1. Tran Xuan Tu 
Title: Ultra-Low-Power Internet-of-Things Research at VNU Hanoi
Abstract: The Internet of Things (IoT) has emerged recently with vast applications in our daily lives. IoT applications are expected to be constrained, low-cost, smart devices running on battery-based or battery-free systems using power harvesters for easy deployment. They also raise security and privacy concerns due to their collection of our personal data. Therefore, emerging IoT applications require ultra-low-power consumption with security and privacy designs in mind. This talk reviews the requirements for emerging IoT applications. After that, a review of our current works on ultra-low-power IoT accelerators for artificial intelligence, security and privacy.
Bio: Xuan-Tu Tran is a full professor in Electronics and Computer Engineering at Vietnam National University, Hanoi (VNU) and the Director of the VNU Information Technology Institute. He received a Ph.D. degree in 2008 from Grenoble INP (CEA-LETI, Minatec), France. He was an invited professor at the University Paris-Sud 11 (2009, 2010), Grenoble INP (2011), UEC Tokyo (2019), adjunct professor at UTS (2017-2023). He was the Director of VNU-Key Laboratory for Smart Integrated Systems (2011-2020) and Co-Director of JTIRC (2017-2020). He has published 3 books, 4 patents and more than 120 scientific papers. His research interests include SoC, NoC, HW architectures for multimedia applications, cryptography, and Artificial Intelligence. He is a Senior Member of the IEEE, IEICE, REV, and VAIP. He won the "Vietnamese Talents Award" in 2015 and the VNU Scientific Award (2016).

2. Huynh Thi Thanh Binh
Title: Evolutionary Multitasking: Recent Advances and Applications
Abstract: Evolutionary multitasking optimization is a cutting-edge topic in the field of computational intelligence that merges evolutionary computation and multitasking methodologies to address multiple optimization problems concurrently, while exploiting the similarities between problems to improve the search performance of algorithms. By leveraging the interactions and dependencies between problems, evolutionary multitasking allows for the sharing and transfer of valuable information between tasks, thereby enhancing the overall search performance. This presentation will provide an overview of the fundamental concepts, principles, and recent advancements of evolutionary multitasking optimization. Additionally, the talk will highlight the practical significance of evolutionary multitasking algorithms and demonstrate how these techniques can effectively tackle complex real-world optimization problems. Through these problems, the talk will delve into algorithm design issues such as solution encoding and the knowledge transfer mechanism in the multitasking environment. Performance evaluations on benchmark datasets will also be presented to demonstrate the effectiveness of evolutionary multitasking algorithm.
Bio
Huynh Thi Thanh Binh is an Associate professor and Vice dean of the School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST) where she is head of Optimization group. She is Vice Chair of the Computer Science Scientific Council, The National Foundation for Science and Technology Development – NAFOSTED (2023-2024). Her current research interests include artificial intelligence, algorithms and optimization, computational intelligence, evolutionary multitasking. She is associate editor of the Engineering Applications of Artificial Intelligence (EAAU), the IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI. In the last 5 years, Dr. Binh and her research team significantly devoted to the fields of multi-task optimization and multi-task evolutionary computing. Her team won the first prize at competition on evolutionary multi-task optimization, multi-task single-objective optimization (MTSOO), 2021 IEEE Congress on evolutionary computation and 2018, 2022 IEEE World Congress on computational intelligence. She is Vice Chair of IEEE Vietnam section and contributes for IEEE Asia Pacific and Vietnam section activities.

3. Than Quang Khoat
Presentation title: On Inference Stability for Diffusion Models
Abstract: Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between timesteps, leading to unstable inference and hence limiting the model’s performance in generating images effectively. This talk will discuss the how each timestep can have significant effect in a DPM, and contribute to the overall loss. Ignoring this may lead to suboptimal training phase. Then some ideas to improve training that not only stabilize the trajectory of a sampling process, accelerate training, and improve synthesized image quality.

