering at Caltech. She received her bachelor’s degree in Biomedical Engineering from Southeast University in China in 2002, and her Ph.D. in Biochemistry and Molecular Biology from Shanghai Jiao Tong University in 2007. She then worked as a postdoctoral scholar with Erik Winfree and Shuki Bruck at Caltech, and as a visiting fellow at Harvard University. Her work on scaling up logic computation with nucleic-acid circuits was the most complex synthetic biochemical circuit ever created, and showed that the strategy of building such systems can be reliable and scalable. She also developed synthetic nucleic-acid systems that exhibit autonomous brain-like behaviors, for example functioning as a Hopfield associative memory. Her work was the first artificial neural network created out of DNA, and suggested the possibility of embedding rudimentary artificial intelligence within biochemical systems. In September 2013, she established her laboratory at Caltech to develop scalable synthetic biochemical circuit architectures for fully general and efficient molecular information processing, to construct nucleic-acid devices with embedded learning, memory and advanced signal classification capabilities for next-generation therapeutics, and to understand the engineering principles for controlling complex motion at the molecular scale with synthetic nucleic-acid robots. Qian is a recipient of the Burroughs Wellcome Fund Career Award at the Scientific Interface, the Okawa Foundation Research Award, and the National Science Foundation Faculty Early Career Development Award.
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Marc Riedel, University of Minnesota
Computing with Crappy Clocks (C^3): A New Paradigm for Molecular Computing
Abstract: Clock distribution networks are a significant source of power consumption and a major design bottleneck for digital circuits, particularly with increasing variability. Completely asynchronous design methodologies have been studied for decades, but these have never gained widespread acceptance. We have proposed an alternative: splitting digital circuitry into small blocks and synchronizing these locally with independent, cheap clocks (generated with simple inverter rings). This is feasible if one adopts a stochastic representation for signal values. Logical computation is performed on randomized bit streams, with signal values encoded in the statistics of the streams. This talk will discuss extensions and applications of these ideas to molecular computing. DNA-based computation via strand displacement is the target experimental chassis.
Biography: Marc Riedel is Associate Professor of Electrical and Computer Engineering at the University of Minnesota. From 2006 to 2011 he was Assistant Professor. He is also a member of the Graduate Faculty in Biomedical Informatics and Computational Biology. From 2004 to 2005, he was a lecturer in Computation and Neural Systems at Caltech. He has held positions at Marconi Canada, CAE Electronics, Toshiba, and Fujitsu Research Labs. He received his Ph.D. and his M.Sc. in Electrical Engineering at Caltech and his B.Eng. in Electrical Engineering with a Minor in Mathematics at McGill University. His Ph.D. dissertation titled “Cyclic Combinational Circuits” received the Charles H. Wilts Prize for the best doctoral research in Electrical Engineering at Caltech. His paper “The Synthesis of Cyclic Combinational Circuits” received the Best Paper Award at the Design Automation Conference. He is a recipient of the NSF CAREER Award.
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Robert Schober, University of Erlangen-Nurnberg
Channel Estimation in Diffusion-Based Molecular Communication Systems
Abstract: In molecular communication (MC) systems, the expected number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this talk, we present a training-based CIR estimation framework for MC systems which aims at estimating the CIR based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter.
