Research Topics in AI/ML/DL, Neural Networks, Computer Vision, Robotics
1. Knowledge Representation (KR) and Reasoning
- Knowledge representation and reasoning (KRR)
 - Types and levels of knowledge
 - Knowledge cycle in AI
 - Argumentation
 - Belief revision and update, belief merging, information fusion
 - Logic in AI
 - Description logics
 
- KR algorithms
 - KR and autonomous agents and multi-agent systems
 - KR and cognitive robotics
 - KR and cyber security
 - KR and education
 - KR and game theory
 - KR and machine learning, inductive logic programming, knowledge acquisition
 - KR and natural language processing and understanding
 - KR and the Web, Semantic Web
 - Knowledge graphs and open linked data
 - Knowledge representation languages
 - Logic programming, answer set programming
 - Multi- and order-sorted representations and reasoning
 - Nonmonotonic logics, default logics, conditional logics
 - Ontology and epistemology
 - Ontology evolution and dynamics
 - Ontology formalisms and models
 - Ontology-based data access, integration, and exchange
 - Applications of KR
 - Philosophical foundations of KR
 - Uncertainty, vagueness, many-valued and fuzzy logics
 - Knowledge-based systems (knowledge acquisition, knowledge discovery and data mining, knowledge representation and management)
 - Knowledge media technologies
 
-- Reasoning and Inference
- Reasoning and inference
 - Reasoning about knowledge
 - Reasoning and evolution
 - Real-time inference
 - Commonsense reasoning
 - Qualitative reasoning, reasoning about physical systems
 - Reasoning about actions and change, action languages
 - Reasoning about constraints, constraint programming
 - Reasoning about knowledge, beliefs, and other mental attitudes
 - Computational aspects of knowledge representation
 - Concept formation, similarity-based reasoning
 - Contextual reasoning
 - Decision making
 - Explanation finding, diagnosis, causal reasoning, abduction
 - Geometric, spatial, and temporal reasoning
 - Inconsistency- and exception-tolerant reasoning, paraconsistent logics
 - Modeling and reasoning about preferences
 
2. Smart Technology
- Pervasive computing and ambient intelligence
 - Hybrid intelligent systems
 - Intelligent system architectures
 - Intelligent machinima generation
 - Explainable recommendation and search
 - Intelligent visual computing
 - Intelligent tutoring systems 
 - Intelligent disaster warning system
 - Intelligent user interfaces
 - Visual intelligence
 - Neuromorphic computing and AI
 - Anticipatory computing
 - Bayesian networks
 - Knowledge-base and theory refinement
 - Graph mining
 - Knowledge discovery (data mining, text mining, and web mining)
 - Data mining and knowledge management
 - Large scale distributed knowledge management
 - Information fusion for decision making under uncertainty
 - Secure distributed learning
 - Data and knowledge representation standards
 - Domain-driven actionable knowledge discovery
 - AI-optimized Hardware
 - Biometrics
 - Multimedia & cognitive informatics
 - Pattern recognition
 - Data clustering
 - Computational social science
 - Computational ecology 
 - Computational behavioral science 
 - Computational medicine
 - Evolutionary computation
 - Computational intelligence
 - Neural computation
 - Brain-computer interfaces & computational neuroscience
 
3. Social Computing and Web Intelligence: Theory and Applications
- Social media analytics
 - Social media mining
 - Web intelligence
 - Web search engines
 - Intelligent web mining and applications
 - Intelligent web personalization
 - Web intelligence applications & search
 - Semantic web techniques and technologies
 - Web mining and ontology construction
 - Social informatics
 - Mobile crowdsourcing
 - Crowdsourcing to understand website privacy policies
 - Learning algorithms for gleaning semantic and structured knowledge from massive social media and web data
 - Information extraction
 - Intelligent web-based systems
 
4. Embodies and Ambient Intelligence  
- Embodied Intelligence, Sensors and Robotics
 - Computer Vision
 - Ontology
 - Pervasive computing
 - Augmented reality
 - Radio frequency identification
 - health care system
 - Smart cities
 - Internet of Things
 - Ambient asisted living
 - Smart healthcare
 - Intelligent transportation
 - Data science
 - Sensing and sensor networks
 - Affective computing
 - Agents and multi-agent systems
 - Context-aware pervasive systems
 
