NCC 2022 will have six plenary talks, along with several invited talks.
Title: Sparsity-aware Bayesian Inference and its Applications
Abstract: The emergence of compressive sensing and the associated l1 recovery algorithms and theory have generated considerable interest in their applications. This talk will present a complementary set of tools based on a Bayesian framework to address the general problem of sparse signal recovery, and discuss the challenges associated with it. Bayesian methods offer superior performance compared to convex optimization-based methods and are parameter tuning-free. They also have the flexibility necessary to deal with a diverse range of measurement modalities and structured sparsity in signals than hitherto possible. Parsimonious signal representation using overcomplete dictionaries for compression, estimation of sparse communication channels with large delay spreads, low dimensional representation of MIMO wireless channels, brain imaging techniques such as MEG and EEG, are a few examples. Further, we show that, by re-interpreting the Bayesian cost function as a technique to perform covariance matching, one can develop new, ultra-fast Bayesian algorithms for sparse signal recovery. As an example application, we discuss the utility of these algorithms in the context of 5G communications with several case studies including wideband time-varying channel estimation, dictionary learning, and, time-permitting, low-resolution analog-to-digital conversion-based signal recovery.
Bio: Chandra R. Murthy received the Ph.D. degree in electrical and computer engineering from the University of California at San Diego, San Diego, USA, in 2006. In September 2007, he joined the Department of Electrical Communication Engineering, Indian Institute of Science, Bengaluru, India, where he is currently working as a Professor. He has over 75 journal articles and over 100 conference papers to his credit.
His research interests are in the areas of energy harvesting communications, multiuser MIMO systems, and sparse signal recovery techniques applied to wireless communications. He was an elected member of the IEEE SPCOM Technical Committee from 2014 to 2019. His paper won the Best Paper Award in the Communications Track at the NCC 2014 and a paper co-authored with his students won the Student Best Paper Award at the IEEE ICASSP 2018 and IEEE ISIT 2021. He was an Associate Editor of the IEEE Signal Processing Letters from 2012 to 2016, IEEE Transactions on Communications from 2017 to 2022, and the Sadhana Journal from 2016 to 2018. He is currently serving as an Area Editor for the IEEE Transactions on Signal Processing and as an Associate Editor for the IEEE Transactions on Information Theory.
Title: Algorithmic Fairness in Networks: Amplifications, Bias Trap, Incentives
Abstract: Networks, and especially *communication* networks, always gave rise to self-reflection on equity: Back in the 2000s a queueing theorist reflected that "ensuring some form of fairness is the reason we created queues in the first place" and truly the feasibility of maintaining various concepts of fairness (e.g., TCP Fairness, Proportional, max-min) in a distributed systems is among our field's towering achievements. But networked algorithms have greatly expanded beyond communication into AI, to multiple stages of information processing, recommendations, and decisions made for us. This have fueled an epidemic of moral hazards since the mid-2010s: every month or week brings another concerning examples of algorithms behaving badly.
This talk will attempt at guiding researchers interested in joining the emerging field of algorithmic fairness from a network angle. Three examples from recent research will highlight the importance of modeling, the opportunities for networked algorithms to guide AI out of the bias trap and orchestrate incentives emerging in fairness distributed guarantees.
Bio: Augustin is an Associate Professor of Computer Science at Columbia University since 2010, where he directs the Mobile Social Lab. The goal of his research is to reconcile the benefits of personal data and social networks with a commitment to fairness and privacy. Recently, his results address transparency in personalization, the role of human mobility in privacy across several domains, the efficiency of crowdsourced content curation, the fairness of incentives and algorithms used in social networking. His research lead to 40 papers in tier-1 conferences (five receiving best or best student paper awards at ACM CoNEXT, SIGMETRICS, USENIX IMC, IEEE MASS, Algotel), covered by several media including the NYT blog, The Washington Post, the Economist, or The Guardian. An ex student of the Ecole Normale Supérieure in Paris, he earned a Ph.D in mathematics and computer science in 2006, a NSF CAREER Award in 2013 and the ACM SIGMETRICS Rising star award in 2013. He has been an active member of the network and web research community, serving in the program committees of ACM SIGMETRICS (as chair), EC, FAccT, SIGCOMM, WebConf, NeurIPS, ICML, EAAMO, CoNEXT (as chair), MobiCom, MobiHoc, IMC, WSDM, COSN, AAAI ICWSM, and IEEE Infocom, as area editor for IEEE TMC, ACM SIGCOMM CCR, ACM SIGMOBILE MC2R, and editor in chief for PACM POMACS.
