Machine Learning Foundations


• Supervised, Unsupervised and Semi Supervised Learning
• Reinforcement Learning and Sequential Decision Making
• Probabilistic Modeling and Bayesian Machine Learning
• Optimization Methods for Machine Learning
• Learning Theory, Generalization and Sample Efficiency
• Representation Learning and Feature Learning

Deep Learning and Neural Architectures


• Deep Neural Networks and Training Dynamics
• Transformers and Attention Based Models
• Graph Neural Networks (GNNs) and Graph Transformers
• Self Supervised and Contrastive Learning
• Neural Architecture Search (NAS)
• Foundation Models and Large Scale Pretraining

Generative Models and Synthetic Data


• Diffusion Models and Score Based Generative Models
• Generative Adversarial Networks (GANs)
• Synthetic Data Generation and Data Centric AI
• Generative Modeling for Images, Text, Audio, Video and Multimodal Data

Advanced Learning Paradigms


• Meta Learning and Few Shot Learning
• Continual, Lifelong and Online Learning
• Multi Task and Transfer Learning
• Active Learning and Curriculum Learning
• Federated, Distributed and Collaborative Learning

Causal and Explainable Machine Learning


• Causal Inference and Causal Discovery
• Causal Representation Learning
• Counterfactual Reasoning
• Explainable and Interpretable Machine Learning

Time Series, Forecasting and Sequential Modeling


• Deep Learning for Time Series Forecasting
• Streaming Data and Online Prediction
• Event Based and Temporal Modeling
• Sequential and Structured Data Analysis

Scientific Machine Learning (SciML)


• Neural Differential Equations
• ML for Physics, Chemistry, Biology and Engineering
• ML for Scientific Discovery, Simulation and Surrogate Modeling
• Physics Informed Machine Learning

ML Security, Safety and Robustness


• Adversarial Attacks and Defenses
• Model Extraction, Poisoning and Evasion Attacks
• Secure and Trustworthy ML Pipelines
• Safety, Reliability and Risk Aware ML
• ML for Safety Critical Systems (healthcare, aviation, autonomous driving)

Scalable, Efficient and Systems Level ML


• Efficient Training: Compression, Pruning, Quantization
• Large Scale ML Systems and Distributed Training
• Hardware Aware ML (GPUs, TPUs, Edge Devices)
• Energy Efficient and Sustainable ML
• Real Time ML, Edge ML andTinyML

Robotics, Embodied AI and Control


• Robot Learning and Policy Optimization
• Embodied Agents and Perception Action Loops
• Sim to Real Transfer
• Learning for Autonomous Systems

ML for Code, Software Engineering and Program Synthesis


• Code Generation and Repair
• Program Synthesis and Verification
• ML Assisted Software Development
• Multimodal Code Understanding

Multimodal Learning, Vision and Perception


• Computer Vision and Visual Recognition
• Vision Language Models and Multimodal Fusion
• 3D Vision, Scene Understanding and Embodied Perception
• Audio, Speech and Sensor Based Learning

Differentiable Programming and Implicit Models


• Differentiable Optimization Layers
• Implicit Neural Representations and Equilibrium Models
• Differentiable Physics and Simulation
• End to End Differentiable Pipelines

Agentic AI and Autonomous ML Systems


• Autonomous ML Agents and Tool Using Systems
• Multi Agent Learning, Cooperation and Negotiation
• Planning + Reasoning + Acting Loops
• Agentic Evaluation and Safety Frameworks

Quantum Machine Learning


• Quantum Inspired ML Algorithms
• Hybrid Quantum Classical Models
• Quantum Optimization and Simulation

ML for Biology, Medicine and Synthetic Bio Design


• Protein and Molecule Design with ML
• DNA/RNA Sequence Modeling
• ML for Gene Editing and Synthetic Biology
• Biological Foundation Models

ML for Economics, Markets and Mechanism Design


• Market Simulation and Prediction
• Mechanism Design and Auctions
• Game Theoretic Machine Learning
• ML for Economic Forecasting

ML for Infrastructure, Networking and Systems Optimization


• ML for Cloud and Distributed Systems
• ML for Networking, Routing and Traffic Optimization
• ML for Resource Allocation and Scheduling

Geospatial, Earth Observation and Climate ML


• Satellite Imagery and Remote Sensing ML
• Geospatial Forecasting and Mapping
• Climate Modeling and Environmental ML

Data Mining, Knowledge Discovery and Predictive Analytics


• Pattern Mining and Anomaly Detection
• Predictive Modeling and Forecasting
• Large Scale Data Mining and Big Data Analytics
• Knowledge Discovery in Databases (KDD)

Applied Machine Learning Across Domains


• Healthcare, Bioinformatics and Drug Discovery
• Finance, Economics and Risk Modeling
• Cybersecurity and Threat Detection
• Social Media, BehaviorModeling and Misinformation
• Education, Personalization and Learning Analytics
• Industrial Systems, IoT and Smart Manufacturing

Evaluation, Benchmarking and Reproducibility


• ML Evaluation Metrics and Benchmark Design
• Reproducibility, Transparency and Open Science
• Dataset Governance, Quality and Bias Detection
• Model Auditing and Performance Diagnostics

AI Governance, Ethics and Societal Impact


• Fairness, Bias and Ethical AI
• AI Governance, Regulation and Policy Frameworks
• Societal Impact and Responsible Deployment
• Human Centered and Human AI Collaborative Systems

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC digital library.