Track 1 – Statistical Learning Theory and Methods
Foundations of statistical learning and inference

High-dimensional statistics and regularisation techniques

Bayesian learning, probabilistic models, and uncertainty quantification

Robust statistics and outlier-resistant learning methods

Graphical models and structured prediction

Semi-supervised and unsupervised learning methods

Statistical optimisation techniques for large-scale problems

Conformal prediction and distribution-free inference

Track 2 – Machine Learning, Deep Learning, and Hybrid Approaches
Foundations and advances in machine learning and data mining

Reinforcement learning and adaptive decision-making

Transfer learning, domain adaptation, and meta-learning

AutoML and neural architecture search

Explainable and interpretable ML models

Time-series forecasting and sequential data modeling

Quantum machine learning and emerging paradigms

Anomaly detection, ensemble methods, and model aggregation

ML for healthcare, diagnostics, and biomedical data

Track 3 – Generative AI and Foundation Models
Generative adversarial networks (GANs) and variational autoencoders (VAEs)

Diffusion models, energy-based models, and flow-based generative techniques

Large language models (LLMs) and multimodal foundation models

Data synthesis, augmentation, and privacy-preserving generation

Controllable text, image, and audio generation

Evaluation, alignment, and safety of generative models

Few-shot, zero-shot, and prompt-based learning techniques

Applications of generative AI in science, design, and industry

Track 4 – Natural Language Processing and Multimodal Understanding
Sentiment and emotion analysis, opinion mining

Information retrieval, question answering, and knowledge-augmented LLMs

Conversational agents, dialog management, and interactive AI

Cross-lingual NLP and low-resource language processing

Neural machine translation and speech-language models

Text summarization, argument mining, and discourse analysis

Multimodal fusion of text, audio, and vision data

Ethical considerations in language and multimodal models

Track 5 – Computer Vision, Image Processing, and 3D Understanding
Foundations and advances in computer vision and image processing

Semantic and instance segmentation, object detection in complex scenes

Visual reasoning, image captioning, and visual question answering

Video understanding, activity recognition, and temporal vision

3D reconstruction, SLAM, and multi-view geometry

Generative image and video synthesis

Facial recognition, affective computing, and biometrics

Image forgery detection, tamper analysis, and deepfake detection

Vision for autonomous systems and human-centric AI

Track 6 – Data Science, Analytics, and Real-World Applications
Foundations and advances in data science, analytics, and real-world systems

Big data analytics and scalable data processing

AI-driven decision support systems

Smart city and urban computing applications

AI in finance, fintech, and risk modeling

Healthcare analytics and personalized medicine

Intelligent transportation, logistics, and mobility solutions

AI for environmental sustainability and climate modeling

Educational analytics and adaptive learning platforms

Track 7 – Robotics, Autonomous Systems, and Edge Intelligence
Learning-based control and safe autonomous navigation

Human-robot interaction, social and collaborative robotics

Swarm intelligence and distributed decision-making

Perception and sensing for robotic platforms

AI at the edge: low-latency, resource-aware intelligence

Soft robotics, bio-inspired systems, and adaptive mechanisms

Reinforcement learning for real-world robotic applications

Reliable and explainable autonomous systems in safety-critical domains