4. Phan Duong Hieu
Title: Cryptography/Privacy/Cybersecurity: A Perspective
Abstract:In this short talk, I will discuss the main research directions in these domains and how the programs at IPP/France can help you to become a researcher in these fields, either as an academic faculty member or an R&D engineer.

5. Nguyen Duc Minh
Title: Leveraging OpenAI for the Design of a Spike Neuron Network Accelerator in Verilog: Integration and Efficacy in a open framework RISC-V-Based System-on-Chip
Abstract: This talk presents an approach to designing a spike neuron network (SNN) accelerator using OpenAI tools, integrated within a RISC-V-based open framework system-on-chip (SoC). The design exploits the inherent parallelism and computational efficiency of SNNs, aiming to address the challenges of real-time, low-power processing in edge computing devices. Our methodology employs OpenAI's advanced algorithms to optimize the Verilog implementation of the SNN, ensuring seamless integration with the RISC-V architecture. This integration allows the accelerator to be directly controlled and utilized by the embedded software to implement a spike neuron network capable of recognizing images from the MNIST dataset with an impressive accuracy of 94%.
Experimental results demonstrate that our SNN accelerator achieves a 40-fold reduction in power consumption compared to traditional neural network implementations.
During the talk, we will delve into the design process, from the initial conception using OpenAI tools to the detailed implementation in Verilog, and the challenges encountered in integrating the accelerator into the RISC-V-based SoC.
Our works suggest that the use of AI in designing hardware accelerators, particularly those based on spike neuron networks, can lead to significant advancements in computational efficiency and power consumption. The successful integration of such accelerators into standard computing frameworks, like the RISC-V SoC, demonstrates the feasibility and benefits of this approach, marking a step forward in the evolution of AI-enabled hardware.
Bio:
Nguyen Duc Minh
Assoc. Professor, Hanoi University of Science and Technology
Assoc. Prof. Nguyen Duc Minh is currently a lecturer at the School of Electrical and Electronics Engineering, Hanoi University of Science and Technology. Mr. Minh has a master's and doctorate in the field of Digital System Design and Verification. Currently, Mr. Minh teaches in the field of digital system design, digital ICs, and systems-on-chip. His research interest lies in non-von Neumann computing systems for lightweight real-time AI applications.

6. Dang The Ngoc
Presentation Title: Quantum Entanglement: From Theory to Technology
Abstract: This presentation delves into the fundamental concepts and historical development of quantum mechanics, with a particular focus on quantum entanglement. It explores the Einstein-Podolsky-Rosen (EPR) paradox, Bell's Inequality, and their implications for the Copenhagen interpretation of quantum physics. The presentation also highlights key experiments that confirmed the violation of Bell's Inequality, culminating in the 2022 Nobel Prize in Physics. Additionally, it discusses practical applications of quantum entanglement in fields such as quantum teleportation and quantum key distribution, as well as our research on this topic.
Presenter: Dr. Vu Quang Minh (PTIT, Vietnam)

7. Nicolas Fabre
Title : Identifying usefulness quantum computing ressources

Abstract:
Quantum information can be encoded using either discrete or continuous variables, and identifying useful quantum states and gates within these encodings is key to advancing universal quantum computation. Quantum states can be represented in various ways, such as through a density matrix or phase space distribution, with the Wigner distribution being a prominent example. The positiveness of the Wigner distribution serves as a valuable indicator of whether a quantum state can be simulated classically.

In this talk, we present a comprehensive framework that uses phase-space techniques to accurately identify quantum resources in encoded quantum computations. For any given quantum code, our construction provides a Wigner function that captures how the symmetries of the code space manifest in physical transformations. This function effectively describes the logical content of any physical state, both inside and outside the code space.

We begin by introducing the Wigner distribution on a double-cylinder phase space, constructed from two pairs of azimuthal-angular coordinates. This representation discretizes position and momentum variables into lattices and defines modular variables, making it particularly well-suited for quantum systems with discrete symmetries, such as physical Gottesman-Kitaev-Preskill (GKP) states. By reducing redundancy in the conventional planar phase space, this approach provides a more efficient description of such quantum systems.

