Since the coronavirus pandemic lockdown, the entire world has been forced to slow down. The signal processing community has been no exception with cancellations of conferences, seminars and research visits. To keep the spirits on and to foster signal processing researches, the One World Signal Processing Seminar Series has invited expert speakers from three exciting areas of signal processing:

  • (DistSP-Opt) Distributed Signal Processing and Optimization

  • (ML-Com) Signal Processing and Machine Learning for Communication

  • (DataSci) Data Science and Information Processing

Talks will be arranged once a week in a virtual format (via Zoom) from mid June to end of August of 2020.


  • (03/07/2020) The lecture video of Prof. Zhang can now be viewed here (Youtube) or here (Bilibili). You can also find the slides here. We will take a break next week and resume on July 17, and our next speaker will be Prof. Santiago Segarra from Rice University.

Next Talk

  • Speaker: Prof. Santiago Segarra (Rice University)

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

  • Date/Time: July 17, 2020, 10:00am (GMT+8).

  • 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.

  • Meeting Link: (please subscribe to our mailing list)

Upcoming Talks (all date/time are in GMT+8)

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: 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

  • 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

  • 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


Past Talks

You can find the recorded seminars at the Youtube 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

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

This virtual seminar series is organized by Wing-kin (Ken) Ma (CUHK-HK), Hoi-to Wai (CUHK-HK), Tsung-hui Chang (CUHK-SZ). For inquiries, please write to this address. It is also supported partly by IEEE Signal Processing Society, Region 10 (Asia Pacific). Particularly, some of the seminars are joint seminars with IEEE Signal Processing Society Distinguished Lectures.