Abstract: Following the immense recent advances in distributed networks, the explosive growth of the Internet, and our increased dependency on these infrastructures, guaranteeing the uninterrupted operation of communication networks has become a major objective in network algorithms. The modern instantiations of distributed networks, such as the Bitcoin network and cloud computing, introduce in addition, new security challenges that deserve urgent attention in both theory and practice. In this talk, I will present a recent framework for obtaining fast, resilient and secure distributed algorithms for fundamental graph problems against several adversarial settings. One of our key results provides the first sublinear broadcast algorithms in the congest model against adversarial edges. We will also provide general compilation schemes that are based on exploiting the high- connectivity of the graph. Finally, I will highlight major open problems in this area.

Bio: Merav Parter is a faculty member in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science. Before joining Weizmann, she was a Fulbright and Rothschild Postdoctoral Researcher in the EECS department of MIT. In the past, she was a Google European Fellow in Distributed Computing, 2012. Her research interests include reliable distributed communication, graph theory, and neural networks. Parter's research is supported (in part) by the European Research Council (starting grant DistRES, 2020-2025), the Israeli Science Foundation (ISF) and the NSF-BSF organization. She is the recipient of the Krill prize for Excellence in Scientific Research of 2021.

Abstract: Artificial Intelligence (AI) is a rapidly advancing field. Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go world-champion level play, to self- driving cars. These ever-expanding AI capabilities open up new exciting avenues for advances in new domains. I will discuss our AI research for advancing scientific discovery for a sustainable future. In particular, I will talk about our research in a new interdisciplinary field, Computational Sustainability, which has the overarching goal of developing computational models and methods to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from biodiversity and wildlife conservation, to multi-criteria strategic planning of hydropower dams in the Amazon basin and materials discovery for renewable energy materials. This work was featured in the Communications of ACM, in a cover article entitled Computational sustainability: computing for a better world and a sustainable future. I will also talk about our work on AI for accelerating the discovery for new solar fuels materials, which has been featured in Nature Machine Intelligence, in a cover article entitled, Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. In this work, we propose an approach called Deep Reasoning Networks (DRNets), which requires only modest amounts of (unlabeled) data, in sharp contrast to standard deep learning approaches. DRNets reach super-human performance for crystal-structure phase mapping, a core, long-standing challenge in materials science, enabling the discovery of solar-fuels materials. DRNets provide a general framework for integrating deep learning and reasoning for tackling challenging problems. For an intuitive demonstration of  our approach, using a simpler domain, we also solve variants of the Sudoku problem. The article DRNets can solve Sudoku, speed scientific discovery, provides a perspective for a general audience about DRNets. Finally, I will highlight cross-cutting computational themes and challenges for AI at the intersection of constraint reasoning and deep learning.

Bio: Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science and the director of the Institute for Computational Sustainability at Cornell University. Gomes received a Ph.D. in computer science in the area of artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and more generally in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key challenges concerning environmental, economic, and societal issues in order to help put us on a path towards a sustainable future. Gomes is the lead PI of an NSF Expeditions in Computing award Gomes has (co-)authored over 150 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including five best paper awards. Gomes is the recipient of the Association for the Advancement of Artificial Intelligence (AAAI) Feigenbaum Prize (2021) for “high-impact contributions to the field of artificial intelligence, through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability, with impactful applications in ecology, species conservation, environmental sustainability, and materials discovery for energy.” Gomes is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of the American Association for the Advancement of Science (AAAS).