Biography: Robert Schober was born in Neuendettelsau, Germany, in 1971. He received the Diplom (Univ.) and the Ph.D. degrees in electrical engineering from the University of Erlangen-Nuermberg in 1997 and 2000, respectively. From May 2001 to April 2002 he was a Postdoctoral Fellow at the University of Toronto, Canada, sponsored by the German Academic Exchange Service (DAAD). From 2002 to 2012 he was a Professor and Canada Research Chair in Wireless Communications at the University of British Columbia (UBC), Vancouver, Canada. Since January 2012 he is an Alexander von Humboldt Professor and the Chair for Digital Communication at the Friedrich Alexander University (FAU), Erlangen, Germany. His research interests fall into the broad areas of Communication Theory, Wireless Communications, and Statistical Signal Processing.Dr. Schober received several awards for his research including the 2002 Heinz Maier–Leibnitz Award of the German Science Foundation (DFG), the 2004 Innovations Award of the Vodafone Foundation for Research in Mobile Communications, the 2006 UBC Killam Research Prize, the 2007 Wilhelm Friedrich Bessel Research Award of the Alexander von Humboldt Foundation, the 2008 Charles McDowell Award for Excellence in Research from UBC, a 2011 Alexander von Humboldt Professorship, and a 2012 NSERC E.W.R. Steacie Fellowship. In addition, he received best paper awards from the German Information Technology Society (ITG), the European Association for Signal, Speech and Image Processing (EURASIP), IEEE WCNC 2012, IEEE Globecom 2011, IEEE ICUWB 2006, the International Zurich Seminar on Broadband Communications, and European Wireless 2000. Dr. Schober is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, and a Fellow of the Engineering Institute of Canada.
Dr. Schober has served as Editor and Guest Editor on the Editorial Boards of several journals including the IEEE Transactions on Communications, the IEEE Journal on Selected Areas in Communications, the IEEE Transactions on Vehicular Technology, the Eurasip Journal on Advances in Signal Processing, and IEEE Sensors. He is currently the Editor-in-Chief of the IEEE Transactions on Communications.
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Gurol Suel, University of California, San Diego
Metabolic and electrical oscillations in bacterial biofilms
Abstract: Bacteria have been intensely studied for over a century and as a result we have gleaned many fundamental insights into biology. However, we are still relatively in the dark in regards to bacterial behavior within densely packed communities known as biofilms. I will present our latest efforts to develop new quantitative experimental approaches and mathematical modeling to study biofilms. In particular, I will share our findings on metabolic coordination within biofilms that give rise to oscillations in space and time, which can enhance collective fitness. Finally, I will present the discovery of a new form of bacterial cell-cell communication that is based on ion channel mediated electrical signaling. This electrical signaling appears to enable long-range coordination of metabolic states in the biofilm.
Biography: Dr. Suel received his PhD in Molecular Biophysics in 2003 and during his thesis work with Dr. Rama Ranganathan, he was part of a team that challenged the traditional view of protein function. Specifically, Dr. Suel applied a statistical thermodynamics approach to identify allosteric regulation in proteins. As a postdoc in the lab of Dr. Michael Elowitz, he generated the first direct experimental evidence that molecular noise (randomness) can determine cell fate outcomes. After starting his independent laboratory in 2007, Dr. Suel continued to define a biological role for noise by integrating single cell measurements, synthetic biology and mathematical modeling. His laboratory then expanded its focus to study bacterial biofilm communities. The work uncovered a cell death pattern that emerges during biofilm development and determines colony morphology by channeling mechanical forces. His group used this insight to engineer the 3D organization of biofilms by controlling cell death. Dr. Suel’s group also developed a microfluidics method to study biofilm growth and uncovered oscillations driven by spatio-temporal coordination of metabolic states among distant cells. These collective oscillations were shown to increase the resilience of biofilms against chemical attack by resolving the social conflict between cooperation and competition among bacteria. More recently, the Suel laboratory discovered a new form of bacterial communication that arises in biofilms: Ion channel mediated electrical cell-to-cell signaling. This finding revealed an unexpected connection between micro and neurobiology with many fundamental implications.
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David Tse, Stanford University and University of California, Berkeley
High Throughput Sequencing Assembly: Information and Computation
Abstract:High throughput sequencing has revolutionized biology and medicine in the past decade. Many high throughput assays have been created to probe various biological molecules such as DNA or RNA. The raw data from high throughput sequencing experiments are hundreds or millions of short reads, or fragments, from the biological molecules, and a key computational problem is to assemble these short reads to reconstruct the molecules. The classical approach to assembly problems is to formulate them as optimization problems; however most of these formulations turn out to be NP-hard. An alternative approach is to first ask an information theoretic question: how much data is needed to reconstruct the ground truth? We give several examples in which this approach gives rise to near linear time optimal assembly algorithms, by focusing only on the problem instances for which there is enough information to reconstruct the ground truth.