Then, we describe how to build the Wigner distribution into the continuous torus, demonstrating how it correctly identifies classically simulable states and highlights the vacuum as a genuine quantum resource.

8. Michèle Wigger
Title: Information-Theoretic Limits of Integrated Sensing and Communication
Abstract:Integrated sensing and communication (ISAC) systems are expected to be important building blocks of future cellular standards as they merge radar and communication into single systems with reduced hardware costs and bandwidth requirements. In this talk we present recent information-theoretic results that characterize the fundamental performance limits of such ISAC systems, and in particular we draw connections to Shannon's famous channel and source-coding theorems, as well as to the theory of optimal detection. We conclude with a discussion of significant information-theoretic ISAC problems that remain open.

9. Marceau Coupechoux
Title: Energy consumption and greenhouse gas emissions of mobile networks
Abstract:Mobile networks have a significant share in global energy consumption and therefore in greenhouse gas emissions. The functions and operations carried out in these networks contribute in various ways to these emissions, whether during the use phase of the networks or during the manufacturing of the equipment. The main goal of this research project is to evaluate and then optimize the contributions of the operations involved in the transmission and reception of information, both during the use phase and the manufacturing phase. We will be particularly interested in 4G and 5G cellular networks. This project is at the heart of the issue of sustainable digital technology.

10. Nguyen Thanh Binh
Talk: The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation

Abstract: Various data analysis techniques and procedures (correlation heatmap, linear discriminant analysis, quadratic discriminant analysis) rely on estimating the covariance matrix or its inverse (the precision matrix). However, missing data can pose significant challenges to this parameter estimation problem. When missing data is presented, imputation is a common way to circumvent the issue as it renders the data complete. Nevertheless, it is imperative to scrutinize the potential trade-offs when opting for imputation as opposed to task-specific methods for handling missing data, especially in the context of subsequent data analysis and inference. In this study, we undertake both empirical and theoretical investigations to assess the impact of imputation in contrast to direct parameter estimation approaches. We focus on the task of estimating the covariance matrix and precision matrix and present an analysis of the error induced by estimating the precision matrix by the inverse of an estimated covariance matrix. Additionally, we propose a sufficient condition that ensures improved performance guarantees for precision matrix estimation based on covariance matrix estimation. The experimental results show that when the number of features is small, direct parameter estimations can be recommended to estimate the precision matrix by inverting the corresponding estimated covariance matrix. However, when the number of features is not small, then inverting the covariance matrix of imputed data gives better results.

11. Talel Abdessalem, IPP, France
Bio: Talel Abdessalem is a Full Professor at Télécom Paris, Institut Polytechnique de Paris, Director of the LTCI Research Center and Dean of Research at Télécom Paris. He is also Deputy VP Research at IP Paris. Before taking the responsibility of the LTCI, he held the Big Data and Market Insights Chair at Télécom Paris, led several research actions funded by the French National Research Agency, and participated to several national and European research projects. His main research achievements are on version control (DBPL'97, DocEng'13), change detection and querying of XML documents (BDA'02, CIKM’08), information extraction from the structured web (VLDB’10, ICDE’12), social networks analysis (DBSocial’11, WWW’11, CIKM’12, RSWeb’13), graph data (CJ’18, EDBT’19, CIKM’19, FGCS’20, FI’21), recommender systems (RecSys'13, RecSys'15, RecSys'18, ACM CSUR’21) and predictive analytics (JMLR’18, IJCNN’20, JMLR’22).
He supervised and co-supervised fourteen Ph.D. students. He received a Ph.D. in Computer Science from Paris Dauphine-PSL University and a Habilitation Degree (HDR) from UPMC-Sorbonne University.

