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
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
Older
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
1. Deep Neural Network Models with Artificial Neural Memory:
- Created novel architectures that incorporate artificial neural memory, leading to breakthroughs in multi-modal and generative AI. ✏️ KDD18, NeurIPS18, ICLR22
- Developed foundational theories for memory operations, enabling more effective learning and reasoning in neural networks. ✏️ ICLR19, ICML20, ICLR23
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:
- Designed agents that leverage memory to improve decision-making. ✏️ NeurIPS21, AAAI22, AAMAS24
- Developed memory-based optimization approaches to improve reinforcement learning. ✏️ NeurIPS22
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)