Biography:David Tse received the B.A.Sc. degree in systems design engineering from University of Waterloo in 1989, and the M.S. and Ph.D. degrees in electrical engineering from Massachusetts Institute of Technology in 1991 and 1994 respectively. From 1994 to 1995, he was a postdoctoral member of technical staff at A.T. & T. Bell Laboratories. From 1995 to 2014, he was on the faculty of the Department of Electrical Engineering and Computer Sciences in the University of California at Berkeley. He is currently a professor at Stanford University.He received a 1967 NSERC graduate fellowship from the government of Canada in 1989, a NSF CAREER award in 1998, the Best Paper Awards at the Infocom conference in 1998 and 2001, the Erlang Prize in 2000 from the INFORMS Applied Probability Society, the IEEE Communications Society and Information Theory Society Joint Paper Awards in 2000 and 2013, the Information Theory Society Paper Award in 2003, a Gilbreth Lectureship from the National Academy of Engineering in 2012, the Signal Processing Society Best Paper Award in 2012, the EURASIP Best Paper Award in 2012 and the IEEE Communications Society Stephen O. Rice Prize in 2013. For his contributions to education, he received the Outstanding Teaching Award from the Department of Electrical Engineering and Computer Sciences at U.C. Berkeley in 2008 and the Frederick Emmons Terman Award from the American Society for Engineering Education in 2009. He is a coauthor, with Pramod Viswanath, of the text Fundamentals of Wireless Communication, which has been used in over 60 institutions around the world. He is the inventor of the proportional-fair scheduling algorithm used in all third and fourth-generation cellular systems.He was an Associate Editor of the IEEE Transactions on Information Theory from 2001 to 2003, the Technical Program co-chair in 2004 and the General co-chair of the International Symposium on Information Theory in 2015. He served on the Board of Governors of the IEEE Information Theory Society from 2003 to 2008 and from 2010 to 2013. He was the plenary speaker for many international conferences and workshops, including the IEEE International Symposium on Information Theory in 2009, the ACM International Conference on Mobile Computing and Networking (MobiCom) in 2007 and the IEEE International Conference on Acoustics, Speech and Signal Processing in 2006.His research interests are in information theory and its applications in various fields, including wireless communication, energy and computational biology.
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Mihaela van der Schaar, University of California, Los Angeles
Understanding Bacterial Self-Organization using Network Formation Games
Abstract: This paper constructs a model to understand the formation of bacterial micro-colonies. This is important because bacterial micro-colonies form the first step toward the formation of structured multicellular communities (bacterial biofilms) that protect the bacteria against antimicrobials. We model the formation of micro-colonies as a dynamic network formation game among individual bacteria. We identify key properties of the evolution of the micro-colonies/networks and how these depend on the characteristics of the bacterial eco-system. In particular, we show that there are system parameters that guarantee that all bacteria will join micro-colonies and other system parameters that guarantee that some bacteria will not join micro-colonies. Our study characterizes the properties of the micro-colonies/networks that persist in the long run, and provides important insights on the process of micro-colony/network evolution and how that process affects the micro-colonies/networks that persist in the long run.
Biography: Mihaela van der Schaar is since 2011 Chancellor’s Professor of Electrical Engineering at University of California, Los Angeles. Her research interests include i) machine learning for decision making, online learning, real-time stream mining, healthcare informatics, machine learning for education; ii) network science, engineering economics and game theory, strategic design, societal, expert and social networks, reputation systems, population dynamics; iii) optimization and learning in multi-user networks and system designs, wireless networks, information and communications technology for the integration of renewable energies and the efficient use of energy, multimedia.She is an IEEE Fellow since 2010, a Distinguished Lecturer of the Communications Society for 2011-2012, the Editor in Chief of IEEE Transactions on Multimedia and a member of the Editorial Board of the IEEE Journal on Selected Topics in Signal Processing. She received an NSF CAREER Award (2004), the Best Paper Award from IEEE Transactions on Circuits and Systems for Video Technology (2005), the Okawa Foundation Award (2006), the IBM Faculty Award (2005, 2007, 2008), the Most Cited Paper Award from EURASIP: Image Communications Journal (2006), the Gamenets Conference Best Paper Award (2011) and the 2011 IEEE Circuits and Systems Society Darlington Award Best Paper Award. She received three ISO awards for her contributions to the MPEG video compression and streaming international standardization activities, and holds 33 granted US patents. She is also the founding director of the UCLA Center for Engineering Economics, Learning, and Networks.
