Research Directions and Interests

Evolutionary Computation

The development of algorithms inspired by biological evolution to solve computational problems

Optimization

The study of methods to optimize the performance of machine learning models

Active Learning

Prioritizing data labelling to optimize the training of supervised models

Deep Learning

Algorithms inspired by the structure and function of the brain referred to as artificial neural networks

Reinforcement Learning

Enabling an agent to learn by trial and error using feedback from actions and experiences

Natural Language

A field that gives machines the ability to read, understand and derive meaning from human language

Time Series

The use of machine learning methods in a manner that seeks to predict future value

Graphics & Visualization

Generating graphics with the aid of machine learning

Techniques and Models

Artificial Intelligence

  1. Supervised Learning (Classification, Regression)
  2. Unsupervised Learning (Clustering)
  3. Transfer Learning
  4. Machine Learning and Unlearning
  5. Federated Learning and Unlearning
  6. Centralized and Decentralized Learning/Optimization

Deep Learning Models

  1. Neural Network (MLP, FLNN, SONIA, ELM, RBFN, CFNN, ANFIS, Hopfield)
  2. Recurrent-based Network (RNN, GRU, LSTM)
  3. Deep Neural Network (DNN, CFNN, CNN, Transformer-based)

Optimization Branches

  1. Meta-heuristic Algorithms (Approximation algorithms, Evolutionary algorithms, Nature-inspired computing, Swarm optimization, soft-computing)
  2. Multi-objective Optimization
  3. Convex programming (Linear programming)
  4. Combinatorial Optimization
  5. Robust Optimization

Natural Language Processing

  1. Word Segmentation
  2. Part-of-speech tagging
  3. Stemming and Parsing
  4. Named entity recognition
  5. Sentiment analysis

Applications

  1. Benchmark functions optimization
  2. Engineering applications
  3. Log template generation for large-scale system
  4. Resources consumption management (decision making system)
  5. Time-series prediction (i.e, hydrology related such as inflow, streamflow, groundwater level forecasting)

My Current Supervisor

  1. Dr. Vu Nguyen HA
  2. Dr. Ti Ti Nguyen
  3. Dr. Ons Aouedi

My Collaborative Scholars

ML + Optimization Field
  1. Prof. Seyedali Mirjalili
  2. Assoc. Prof. Diego Oliva
  3. Prof. Huynh Thi Thanh Binh
  4. Prof. Ibrahim Aljarah
  5. Prof. Hossam Faris
  6. Assoc. Prof. Binh Minh Nguyen
  7. Dr. Essam Halim Houssein
  8. Dr. Ali Asghar Heidari
  9. Dr. Giang Nguyen
  10. Dr. Olaide Oyelade
  11. Dr. Daniel Stolfi Rosso
  12. Assoc. Prof. Harish Garg
Applied AI in Cloud, Edge Computing Field
  1. Prof. Symeon Chatzinotas
  2. Dr. Nguyen Cong Luong
  3. Dr. Van-Dinh Nguyen
  4. Assoc. Prof. Quang-Trung Luu
Applied AI in Hydrology Field
  1. Assoc. Prof. Ozgur Kisi
  2. Prof. Ahmed El-Shafie
  3. Dr. Surajit Deb Barma
  4. Dr. Ali Najah Ahmed
  5. Dr. Pham Quoc Bao
Applied AI in Mining Industry Field
  1. Dr. Nguyen Hoang
  2. Dr. Tran Trung Tin
  3. Assoc. Prof. Yosoon Choi
Applied AI in System Log
  1. Prof. Kensuke Fukuda
  2. Dr. Satoru Kobayashi

Artificial Intelligence Independent Research Group (AIIR Group)