Welcome back! We’re thrilled to announce the Fall 2020 version of the One World Signal Processing Seminars. The seminars of this season will feature a number of leading scholars based on North America. Our theme will be Machine Learning for Signal Processing. Talks will cover various important cutting-edge topics in Machine learning, signal processing and data analytics. Outstanding researchers from different areas will introduce state-of-art algorithms, discuss analysis of fundamental limits, and showcase experiments in real-world applications.

Talks will be arranged in a virtual format (via Zoom) from mid-October to mid-December.


  • (22/09/2020) We’re thrilled to announce the Fall 2020 edition of the One World Signal Processing Seminars. Please also welcome the new organizers: Xiao Fu (OSU), Mingyi Hong (UMN), Yanning Shen (UC Irvine). Our first talk will be given by Prof. Georgios B. Giannakis (UMN) on Oct 22. Sign up below and stay tuned for more details to be announced.

Next Talk

  • Speaker: Georgios B. Giannakis (UMN)

  • Title: Adaptive Diffusions for Scalable and Robust Learning over Graphs

  • Date/Time: October 22, 2020, 6:00pm PT (check your local time here)

  • Abstract: Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heatkernel, enjoy remarkable classification accuracy at modest computational requirements. Theirperformance however depends on the extent to which the chosen diffusion captures a typicallyunknown label propagation mechanism that can be specific to the underlying graph, andpotentially different for each class. This talk will introduce a disciplined, data-efficient approachto learning class-specific diffusion functions adapted to the underlying network topology. Thenovel learning approach leverages the notion of “landing probabilities” of class-specific randomwalks, which can be computed efficiently, thereby ensuring scalability to large graphs.Furthermore, a robust version of the classifier becomes available for graph-aware learning evenin noisy environments. Classification tests on real networks will demonstrate that adapting thediffusion function to the given graph and observed labels, markedly improves the performanceover fixed diffusions; reaching – and many times surpassing – the classification accuracy ofcomputationally heavier state-of-the-art competing methods, that rely on node embeddings anddeep neural networks.

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Upcoming Talks (all date/time are in Pacific Time)

Click on the [+] button below to check on details about the talk. Click here if you would like to import the seminar information to iCal/Google Calendar.

  • Title: Nonparametric Multivariate Density Estimation: A Low-Rank Characteristic Function Approach

  • Abstract: Effective non-parametric density estimation is a key challenge in high-dimensional multivariate data analysis. In this paper,we propose a novel approach that builds upon tensor factorization tools. Any multivariate density can be represented by its characteristic function, via the Fourier transform. If the sought density is compactly supported, then its characteristic function can be approximated, within controllable error, by a finite tensor of leading Fourier coefficients, whose size de-pends on the smoothness of the underlying density. This tensor can be naturally estimated from observed realizations of the random vector of interest, via sample averaging. In order to circumvent the curse of dimensionality, we introduce a low-rank model of this characteristic tensor, which significantly improves the density estimate especially for high-dimensional data and/or in the sample-starved regime. By virtue of uniqueness of low-rank tensor decomposition, under certain conditions, our method enables learning the true data-generating distribution. We demonstrate the very promising performance of the proposed method using several measured datasets.

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Past Talks

You can find the recorded seminars at the Youtube channel or Bilibili channel. Click on the [+] button below to check on details about the talk.

  • Title: Computational approaches for guiding rational vaccine design: Case studies in HCV, HIV, and COVID-19 (DataSci)

  • Abstract: This talk will describe how computational modelling and high-dimensional statistics can aid the rational design of vaccines. Approaches familiar in signal processing and physics will be introduced and applied to genetic sequence data of viruses measured from infected individuals. These approaches will be used to build computational models that inform how viral function/structure is mediated by correlated sets of genetic mutations, and to simulate viral evolutionary dynamics in individuals who present specific immune responses. When combined with experimental and clinical data, the talk will describe how the models may be used to identify new vaccine candidates for the hepatitis C virus (HCV) and for HIV. Recent progress on the use of sequence analysis to guide vaccine design for COVID-19 will also be discussed.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Tencent), Lecture Slides

  • Title: Modeling and learning social influence from opinion dynamics under attack (DistSP-Opt)

