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Professor Jialiang Li, from the Department of Statistics and Data Science and the Duke-NUS Graduate Medical School at the National University of Singapore, is a renowned researcher specializing in threshold models, structural equation modeling, personalized and diagnostic medicine, model averaging, data smoothing, statistical machine learning, and survival analysis. An elected member of the International Statistical Institute (ISI) and a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics (IMS), he has received accolades such as Singapore's Research Talent Award. Professor Li has published extensively in leading journals and serves as an editor for esteemed publications like the Annals of Applied Statistics, Biometrics, and the Annual Review of Statistics and Its Application.

Seminar Details:

    • Title: Robust Model Averaging Prediction of Longitudinal Response with Ultrahigh-dimensional Covariate
    • Speaker: Professor Dr. Jialiang Li
    • Time: 02:00 PM, Saturday, December 12nd, 2024.
    • Venue: Room C.41, University of Science, VNU-HCM.
      (227 Nguyen Van Cu Street, Ward 4, District 5, Ho Chi Minh City)

Abstract:

Model averaging is an attractive ensemble technique to construct fast and accurate prediction. Despite of having been widely practiced in cross-sectional data analysis, its application to longitudinal data is rather limited so far. We consider model averaging for longitudinal response when the number of covariates is ultrahigh. To this end, we propose a novel two-stage procedure in which variable screening is first conducted and then followed by model averaging. In both stages, a robust rank-based estimation function is introduced to cope with potential outliers and heavy-tailed error distributions, while the longitudinal correlation is modeled by a modified Cholesky decomposition method and properly incorporated to achieve efficiency. Asymptotic properties of our proposed methods are rigorously established, including screening consistency and convergence of the model averaging predictor, with uncertainties in the screening step and selected model set both taken into account. Extensive simulation studies demonstrate that our method outperforms existing competitors, resulting in significant improvements in screening and prediction performance. Finally, we apply our proposed framework to analyze a human microbiome dataset, showing the capability of our procedure in resolving robust prediction using massive metabolites.

Keywords: Robust statistics; Longitudinal model; high-dimensional model

Registration:

We kindly invite faculty members, PhD candidates, graduate students, and undergraduate students to participate.
Register here!

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  • Invited Speaker: NGUYEN - TRUC - DAO  NGUYEN (Department of Mathematics and Statistics, San Diego State University, San Diego, USA)
  • Date & Time: 12nd December 2024, 2024, 09:00 - 11:00.
  • Title:  Optimization of Controlled Sweeping Processes and Neural Networks
  • Room: F2017, Nguyen Van Cu Campus.
  • Abstract:  This presentation focuses on applying the discrete approximation method to establish necessary optimality conditions in an optimization problem for fully controlled constrained sweeping processes. Additionally, we explore its applications in various practical dynamical models. The first model deals with the dynamics of mobile robots navigating obstacles, while the second pertains to model predictive control utilizing neural networks.
  • Organizer: NGUYEN DANG KHOA, LY KIM HA (Vietnam National University – Ho Chi Minh City, University of Science).

After the seminar, Prof. Nguyen Truc Dao Nguyen also gives an introduction about San Diego State University and some scholarships at SDSU. 

 

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Mechanics Department Seminar - November 2024

The Department of Multiphysics Systems Design Laboratory (MSDL) at Jeonbuk National University (South Korea), led by Associate Professor Dr. Hyungmin Jun, will hold a professional seminar.

Associate Professor Dr. Hyungmin Jun is currently a faculty member at Jeonbuk National University (South Korea) and serves as the Principal Investigator of the Multiphysics Systems Design Laboratory. He earned his Ph.D. in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2015 and conducted postdoctoral research at the Massachusetts Institute of Technology (MIT). His primary research areas include DNA nanotechnology, computational mechanics, and medical artificial intelligence, with numerous publications in leading scientific journals.

Seminar Details:

    • Title: Vision and Research of the Multiphysics Systems Design Laboratory
    • Speaker: Associate Professor Dr. Hyungmin Jun
    • Time: 9:00 AM – 11:00 AM, Thursday, November 28, 2024
    • Venue: Room C32A, University of Science, VNU-HCM
      (227 Nguyen Van Cu Street, Ward 4, District 5, Ho Chi Minh City)

Abstract:

The Multiphysics Systems Design Laboratory (MSDL) at Jeonbuk National University conducts cutting-edge research at the intersection of DNA nanotechnology, AI for medical and smart farm applications, and biomechanics and computational methods, emphasizing applying advanced mathematical and computational methods. In DNA nanotechnology, we design and synthesize DNA origami structures and hybrid RNA-DNA nanoparticles for applications in gene therapeutics, drug delivery, and material science. In AI research, our work spans data-driven digital biomarkers, AI-assisted diagnostics, and smart farming systems, applying deep learning techniques to improve healthcare and agriculture areas. In computational mechanics, we specialize in nonlinear finite element methods, phase-field modeling, composite structure analysis, structural topology optimization, and virtual heart simulations to address complex engineering challenges. The lab’s interdisciplinary approach underscores the synergy between engineering and mathematics in solving real-world problems, fostering innovation, and offering impactful solutions across diverse fields.

