Homepage of Dr. Hung Le

About

I am Hưng, a research lecturer and Australian Research Council DECRA Fellow at Applied Artificial Intelligence Institute (A2I2), Deakin University.

News

2025 PhD Student Intake: 1 PhD scholarship available for relevant research topics aligned with my DECRA grant. I am not accepting master's students at this time. Due to the high volume of applications, only shortlisted candidates will receive a response.
10/2024: 1 paper accepted at WACV
10/2024: My 2024 PhD scholarship was given to Manh Nguyen
09/2024: 1 paper accepted at Transactions on Machine Learning Research journal
09/2024: I have been invited to deliver a tutorial at AJCAI 2024
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08/2024: 1 grant accepted at Discovery Early Career Researcher Award (DECRA) 2025
07/2024: 2 papers accepted at ECAI 2024 (Oral) and another at ISSTA 2024
06/2024: I will serve as Senior Program Committee at AAAI 2025
05/2024: 1 paper accepted at ECML-PKDD 2024
2024 Blog: I plan to transfer my blogs to Substack for ease of management
04/2024: 1 paper accepted at IJCAI 2024
02/2024: I will deliver a tutorial at AAMAS 2024. The tutorial will be about exploration in RL.
12/2023: 1 paper accepted at AAMAS 2024
11/2023: 1 paper accepted at Transactions on Machine Learning Research journal
2024 Spring Intake: I am looking for 1 PhD student (full scholarships) who want to work directly with me on neural memory or reinforcement learning topics. Ping me if you find it interesting.
09/2023: 1 paper accepted at ACML 2023 and another at Artificial Intelligence journal
08/2023: I will serve as Senior Program Committee at AAAI 2024
07/2023: My 2023 PhD scholarship was given to Hoang Nguyen and Dai Do
04/2023: 1 paper accepted at IJCAI 2023 and another at International Journal of Impact Engineering
01/2023: 1 paper accepted at ICLR 2023
12/2022: I have delivered AJCAI22 tutorial on Memory-based Reinforcement Learning
11/2022: 1 paper accepted at AAAI 2023
09/2022: 3 papers accepted at NeurIPS 2022 and another 2 at ICONIP 2022
07/2022: I will serve as Senior Program Committee at AAAI 2023
06/2022: 1 paper accepted at ECCV 2022
05/2022: 1 paper accepted at ICML 2022
03/2022: I was promoted to Research Lecturer Position (Permanent Track)
01/2022: 1 paper accepted at NAACL 2022
12/2021: 1 paper accepted at ICLR 2022

My Research

Inspired by biological systems and traditional computer architectures, I am deeply passionate about advancing the capacity of neural networks for representational (the what) and functional (the how) learning in computer science. My current research focuses on pioneering advancements in deep learning, reinforcement learning, and artificial memory to develop robust, generalizable and human-like AI solutions for long-term sequence modeling and decision-making. I have made significant contributions in the following areas:


1. Deep Neural Network Models with Artificial Neural Memory:

2. General-Purpose Neural Computers:

  • Investigated the design and implementation of neural computers akin to Universal Turing Machines, capable of handling diverse tasks such as continual learning, machine reasoning and reinforcement learning. ✏️ ICLR20, ICML22
  • Devised a general memory module that enhances the generalization and reasoning capabilities of various deep learning models from small RNNs to giant LLMs. ✏️ TMLR24

3. Memory-Based Reinforcement Learning Agents:

I have applied these techniques to time-series, dialogue systems, healthcare, human-ai symbiosis, material science, robotics and gaming applications, demonstrating a commitment to advancing AI capabilities across various domains through innovative research and practical applications.

Past Research Projects

  • Memory and Attention in Deep Learning (my PhD at Deakin University, 2017-2020) PhD. Thesis. My great supervisors: A/Prof. Truyen Tran and Prof. Svetha Venkatesh
  • Graphical & Topic Model (when I did Master at HUST with A/Prof. Khoat Than, 2016-2017 dropout)
  • Kalman Filter & Optimal Control (when I worked at Viettel R&D, 2015-2017)
  • Fuzzy Logics (when I did Bachelor Honours at HUST with A/Prof. Khang Tran, 2015)