  • Abstract: Opinion dynamics models aim at capturing the phenomenon of social learning through public discourse. While a functioning society should converge towards common answers, the reality often is characterized by divisions and polarization. This talk reviews the key models that capture social learning and its vulnerabilities. In particular, we review models that explain the effect of bounded confidence and social pressure from zealots (i.e. fake new sources) and show how very simple models can explain the trends observed when social learning is subject to these phenomena. Their influence exposes trust different agents place on each other and introduce new learning algorithms that can estimate how agents influence each other.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Cell Detection by Functional Inverse Diffusion and Nonnegative Group Sparsity (DataSci)

  • Abstract: On August 28, 2018, a Stockholm-based biotech company launched a new product: The Mabtech IRIS, a next-generation FluoroSpot and ELISpot reader. The reader is a machine designed to analyze a type of biomedical image-based assays that are commonly used in immunology to study cell responses. A contemporary use case involve the development of vaccines for SARS-CoV-2. At the heart of this machine is a positivity constrained groups sparsity regularized least squares optimization problem, solved with large scale optimization methods.

    The presentation will outline the problem of analyzing FluoroSpot assays from a signal processing and optimization perspective and explain the methods we designed to solve it. The problem essentially amounts to counting, localizing and quantifying heterogeneous diffuse spots in an image. The solution involves components such as the development of a tractable linear model from the physical properties that govern the reaction-diffusion-adsorption-desorption process in the assay; the formulation of an inverse problem in function spaces and its discretized approximation; the role of group sparsity in finding a plausible solution to an otherwise ill-posed problem; and how to efficiently solve the resulting 40 million variable optimization problem on a GPU.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Signal Processing and Optimization in UAV Communication and Trajectory Design (ML-Com)

  • Abstract: Unmanned aerial vehicles (UAVs) or drones have found numerous applications in wireless communication, as either aerial user terminal or mobile access point (AP). Compared to conventional terrestrial wireless systems, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement, bringing several crucial issues such as how to exploit line-of-sight (LoS) dominant UAV-ground channels while mitigating resulted strong interference, meet distinct UAV communication requirements on critical control messages versus high-rate payload data, cater for the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as leveraging the new degree of freedom via controlling the UAV trajectory for communication performance enhancement. In this talk, we will provide an overview of the above challenges and practical issues in UAV communications, their state-of-the-art solutions (with an emphasis on promising signal processing and optimization techniques used in them), as well as important directions for future research.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Inferring Networks and Network Properties from Graph Dynamic Processes (DataSci)

  • Abstract: We address the problem of identifying structural features of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. Our approach leverages concepts from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end, we develop efficient inference algorithms stemming from provably tight convex relaxations of natural non-convex criteria. For scenarios where the number of samples is insufficient for exact graph recovery, we show that coarser graph features (such as communities or centrality values) can still be correctly inferred.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: A programmable wireless world with reconfigurable intelligent surfaces (ML-Com)

  • Abstract: Wireless connectivity is becoming as essential as electricity in our modern world. Although we would like to deliver wireless services everywhere, the underlying physics makes it hard: the signal power is vanishing very quickly with the propagation distance and is absorbed or scattered off various objects. Even when we have a “strong" signal, only one in a million parts is being received, thus, there is a large room for improvements! What if we could tune the propagation environment to our needs? This is the main goal of reconfigurable intelligent surfaces, which is a beyond-5G concept currently hyped by the communication research community. The idea is to support the transmission from a source to a destination by deploying specially designed surfaces that can reconfigure how incident signal waves are reflected. Ideally, the surface will take the signal energy that reaches it and retransmit it focused on the receiver. This opens a new design dimension: we can not only optimize the transmitter and receiver but also control the channel by real-time programming. In this talk, I will explain the fundamentals of this new technology by building up a basic system model and demonstrate its properties. I will then discuss the prospects of the technology, including potential use cases, the main signal processing issues, and debunk three myths that are currently spreading among researchers.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Machine Learning for Massive MIMO Communications (ML-Com)

  • Abstract: This talk provides two examples in which machine learning can significantly improve the design of wireless communication systems. In the first part of the talk, we show that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system in which a base-station (BS) serves multiple mobile users, each with a rate-limited feedback link to the BS. The key observation here is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem -- in contrast to the conventional approach where the channel state information (CSI) is independently quantized at each user. We show that a DNN architecture implementing distributed source coding -- mapping the received pilots directly into finite feedback bits at the user side, then mapping the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. In the second part of the talk, we propose an autoencoder-based symbol-level precoding scheme for a time-division duplex (TDD) massive MIMO system with 1-bit digital-to-analog converters. The goal here is to design downlink transmission schemes which are robust to imperfect CSI. Toward this end, we leverage the concept of autoencoder wherein the end-to-end system is modeled by a DNN, and the constellation and the precoding scheme can be jointly designed so that the overall system is robust to channel uncertainty.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Learning Higher-Order Interactions with Graph Volterra Models (DataSci)