This seminar will also highlight scholarship and research opportunities at Jeonbuk National University for interested students and researchers.

Registration:

We warmly invite faculty members, PhD candidates, graduate students, and undergraduate students to participate.
Register here!

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WorldQuant Office Tour Event

WorldQuant is a company specializing in developing and deploying systematic financial strategies across various asset classes and global markets. It seeks to generate predictive signals (alphas) through its proprietary research platform to implement financial strategies that capitalize on market inefficiencies.

On December 4, 2024, WorldQuant will host an Office Tour Event to welcome outstanding students from the Faculty of Mathematics and Computer Science to its office. The event will offer insights into WorldQuant, the field of quantitative finance, internship opportunities for 2025, and the chance to network with experienced researchers working at the company.

Details of the Office Tour Event are as follows:

  • Time: 3:00 PM – 5:00 PM, Wednesday, December 4, 2024
  • Location: Saigon Centre Building, 67 Le Loi Street, Ben Nghe Ward, District 1, Ho Chi Minh City
  • Expected number of participants: 25-30 students

Register now: Click here to register

Priority Criteria:

  • Third- or fourth-year students from the Faculty of Mathematics and Computer Science with an interest in quantitative finance.
  • Students in the Honors Program or with a GPA > 3.2/4.
  • Preference will be given to students with awards from national or international academic competitions.
  • Preference will also be given to students with published scientific research.

Registration: until November 29, 2024.

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Long Tran-Thanh is a Professor at the Department of Computer Science, University of Warwick, UK. He is currently the Director of Research of the department (Deputy-Head) and the university's Chair of Digital Research Spotlight. Long has been doing active research in a number of key areas of Artificial Intelligence and multi-agent systems, mainly focusing on multi-armed bandits, game theory, and incentive engineering, and their applications to Al for Social Good. He has published more than 80 papers at peer-reviewed A* conferences in Al/ML (including AAAI, AAMAS, CVPR, IUCAI, NeurIPS) and journals (JAAMAS, AlJ), and have received a number of prestigious national/international awards, including 2 best paper honourable mention awards at top-tier Al conferences (AAAI, ECAI), 2 Best PhD Thesis Award Honourable Mentions (UK's BCS and Europe's ECCAl/EurAl), and the co-recipient of the 2021 AlJ Prominent Paper Award (for one of the 2 most influential papers between 2014-2021 published at the Artificial Intelligence Journal). Long has also been actively involved in a number of community services, including being the local co-chair for AAMAS 2021, AAMAS 2023, KR 2021, KR 2024, and AAMAS 2027. He is an Associate Editor for JAAMAS, and a member of the Editorial Board for AlJ. Previously he was a member of the IFAAMAS Board of Directors between 2018-2024 and a Turing Fellow at the Alan Turing Institute, UK.

Students, graduate students, PhD candidates, and interested colleagues are welcome to join the seminar with the following details:

  • Time: 11:00 AM, Monday, November 25, 2024
  • Venue: Room E202, Campus I, University of Science, 227 Nguyen Van Cu Street, Ward 4, District 5, Ho Chi Minh City
  • Title: Attacking Reinforcement Learning Agents via Data Poisoning and How to Defend
  • Abstract:

Bandit algorithms and Reinforcement Learning models have been widely used in many successful applications in the recent years. However, it has been shown that these algorithms are vulnerable against data poisoning attacks, where an Adversary can manipulate the feedback of our Agent, guiding it to learn a suboptimal (or a targeted) behaviour on the long run. In this talk I will discuss the theoretical boundaries of such attacks, such as what the provable necessary and sufficient conditions are for a successful attack against different types of learning agents. I will also discuss a verification based way of defence mechanism against such data poisoning attacks. This talk is a summary of our recent papers published a AAAI 2022, ISCAl 2022, AAMAS 2024, with some new unpublished results.

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  1. Mathematical Analysis Seminar, Academic Year 2024-2025
  2. FIRIS Investment and Consulting Company - Actuarial Intern
  3. Sungkyunkwan University (SKKU) (South Korea)
  4. Vừ A Dính Scholarship for the Academic Year 2024-2025