Edit research

_data/citations.yaml

@@ -1,5 +1,41 @@
 # DO NOT EDIT, GENERATED AUTOMATICALLY
 
+- id: doi:10.48550/ARXIV.2506.03167
+  title: Distributionally Robust Wireless Semantic Communication with Large AI Models
+  authors:
+  - Long Tan Le
+  - Senura Hansaja Wanasekara
+  - Zerun Niu
+  - Yansong Shi
+  - Nguyen H. Tran
+  - Phuong Vo
+  - Walid Saad
+  - Dusit Niyato
+  - Zhu Han
+  - Choong Seon Hong
+  - H. Vincent Poor
+  publisher: arXiv
+  date: '2024-01-01'
+  link: https://doi.org/g9v3k4
+  orcid: 0009-0007-4897-8381
+  plugin: sources.py
+  file: sources.yaml
+  type: paper
+  description: A distributionally robust approach for wireless semantic communication
+    with large AI models that addresses uncertainty in wireless channels and semantic
+    information transmission, enhancing reliability in AI-powered communication systems.
+  buttons:
+  - type: source
+    text: ArXiv
+    link: https://arxiv.org/abs/2506.03167
+  tags:
+  - semantic communication
+  - large AI models
+  - distributionally robust optimization
+  - wireless communication
+  - uncertainty
+  - robustness
+  journal: arXiv preprint
 - id: arxiv:2006.08848
   title: Personalized Federated Learning with Moreau Envelopes
   authors:
@@ -204,41 +240,6 @@
   journal: Mobile Networks and Applications
   plugin: sources.py
   file: sources.yaml
-- id: doi:10.48550/ARXIV.2506.03167
-  title: Distributionally Robust Wireless Semantic Communication with Large AI Models
-  authors:
-  - Long Tan Le
-  - Senura Hansaja Wanasekara
-  - Zerun Niu
-  - Yansong Shi
-  - Nguyen H. Tran
-  - Phuong Vo
-  - Walid Saad
-  - Dusit Niyato
-  - Zhu Han
-  - Choong Seon Hong
-  - H. Vincent Poor
-  publisher: arXiv
-  date: '2024-01-01'
-  link: https://doi.org/g9v3k4
-  type: paper
-  description: A distributionally robust approach for wireless semantic communication
-    with large AI models that addresses uncertainty in wireless channels and semantic
-    information transmission, enhancing reliability in AI-powered communication systems.
-  buttons:
-  - type: source
-    text: ArXiv
-    link: https://arxiv.org/abs/2506.03167
-  tags:
-  - semantic communication
-  - large AI models
-  - distributionally robust optimization
-  - wireless communication
-  - uncertainty
-  - robustness
-  journal: arXiv preprint
-  plugin: sources.py
-  file: sources.yaml
 - id: arxiv:2407.07421
   title: Federated PCA on Grassmann Manifold for IoT Anomaly Detection
   authors:

_data/orcid.yaml

@@ -1,2 +1 @@
-
-
+- orcid: 0009-0007-4897-8381

index.md

@@ -5,7 +5,7 @@
 
 {% capture text %}
 
-We conduct cutting-edge research in artificial intelligence, federated learning, and large language models. Our work focuses on developing efficient, fair, and interpretable AI systems that can operate effectively in distributed and resource-constrained environments.
+Our research integrates distributed computing, mathematical optimization, and artificial intelligence to build scalable, efficient, and interpretable learning systems. By combining rigorous theoretical foundations with practical deployment, we deliver intelligent solutions that perform reliably in real-world, resource-constrained environments.
 
 {%
   include button.html

research/index.md

@@ -7,9 +7,9 @@ nav:
 
 # {% include icon.html icon="fa-solid fa-microscope" %}Research
 
-Our research focuses on cutting-edge areas in artificial intelligence, machine learning, and distributed systems. We investigate novel approaches to improve AI efficiency, fairness, and interpretability, with particular emphasis on large language models, federated learning, and edge computing.
+Our research investigates the synergistic interplay of distributed computing, mathematical optimization, and artificial intelligence to create scalable, efficient, and trustworthy machine learning systems. We develop algorithms that jointly exploit distributed architectures and optimization principles to advance the efficiency, fairness, and interpretability of modern AI, with particular emphasis on large language models, federated and edge learning, and real-time collaborative inference. 
 
-Our team explores both theoretical foundations and practical applications, contributing to the advancement of intelligent systems that can operate effectively in real-world, resource-constrained environments.
+At the same time, the team explores both theoretical foundations and practical applications, contributing to the advancement of intelligent systems capable of operating effectively in real-world, resource-constrained environments. This integrated approach delivers new theoretical insights and deployable frameworks for next-generation AI across heterogeneous and dynamic computational settings.
 
 {% include section.html %}