Abstract: Consensus protocols have traditionally been studied in a setting where all participants are known to each other from the start of the protocol execution. In the parlance of the blockchain literature, this is referred to as the permissioned setting. Open blockchains like Bitcoin and Ethereum operate in a permissionless setting, i.e. they establish consensus over an unknown network of participants that anybody can join, with as many identities as they like in any role. The arrival of this new form of consensus protocol brings with it many questions. Beyond specific protocols like Bitcoin, what can we prove about permissionless protocols in a general sense? How does recent work on permissionless protocols in the blockchain literature relate to the well- developed history of research on permissioned protocols in distributed computing? To answer these questions, we describe a formal framework for the analysis of both permissioned and permissionless systems. Our framework allows for "apples-to-apples" comparisons between different categories of protocols and, in turn, the development of theory to formally discuss their relative merits. A major benefit of the framework is that it facilitates the application of a rich history of proofs and techniques in distributed computing to problems in blockchain and the study of permissionless systems. Within our framework, we then address the questions above. Our results include formal separations between the permissioned and permissionless settings, and in the latter, between proof-of-work and proof-of-stake protocols. Joint work with Andrew Lewis-Pye.

Bio: Tim Roughgarden is a Professor of Computer Science at Columbia University. Prior to joining Columbia, he spent 15 years on the computer science faculty at Stanford, following a PhD at Cornell and a postdoc at UC Berkeley. His research interests include the many connections between computer science and economics, as well as the design, analysis, applications, and limitations of algorithms. For his research, he has been awarded the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Kalai Prize in Computer Science and Game Theory, the Social Choice and Welfare Prize, the Mathematical Programming Society's Tucker Prize, and the EATCS-SIGACT Gödel Prize. He was an invited speaker at the 2006 International Congress of Mathematicians, the Shapley Lecturer at the 2008 World Congress of the Game Theory Society, and a Guggenheim Fellow in 2017. He has written or edited ten books and monographs, including Twenty Lectures on Algorithmic Game Theory (2016), Beyond the Worst-Case Analysis of Algorithms (2020), and the Algorithms Illuminated book series (2017-2020).

Abstract: To support real-time & sustainable machine intelligence that exploits the rapid growth in data generated from wearable and IoT-based sensors (e.g., cameras, LIDAR), there is a need to optimize the execution of AI/ML pipelines on such resource-constrained embedded devices. In fact, assuring adequate and ubiquitous availability of power to such devices remains the most serious resource bottleneck and requires advances on both the supply side (increasing the amount of energy harvested within appropriate form factors) and the demand side (new mechanisms for ultra-low power sensing and inferencing). Through this talk, I shall introduce the paradigm of collaborative machine intelligence (CMI), where the sensing and inferencing pipelines on individual wearable and IoT devices collaborate in real-time to tackle this challenge by balancing communication vs. computation overheads. First, I will describe work on advances in battery-less, or low-power, pervasive sensing and communication, enabled by such collaboration. Second, using an exemplar video monitoring application, I will describe how CMI can provide dramatic reductions in energy overheads, improvements in accuracy and increase in edge processing throughput for DNN-based vision tasks. CMI dovetails into the emerging vision of a “Cognitive & Collaborative Edge”, that evolves from its current focus on mere computation offloading to a platform for enabling such collaborative and trusted sense-making.

Bio: Archan Misra is Vice Provost (Research) and Professor of Computer Science at Singapore Management University (SMU). Archan has led a number of multi-million-dollar, flagship research initiatives at SMU, including the LiveLabs research center and SMU’s Center for Applied Smart-Nation Analytics (CASA), that have focused on exploiting pervasive sensing for novel smart-city applications, Over a 20+ year research career spanning both academics and industry (at IBM Research and Bellcore), Archan has published extensively on, and practically deployed, technologies spanning wireless networking, mobile & wearable sensing and urban mobility analytics. His current research interests lie in ultra-low energy execution of machine intelligence algorithms, using wearable and IoT devices, based on multi-modal sensor data. Archan is a current recipient of the prestigious Investigator grant (from Singapore’s National Research Foundation) for sustainable man-machine interaction intelligence. Archan holds a Ph.D. from the University of Maryland at College Park, and chaired the IEEE Computer Society's Technical Committee on Computer Communications (TCCC) from 2005-2007.