Title: Overcoming data availability attacks in blockchain systems: a graph-coding perspective.
Abstract: Blockchain systems are already gaining popularity in a variety of applications due to their decentralized design that is favorable in many settings. To overcome excessive storage and latency burden, light nodes and side blockchains have been proposed to, respectively, enhance the basic blockchain architecture. However, both light nodes and side chains are vulnerable to data availability (DA) attacks by malicious nodes. We first show that coded data representation offers greater robustness against DA attacks. We then discuss graph codes specifically designed for the target applications in blockchain systems and show that they perform better than previously proposed methods; intriguingly, the new finite-length code optimization framework unveils code properties beyond the established metrics. This line of work opens up new possibilities for applications of channel coding methods in blockchain-based data systems.
Biography: Lara Dolecek is a Full Professor with the Electrical and Computer Engineering Department and Mathematics Department (courtesy) at the University of California, Los Angeles (UCLA). She holds a B.S. (with honors), M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences, as well as an M.A. degree in Statistics, all from the University of California, Berkeley. She received the 2007 David J. Sakrison Memorial Prize for the most outstanding doctoral research in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Prior to joining UCLA, she was a postdoctoral researcher with the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. She received IBM Faculty Award (2014), Northrop Grumman Excellence in Teaching Award (2013), Intel Early Career Faculty Award (2013), University of California Faculty Development Award (2013), Okawa Research Grant (2013), NSF CAREER Award (2012), and Hellman Fellowship Award (2011). With her research group and collaborators, she received numerous best paper awards. Her research interests span coding and information theory, graphical models, statistical methods, and algorithms, with applications to emerging systems for data storage and computing. She currently serves as an Associate Editor for IEEE Transactions on Information Theory and as the Secretary of the IEEE Information Theory Society. Prof. Dolecek is 2021-2022 Distinguished Lecturer of the IEEE Information Theory Society. Prof. Dolecek has served as a consultant for a number of companies specializing in data communications and storage.
Title: What is next in signal processing for MIMO communication?
Abstract: In the last 20 years, MIMO wireless communication has gone from concept to commercial deployments in millions of devices. Two flavors of MIMO -- massive and mmWave -- are key components of 5G. In this talk, I will examine aspects of MIMO communication that may influence the next decade of wireless communications. I will start by highlighting, from a signal processing perspective, what was interesting about taking MIMO to higher carrier frequencies at mmWave. Then I will speculate about forthcoming directions for MIMO communication research. I will discuss the implications of going to mmWave about 100 GHz to terahertz frequencies, including implications on the channel assumptions and array architectures. I will make the case that we need to it may be relevant to go back to signals from a circuits perspective, to make physically consistent MIMO models that work with large bandwidths. Finally, I will talk about how other advancements in circuits, antennas, and materials may change the models and assumptions that are used in MIMO signal processing.
Bio: Robert W. Heath Jr. is the Lampe Distinguished Professor in the Department of ECE at North Carolina State University. He is the recipient or co-recipient of several awards including the 2019 IEEE Kiyo Tomiyasu Award, the 2020 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, the 2020 North Carolina State University Innovator of the Year Award and the 2021 IEEE Vehicular Technology Society James Evans Avant Garde Award. He authored "Introduction to Wireless Digital Communication” (Prentice Hall in 2017) and "Digital Wireless Communication: Physical Layer Exploration Lab Using the NI USRP” (National Technology and Science Press in 2012). He co-authored “Millimeter Wave Wireless Communications” (Prentice Hall in 2014) and "Foundations of MIMO Communications" (Cambridge 2019). He is a current member-at-large of the IEEE Communications Society Board-of-Governors (2020-2022) and a past member-at-large on the IEEE Signal Processing Society Board-of-Governors (2016-2018). He was EIC of IEEE Signal Processing Magazine from 2018-2020. He is a licensed Amateur Radio Operator, a registered Professional Engineer in Texas, a Private Pilot, a Fellow of the National Academy of Inventors, and a Fellow of the IEEE.
