Research Areas in Artificial Intelligence
For those who are interested in doing research in AI or want to know what are the main research fields in AI, I provide here a taxonomy of the most recent research topics in AI and their subtopics. Similar lists are used to classify papers into different categories when they are submitted to various AI conferences.
Data Mining and Data Science (DMS)
- DMS: Anomaly/Outlier Detection
- DMS: Applications
- DMS: Data Compression
- DMS: Data Stream Mining
- DMS: Data Visualization & Summarization
- DMS: Graph Mining, Social Network Analysis & Community Mining
- DMS: Linked Open Data, Knowledge Graphs & KB Completion
- DMS: Mining of Spatial, Temporal or Spatio-Temporal Data
- DMS: Mining of Visual, Multimedia & Multimodal Data
- DMS: Other Foundations of Data Mining & Knowledge Management
- DMS: Personalization & User Modeling
- DMS: Recommender Systems
- DMS: Rule Mining & Pattern Mining
- DMS: Scalability, Parallel & Distributed Systems
- DMS: Web Search & Information Retrieval
Machine Learning (ML)
- ML: Active Learning
- ML: Adversarial Learning & Robustness
- ML: Applications
- ML: Auto ML and Hyperparameter Tuning
- ML: Bayesian Learning
- ML: Bias and Fairness
- ML: Bio-inspired Learning
- ML: Causal Learning
- ML: Classification and Regression
- ML: Clustering
- ML: Deep Generative Models & Autoencoders
- ML: Deep Learning Theory
- ML: Deep Neural Architectures
- ML: Deep Neural Network Algorithms
- ML: Dimensionality Reduction/Feature Selection
- ML: Distributed Machine Learning & Federated Learning
- ML: Ensemble Methods
- ML: Evaluation and Analysis (Machine Learning)
- ML: Evolutionary Learning
- ML: Genetic Algorithms
- ML: Graph-based Machine Learning
- ML: Imitation Learning & Inverse Reinforcement Learning
- ML: Kernel Methods
- ML: Learning Preferences or Rankings
- ML: Learning Theory
- ML: Lifelong and Continual Learning
- ML: Matrix & Tensor Methods
- ML: Meta Learning
- ML: Multi-class/Multi-label Learning & Extreme Classification
- ML: Multi-instance/Multi-view Learning
- ML: Multimodal Learning
- ML: Online Learning & Bandits
- ML: Optimization
- ML: Other Foundations of Machine Learning
- ML: Privacy-Aware ML
- ML: Probabilistic Methods
- ML: Quantum Machine Learning
- ML: Reinforcement Learning Algorithms
- ML: Reinforcement Learning Theory
- ML: Relational Learning
- ML: Representation Learning
- ML: Scalability of ML Systems
- ML: Semi-Supervised Learning
- ML: Time-Series/Data Streams
- ML: Transfer, Domain Adaptation, Multi-Task Learning
- ML: Transparent, Interpretable, Explainable ML
- ML: Unsupervised & Self-Supervised Learning
Speech & Natural Language Processing (SNLP)
- SNLP: Adversarial Attacks & Robustness
- SNLP: Bias, Fairness, Transparency & Privacy
- SNLP: Conversational AI/Dialogue Systems
- SNLP: Discourse, Pragmatics & Argument Mining
- SNLP: Generation
- SNLP: Information Extraction
- SNLP: Interpretability & Analysis of NLP Models
- SNLP: Language Grounding
- SNLP: Language Models
- SNLP: Learning & Optimization for SNLP
- SNLP: Lexical & Frame Semantics, Semantic Parsing
- SNLP: Machine Translation & Multilinguality
- SNLP: Ontology Induction from Text
- SNLP: Phonology, Morphology, Word Segmentation
- SNLP: Psycholinguistics and Language Learning
- SNLP: Question Answering
- SNLP: Sentence-level semantics and Textual Inference
- SNLP: Sentiment Analysis and Stylistic Analysis
- SNLP: Speech and Multimodality
- SNLP: Summarization
- SNLP: Syntax Tagging, Chunking & Parsing
- SNLP: Text Classification
- SNLP: Text Mining
Vision (VIS)
- VIS: 3D Computer Vision
- VIS: Adversarial Attacks & Robustness
- VIS: Applications
- VIS: Bias, Fairness & Privacy
- VIS: Biometrics, Face, Gesture & Pose
- VIS: Computational Photography, Image & Video Synthesis
- VIS: Image and Video Retrieval
- VIS: Interpretability and Transparency
- VIS: Language and Vision
- VIS: Learning & Optimization for CV
- VIS: Low Level & Physics-based Vision
- VIS: Medical and Biological Imaging
- VIS: Motion & Tracking
- VIS: Multi-modal Vision
- VIS: Object Detection & Categorization
- VIS: Other Foundations of Computer Vision
- VIS: Representation Learning for Vision
- VIS: Scene Analysis & Understanding
- VIS: Segmentation
- VIS: Video Understanding & Activity Analysis
- VIS: Vision for Robotics & Autonomous Driving
- VIS: Visual Reasoning & Symbolic Representations
Uncertainty in AI (UAI)
- UAI: Applications
- UAI: Bayesian Networks
- UAI: Causality
- UAI: Decision/Utility Theory
- UAI: Graphical Models
- UAI: Markov Decision Processes
- UAI: Other Foundations of Reasoning under Uncertainty
- UAI: Probabilistic Inference