  • Abstract: Complex network processes are known to be driven not only by pairwise interactions but also by the interactions of small groups of tightly connected nodes, sometimes called higher-order interactions. So, identifying these higher-order interactions becomes paramount to gain insight in the nature of such processes. While predicting pairwise nodal interactions (links) from network data is a well-studied problem, the identification of higher-order interactions (high-order links) has not been fully understood. In this talk, we review several approaches that have been proposed for addressing this task and examine their respective limitations. Furthermore, cross-fertilizing ideas from Volterra series and linear structural equation models, we introduce a principled method that can capture higher-order interactions among nodes, the so-called graph Volterra model. The proposed approach can identify higher-order interactions among nodes by the respective graph Volterra kernels. To motivate the adoption of our new model, we demonstrate its performance for higher-order link prediction using real data from social networks and smart grids.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Communication efficient distributed learning (DistSP-Opt)

  • Abstract: Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this topic is that recent progresses have been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This talk will provide a brief introduction of some distributed learning techniques that have recently been developed, namely lossy communication compression (e.g., quantization and sparsification), asynchronous communication, and decentralized communication. The goal of this presentation is to let the general audience understand the principle of communication efficient distributed learning algorithms and systems.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (bilibili), Lecture Slides

  • Title: Learning to team play (ML-Com)

  • Abstract: Cooperation is an essential function in a wide array of network scenarios, including wireless, robotics, and beyond. In decentralized networks, cooperation (or team play) must be achieved by agents despite the lack of common information regarding the global state of the network. Cooperation in the presence of state information uncertainties is a highly challenging problem for which no simple optimization based robust solution exist, in most cases. In this talk, we describe a machine learning approach this problem. We introdue so called Team Deep Learning Networks (Team-DNN) where agents learn to coordinate with each other under uncertainties. We apply it to wireless optimization problems and emphasize power control as a possible use case. We show how devices can learn how to message each other relevant information and take appropriate transmission decisions. Finally, team DNNs are extended to include the principle of mixture of experts (MoE) that enable the team DNNs to divide the problem space into different regions of state uncertainties and get optimized behavior in each one.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

  • Title: Decentralized stochastic non-convex optimization (Dist-SP)

  • Abstract: In many emerging applications, it is of paramount interest to learn hidden parameters from the data collected at individual units. For example, self-driving cars may use onboard cameras to identify pedestrians, highway lanes, or traffic signs in various light and weather conditions. Problems such as these can be framed as classification, regression, or risk minimization, at the heart of which lies stochastic optimization. When the underlying datasets are large and further contain private information, it is not typically feasible to collect and process the entire data at a central location to solve the corresponding optimization problems. Decentralized methods thus are preferable as they benefit from local (short-range) communication and are able to tackle data imperfections both in space (geographical diversity) and in time (noise in measurements). In this talk, I will present our recent work that develops a novel algorithmic framework to address various aspects of decentralized stochastic optimization for strongly convex and non-convex problems in both online and batch data scenarios. I will quantify the performance of the underlying algorithms and describe the regimes of practical interest where the convergence rates are near-optimal. Moreover, I will characterize certain desirable attributes of such methods in the context of linear speedup and network-independent convergence rates. I will conclude by demonstrating the key aspects of the proposed methods with the help of numerical experiments.

  • Speaker Homepage

  • Video Link (Youtube), Video Link (Bilibili), Lecture Slides

How it works / Subscribe

Each talk will last 45 minutes, followed by a 10-15 minutes Q&A. All talks will be recorded and uploaded to Youtube for later views (subjected to the speaker's consent). We will announce the next speaker on this website. You will receive the access information (link of the Zoom-room and the password) the day (or approx. 12 hours) before each talk through subscribing to a mailing list.

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Other One World Seminar Series

Organizers and Contact

The virtual seminar series of this season is organized by Xiao Fu (OSU), Mingyi Hong (UMN), Yanning Shen (UC Irvine), with the advisory board member Wing-kin (Ken) Ma (CUHK-HK), Hoi-to Wai (CUHK-HK), Tsung-hui Chang (CUHK-SZ). It is also supported partly by IEEE Signal Processing Society. For inquiries, please write to this address.