4. Artificial Intelligence (AI)
- Green Artificial Intelligence (AI)
 - AI agents and perceptions
 - AI models and algorithms
 - AI environments
 - Multi-agent systems
 - Multi-Agent systems and distributed AI
 - Artificial narrow intelligence (ANI), Artificial general intelligence (AGI), Artificial superintelligence (ASI)
 - AI and information agents on the Internet
 - AI systems integration
 - Hybrid intelligent systems
 - AI governance and framework
 - Artificial life (ALife or A-Life)
 - Conscious AI
 - AI hallucinations
 - AI and Intelligent agents
 - Responsible and explainable AI
 - Self-supervised representation learning
 - Human activity recognition
 - Action understanding
 - Game theory
 - AI at scale
 - Wireless AI perception
 - Physics Informed Deep Learning
 - Physics Informed Machine Learning
 - Emerging intelligent technologies and applications 
 - Intelligent computer networks and systems
 - Collective intelligence
 - Commonsense reasoning
 - Probabilistic reasoning
 - Computer model (simulator) and model uncertainty
 - On-device AI
 - Generative AI and Large Language Models (LLMs)
 - AI tools & applications
 - AI accelerators
 - Photonic Accelerators for AI
 - Heuristic and AI planning strategies and tools
 - Computational theories of learning
 - State-space problem representations
 - Uninformed and heuristic search
 - Game playing and adversarial search
 - Logical agents
 - Constraint satisfaction problems
 - New learning systems and styles
 - Intelligent user interfaces
 - Brain–computer interface/Mind-machine interface
 - Social computing and crowdsourcing systems
 - Defense AI technology
 - AI for social good
 - Decision management
 - Knowledge management and business intelligence
 - Learning algorithms for gleaning semantic and structured knowledge from massive social media and web data
 - Business intelligence systems
 - Business process management and workflow mining
 - Genetic algorithms and evolutionary computing
 - Pattern discovery (association analysis, anomaly detection, and cluster analysis)
 - Spatiotemporal data analysis
 - Data mining and predictive modeling
 - Engineering informatics
 - Cheminformatics
 - Materials informatics
 - Approximate inference for graphical models
 - Multimedia techniques for enhanced accessibility systems
 - Modeling and organizing large-scale Internet image collections
 - Feedback motion planning for robotics
 - Financial and stock market monitoring and prediction
 
-- Quantum AI
- Quantum machine learning,
 - Quantum-inspired soft computing,
 - Hybrid classical-quantum neural network models,
 - Qubit- and qutrit-based quantum-inspired neural network models,
 - Quantum optimization,
 - Hybrid classical-quantum algorithms
 - Variational quantum algorithms
 - Quantum metaheuristics
 
5. Machine Learning (ML)
- Regression
 - Classification
 - Clustering
 - Dimensionality Reduction
 - Ensemble Methods
 - Neural Nets and Deep Learning
 - Transfer Learning
 - Reinforcement Learning
 - Natural Language Processing
 - Word Embeddings
 
- ML pipelines: feature pipelines, training pipelines, and inference pipelines.
 - Supervised learning
 - Unsupervised learning
 - Semi-supervised learning
 - Deep learning vs. shallow learning
 - ML algorithms and theory
 - Learning and inference
 - Feature engineering
 - Feature selection
 - Feature transformations
 - Shapley value in machine learning
 - Causal modeling and machine learning
 - Causal structure learning and inference
 - Characterization of causal information in observational data
 - Understanding machine learning tasks in light of causality
 - Machine learning methods exploiting causal knowledge
 - Efficient causal discovery in large-scale data
 - Real-world problems for causal analysis.
 - Applied machine learning
 - Learning dynamical systems
 - Learning algorithms for latent variable models 
 - Fine tuning and transfer learning in ML
 - Latent social characteristics learning
 - Machine learning for intelligent IoT
 - Reinforcement learning
 - Advanced learning technologies
 - Data mining and machine learning tools
 - Machine learning in signal processing
 - Reducing dimensionality with principal component analysis (PCA)
 - Ontology
 - Storytelling in games
 - Style transfer
 - Ethical computing
 - Affective computing
 - Cognitive machines
 - Cognitive computing systems
 - Cognitive modeling
 - Medical and diagnostic systems
 - Bioinformatics using intelligent and machine learning techniques
 - Autonomous multi-robot systems
 - Automated planning and scheduling
 - Automatic control
 - Robotic process automation
 - Robot planning and control
 - Human-robot interaction
 - Machine perception (machine vision, machine hearing, machine touch)
 - Multi-modality
 - Sensor machine learning
 - Vision intelligence and machine learning
 - Machine deception
 - Machine perception
 - Machine learning platforms
 - Distributed algorithms in machine learning
 - Machine listening and learning
 - Machine reading
 - Optimization methods for machine learning
 - Markov decision process
 - Sequential decision making
 - Interactive machine learning
 - Embedding and representation learning
 - Active learning
 - Transfer learning
 - Spectral learning
 - Statistical relational learning
 - Statistical methods for machine learning
 - Bayesian learning
 - Bayesian statistics
 - Improving learning efficiency
 
