8 tracksBroad by design

Find your track

MLADS is broad by design. The tracks below help you find where your work belongs — but they're a starting point, not a fence. If your research sits near this scope and isn't listed, submit it anyway.

AI for Industry

HealthcareFinanceManufacturingEnergyRetailMobilityPublic sector — where models meet real constraints and real stakes

Responsible AI: Ethics, Fairness & Governance

Fairness, accountability & transparencyBias detection & mitigationPrivacy-preserving MLAI regulation & policyTrustworthy & explainable AI

Reinforcement Learning & Decision Systems

RL algorithms & applicationsMulti-agent systemsOptimisation under uncertaintyControlOperations research meets learning

Data Engineering, Big Data & MLOps

Scalable data pipelinesDistributed & federated learningFeature storesModel deployment, monitoring & governanceStreaming systemsData quality

Applied Data Science & Analytics

Predictive analyticsTime-series forecastingCausal inferenceRecommender systemsAnomaly detectionData-driven decision-makingVisual analytics

Computer Vision & Pattern Recognition

Image & video understandingObject detection & segmentationMultimodal learningMedical & remote-sensing imagingGenerative vision

NLP & Large Language Models

LLMs & foundation modelsRetrieval-augmented generationText miningMachine translationDialogue systemsEvaluation, alignment & safety

Core Machine Learning & Deep Learning

Supervised, unsupervised & self-supervised learningNeural architecturesOptimisationGenerative modelsTransfer & few-shot learningModel interpretabilityTheory of generalisation

Don't see your topic?

Submit anyway. As long as your work relates to machine learning or applied data science, it's within scope. The tracks are a map, not a gate.

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