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Haris Vikalo, The University of Texas at Austin
Decoding Genetic Variations: Algorithms for Haplotype Assembly
Abstract: Rapid advances in high-throughput DNA sequencing have enabled unprecedented studies of genetic variations. Information about variations in the genome of an individual is provided by haplotypes, ordered collections of polymorphisms on a chromosome. Knowledge of haplotypes is instrumental in finding genes associated with diseases, drug development and evolutionary studies. Haplotype assembly from high-throughput sequencing data is an NP-hard problem rendered challenging due to errors and limited lengths of sequencing reads. Our key observation is that the minimum error-correction formulation of the haplotype assembly problem is identical to the task of deciphering a coded message received over a noisy channel – a classical problem in the mature field of communication theory. Exploiting this connection, we develop novel haplotype assembly schemes and study the problem from an information-theoretic perspective. Moreover, relying on an alternative formulation of haplotype assembly as a structured matrix factorization, we develop and analyze iterative algorithms that efficiently solve the assembly problem in both diploid and polyploid setting.
Biography: Haris Vikalo received the B.S. degree from the University of Zagreb, Croatia, in 1995, the M.S. degree from Lehigh University in 1997, and the Ph.D. degree from Stanford University in 2003, all in electrical engineering. He held a short-term appointment at Bell Laboratories, Murray Hill, NJ, in the summer of 1999. From January 2003 to July 2003 he was a Postdoctoral Researcher, and from July 2003 to August 2007 he was an Associate Scientist at the California Institute of Technology. Since September 2007, he has been with the Department of Electrical and Computer Engineering, the University of Texas at Austin, where he is currently an Associate Professor. He is a recipient of the 2009 National Science Foundation Career Award. His research is in signal processing, bioinformatics, machine learning, and communication systems.
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Erik Winfree, California Institute of Technology
Designing and verifying molecular circuits made of DNA
Abstract:Inspired by the information processing core of biological organisms and its ability to fabricate intricate machinery from the molecular scale up to the macroscopic scale, research in synthetic biology, molecular programming, and nucleic acid nanotechnology aims to create information-based chemical systems that carry out human-defined molecular programs that input, output, and manipulate molecules and molecular structures. For chemistry to become the next information technology substrate, we will need improved tools for designing, simulating, and analyzing complex molecular circuits and systems. Using dynamic DNA nanotechnology as a model system, I will discuss how programming languages can be devised for specifying molecular systems at a high level, how compilers can translate such specifications into concrete molecular implementations, how both high-level and low-level specifications can be simulated and verified, and how these techniques can be used to design, implement, and understand nucleic-acid circuits that exhibit specified non-equilibrium dynamics.
Biography: Erik Winfree is Professor of Computer Science, Computation & Neural Systems and Bioengineering at Caltech. He is the founder of two NSF “Expeditions in Computing”, the Molecular Programming Project (2008-2013) and Molecular Programming Architectures, Abstractions, Algorithms, and Applications (2013-2018). Winfree is the recipient of the Feynman Prize for Nanotechnology (2006), the NSF PECASE/CAREER Award (2001), the ONR Young Investigators Award (2001), a MacArthur Fellowship (2000), the Tulip prize in DNA Computing (2000), and MIT Technology Review’s first TR100 list of “top young innovators” award (1999). Prior to joining the faculty at Caltech in 1999, Winfree was a Lewis Thomas Postdoctoral Fellow in Molecular Biology at Princeton, and a Visiting Scientist at the MIT AI Lab. Winfree received a B.S. in Mathematics and Computer Science from the University of Chicago in 1991, and a Ph.D. in Computation & Neural Systems from Caltech in 1998.
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