Title: Optimal Clustering with Bandit Feedback
Abstract: This paper considers the problem of online clustering with bandit feedback. A set of arms (or items) can be partitioned into various groups that are unknown. Within each group, the observations associated to each of the arms follow the same distribution with the same mean vector. At each time step, the agent queries or pulls an arm and obtains an independent observation from the distribution it is associated to. Subsequent pulls depend on previous ones as well as the previously obtained samples. The agent's task is to uncover the underlying partition of the arms with the least number of arm pulls and with a probability of error not exceeding a prescribed constant $\delta$. The problem proposed finds numerous applications from clustering of variants of viruses to online market segmentation. We present an instance-dependent information-theoretic lower bound on the expected sample complexity for this task, and design a computationally efficient and asymptotically optimal algorithm, namely Bandit Online Clustering (BOC). The algorithm includes a novel stopping rule for adaptive sequential testing that circumvents the need to exactly solve any NP-hard weighted clustering problem as its subroutines. We show through extensive simulations on synthetic and real-world datasets that BOC's performance matches the lower bound asymptotically, and significantly outperforms a non-adaptive baseline algorithm.
Biography: Vincent Y. F. Tan (S'07-M'11-SM'15) was born in Singapore in 1981. He received the B.A. and M.Eng. degrees in electrical and information science from Cambridge University in 2005, and the Ph.D. degree in electrical engineering and computer science (EECS) from the Massachusetts Institute of Technology (MIT) in 2011. He is currently a Dean’s Chair Associate Professor with the Department of Electrical and Computer Engineering and the Department of Mathematics, National University of Singapore (NUS). His research interests include information theory, machine learning, and statistical signal processing.
Dr. Tan is an elected member of the IEEE Information Theory Society Board of Governors. He was an IEEE Information Theory Society Distinguished Lecturer from 2018 to 2019. He received the MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize in 2011, the NUS Young Investigator Award in 2014, the Singapore National Research Foundation (NRF) Fellowship (Class of 2018), the Engineering Young Researcher Award in 2018, and the NUS Young Researcher Award in 2019. A dedicated educator, he was awarded the Engineering Educator Award in 2020 and 2021 and the (university level) Annual Teaching Excellence Award in 2022. He is currently serving as a Senior Area Editor for the IEEE Transactions on Signal Processing and as an Associate Editor in Machine Learning and Statistics for the IEEE Transactions on Information Theory.
Title: Shift, Scale and Restart Smaller Models to Estimate Larger Ones: Agent-based Simulators in Epidemiology
Abstract: Agent-based simulators are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an evolving pandemic. They provide the flexibility to accurately model a heterogeneous population with time and location varying, person specific interactions. To accurately model detailed behaviour, typically each person is separately modelled. This however, may make computational time prohibitive when the region population is large and when time horizons are long. We observe that simply considering a smaller aggregate model and scaling up the output leads to inaccuracies. In this talk we primarily focus on the COVID-19 pandemic and dig deeper into the underlying probabilistic structure of an associated agent based simulator (ABS) to arrive at modifications that allow smaller models to give accurate statistics for larger models. We exploit the observations that in the initial disease spread phase, the starting infections behave like a branching process. Further, later once enough people have been infected, the infected population closely follows its mean field approximation. We build upon these insights to develop a shifted, scaled and restart version of the simulator that accurately evaluates the ABS's performance using a much smaller model while essentially eliminating the bias that otherwise arises from smaller models
Bio: Sandeep is a senior professor at the School of Technology and Computer Science in Tata Institute of Fundamental Research in Mumbai. His research interests lie in applied probability including in sequential learning, mathematical finance, Monte Carlo methods, and game theoretic analysis of queues. Lately, he has been involved in modelling Covid-19 spread in Mumbai, and in mathematics of agent-based simulation models. He is currently on the editorial board of Stochastic Systems. Earlier he has been on editorial boards of Mathematics of Operations Research, Management Science and ACM TOMACS.
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