12. Stéphane Bressan
Bio: Stéphane Bressan is an Associate Professor in the Department of Computer Science of the School of Computing (SoC) of the National University of Singapore (NUS). Stéphane is a Track Leader for Maritime Information Technologies at NUS Centre for Maritime Studies (CMS), an Affiliate Professor at NUS Business Analytics Centre and a Faculty Affiliate at NUS Institute of Data Science. He is also a member of the International Research Laboratory on Artificial Intelligence (IPAL) (Singapore-France CNRS IRL 29255).
In 1990, Stéphane joined the European Computer-industry Research Centre (ECRC) of Bull, ICL, and Siemens in Munich (Germany). In 1994, he was appointed site Leader of the Database Platform project and Principal Investigator and Work-package manager for the European IDEA ESPRIT project on Intelligent Databases. From 1996 to 1998, he was Research Associate at the Sloan School of Management of the Massachusetts Institute of Technology (MIT) (United States of America).
Stéphane's research interest is the integration, management, and analysis of data from heterogeneous, disparate, and distributed sources. Stéphane has developed expertise in data- and physics-driven modelling, simulation, and optimisation with data mining and machine learning algorithms.

13. Nguyen Quoc Hung
Bio: Dr. Hung Q. Nguyen (hungngq@hus.edu.vn) is an experimental physicist who focuses on quantum physics at the nanoscale. After a Bachelor at Vietnam National University Hanoi in condensed matter physics, Dr. Hung studied quantum phase transition between superconductor and insulator at Brown University for his PhD. In 2010, he started his post-doc at Neel Institute, Grenoble, in collaboration with Prof. J. Pekola at Aalto University. Here, he created the most powerful microcooler using superconducting tunnel junctions that reach 30 mK at 1 nW cooling power. His journey in Europe concluded with a 2 years contract at the University of Copenhagen, focusing on measuring topological qubits from a semiconductor-superconductor hybrid nanowire that could host Majorana quasiparticles. In Vietnam, Dr. Hung led research in micro/nano devices at Nano and Energy Center, Hanoi University of Science, VNU. His group fabricates microcoolers that work at room temperature using a thermoelectric effect from telluride-based materials. He is also an active researcher in quantum computing, where he pioneers the use of NISQ computers in demonstrating fundamental phenomena of quantum physics.

14. Le Hong Phuong
Bio: Phuong Le-Hong is an Associate Professor in the Department of Mathematics, Mechanics and Informatics (MIM), VNU University of Science, Hanoi. He has been the director of the Data Science Laboratory at MIM since 2019. He is also a senior research scientist at FPT Smart Cloud, a member company of FPT Corporation where he leads the natural language processing team of the AI division. He obtained a PhD in computer science at Lorraine Université (France) in 2010. He was a teaching and research assistant at the Institut National Polytechnique de Lorraine (INPL, France) in 2011. His research interests include computational linguistics and big data analytics. His personal home page is at http://mim.hus.vnu.edu.vn:8080/lhp/ .

15. Nguyen Van Tam
Bio: Van-Tam Nguyen, received the Diplôme d’Ingenieur from CentraleSupelec, the M.Sc. degree in automatic and signal processing from University Paris-Saclay, the graduation degree in image processing from EPFL, Switzerland, in 2000, the Ph.D. degree in communications and electronic from Telecom Paris in 2004, and the H.D.R. degree from University Sorbonne in 2016. He has been at Telecom Paris since 2005, where he is currently Full Professor and Head of computer science and network department (INFRES) and communication and electronic department (COMELEC). He is also Director of the ICMS (Intelligent Cybersecurity for Mobility Systems) Chair, Deputy Director of the LTCI laboratory and member of Research Committee of IP Paris. He was a NICT’s Rank A Guest Researcher, Japan (2012-2013). He has held visiting positions at UC Berkeley (2013-2016) and Stanford (2016-2017). He was director of Intek Institute (2018) and CEO of TAM-AI Co. Ltd (2019-2021). His research interests includes embedded and distributed AI, AIoT, AI for cybersecurity and AI for cognitive sciences. He is the author or co-author of over 100 publications., five patents and one technology transfer. He received a Senior Marie Curie Fellowship from the European Commission in 2015