- UAI: Probabilistic Programming
- UAI: Relational Probabilistic Models
- UAI: Sequential Decision Making
- UAI: Stochastic Models & Probabilistic Inference
- UAI: Stochastic Optimization
- UAI: Uncertainty in AI (General/Other)
- UAI: Uncertainty Representations
Planning and Search (PLAN)
- PLAN: Adversarial Search
- PLAN: Classical Planning Techniques and Analysis
- PLAN: Continuous Planning, On-line and Real-time domains
- PLAN: Distributed Search
- PLAN: Evolutionary computation
- PLAN: Foundations of Planning
- PLAN: Generalized Planning
- PLAN: Multi-agent and Distributed Planning
- PLAN: Nondeterministic and Partially Observable Domains
- PLAN: Plan Execution, Monitoring, Replanning, and Plan Repair
- PLAN: Planning and Scheduling with Time Constraints
- PLAN: Planning under Uncertainty, Probablisitic Planning, MDPs and POMDPs
- PLAN: Scheduling
- PLAN: Simulation/Sampling based search
- PLAN: Heuristic Search
Constraints and Satisfiability (CS)
- CS: Answer Set Programming
- CS: Constraint and Satisfiability (General/Other)
- CS: Constraint Learning and Acquisition
- CS: Constraint Logic Programming
- CS: Constraint Optimization
- CS: Constraint Programming
- CS: Constraint Satisfaction
- CS: Constraints, Data Mining and Machine Learning
- CS: Distributed CSP/Optimization
- CS: Dynamic Programming
- CS: Global Constraints
- CS: Inductive and Co-Inductive Logic Programming
- CS: Logic Programming
- CS: Model Counting
- CS: Modeling and Formulation
- CS: Satisfiability
- CS: Search
- CS: Solvers and Tools
Knowledge Representation and Reasoning (KRR)
- KRR: Action, Change, and Causality
- KRR: Argumentation
- KRR: Automated Reasoning and Theorem Proving
- KRR: Belief Change
- KRR: Case-Based Reasoning
- KRR: Common-Sense Reasoning
- KRR: Computational Aspects
- KRR: Description Logics
- KRR: Diagnosis and Abductive Reasoning
- KRR: Geometric, Spatial, and Temporal Reasoning
- KRR: Goal Reasoning
- KRR: Knowledge Acquisition
- KRR: Knowledge Engineering
- KRR: Knowledge Graphs
- KRR: Knowledge Representation (General/Other)
- KRR: Knowledge Representation Languages
- KRR: Logic Programming
- KRR: Nonmonotonic Reasoning
- KRR: Ontologies
- KRR: Preferences
- KRR: Provenance, Privacy and Security in Semantic Data
- KRR: Qualitative Reasoning
- KRR: Reasoning on Knowledge, Beliefs and Other Mental Attitudes
- KRR: Semantic Web
Robotics (ROB)
- ROB: Applications
- ROB: Behavior Learning & Control
- ROB: Cognitive Robotics
- ROB: Human-Robot Interaction
- ROB: Learning & Optimization for ROB
- ROB: Localization, Mapping, and Navigation
- ROB: Manipulation
- ROB: Motion and Path Planning
- ROB: Multimodal Perception & Sensor Fusion
- ROB: Multi-Robot Systems
- ROB: Other Foundations of Intelligent Robots
- ROB: State Estimation
Multi-Agent Systems (MAS)
- MAS: Agent Theories and Models
- MAS: Agent-Based Simulation and Emergent Behavior
- MAS: Algorithmic Game Theory, Auctions, and Economic Paradigms
- MAS: Communication, Coordination and Collaboration
- MAS: Computational Social Choice
- MAS: Distributed Problem Solving
- MAS: Multiagent Learning
- MAS: Multiagent Planning
- MAS: Multiagent Systems (General/Other)
- MAS: Norms and Normative Systems
- MAS: Temporal, Epistemic, and Strategic Reasoning
- MAS: Verification and Validation
Humans and AI (HAI)
- HAI: Brain-Sensing and Analysis
- HAI: Human Computation
- HAI: Human-Agent Negotiation
- HAI: Human-Aware Planning and Behavior Prediction
- HAI: Human-Computer Interaction
- HAI: Human-Computer Teamwork, Team formation and collaboration
- HAI: Human-in-the-loop Planning/Learning
- HAI: Human-in-the-loop Safeness, Trustworthiness and Fairness
- HAI: Human-in-the-loop: Decision-Making and Explanability
- HAI: Human-Robot/Agent Interaction
- HAI: Interaction Techniques and Devices
- HAI: Language Acquisition
- HAI: Learning Human Values and Preferences
- HAI: Planning and Decision Support for Human-Machine Teams
- HAI: Understanding People, Theories, Concepts and Methods
- HAI: User Experience and Usability
- HAI: User Modeling and Personalization
Other Topics
- AI, Fairness, Ethics and trust (FAIR)
- AI for Social Good (AISG)
- Multidisciplinary Topics (MULT)
My own research focuses on the following topics (and their intersection):
- ML: Reinforcement Learning Algorithms
- ML: Deep Neural Network Algorithms
- UAI: Markov Decision Processes
- PLAN: Nondeterministic and Partially Observable Domains
- ROB: Motion and Path Planning
- ROB: Multi-Robot Systems
- MAS: Multiagent Learning
You can find a list of my published papers here.
Let me know if you are interested in one of these topics in the comments :)