-- Robotics and Robotic Learning
- Polyfunctional robots
 
6. Deep Learning (DL)
- Deep learning and representation learning
 - Neural networks and deep learning
 - Deep neural networks
 - Convolutional neural network
 - Manifold learning
 - Deep visualization
 - Deep learning and computer vision
 - Deep learning platforms
 - Deep learning hardware accelerators (GPUs, FPGAs, and TPUs)
 - Deep generative models
 - Deep reinforcement learning
 - Deep hashing
 - Deep reranking
 - Query expansion
 - Deep token pooling
 
7. Algorithms for ML/DL
-- Commonly Used ML/DL Algorithms
- Linear Regression
 - Logistic Regression
 - Decision tree
 - Linear Discriminant Analysis
 - Classification and Regression Trees
 - Naive Bayes
 - K-Nearest Neighbors (KNN)
 - Learning Vector Quantization (LVQ)
 - Support Vector Machines (SVM)
 - Random Forest
 - Boosting
 - AdaBoost
 - SVM
 - K-means Clustering
 - Apriori Learning Algorithm
 - PCA (Principal Component Analysis)
 - Dimensionality reduction
 - Gradient boosting algorithm and AdaBoosting
 - Stable Diffusion algorithm (text-to-image)
 
-- Statistical Methods for Machine Learning
- Density Estimation
 - Nonparametric Regression
 - Linear Regression
 - Sparsity
 - Nonparametric Sparsity
 - Linear Classifiers
 - Nonparametric Classifiers
 - Random Forests
 - Clustering
 - Graphical Models
 - Directed Graphical Models
 - Causal Inference
 - Minimax Theory
 - Nonparametric Bayesian Inference
 - Conformal Prediction
 - Differential Privacy
 - Optimal Transport and Wasserstein Distance
 - Two Sample Testing
 - Dimension Reduction
 - Boosting
 - Support Vector Machines
 
8. Artificial Neural Networks
- Convolutional neural networks (CNNs)
 - Recurrent neural networks (RNNs)
 - Deep learning algorithms
 - Multi-layer feedforward neural networks
 - Adaptive Learning
 - Patter extraction and detection
 - Semantic meaning extraction from imprecise data
 - Feedforward Neural Networks
 - Radial Basis Function Networks
 - Time delay neural network
 - Regulatory feedback network
 - Probabilistic neural network
 - Genetic Scale RNN
 - Holographic Associative Memory
 - Spiking Neural Networks
 - Cascading Neural Networks
 - Dynamic Neural Networks
 - Neuro-Fuzzy Networks
 - One Shot Associative Memory
 - Instantaneously Trained Networks
 - Hierarchical Temporal Memory
 - Oscillating Neural Network
 - Growing Neural Gas
 - Counter Propagation Neural Network
 - Hybridization Neural Network
 
9. Natural Language Processing, Large Language Models, Generative AI, and Foundation Models
- Natural Language Processing (NLP)
- NLP tokenization
 - Linguistic Intelligence in AI
 - Computational linguistics and speech processing
 - Speech recognition systems
 - Text-to-speech synthesizers
 - Automated voice response systems
 - Automatic speech recognition
 - Language instruction materials
 - Biomedical natural language processing
 - Machine translation/multilingual processing
 - Information extraction and natural language processing
 - Natural language generation and understanding
 - Automatic text summarization
 - Natural language processing and computational linguistics
 - Natural language processing: algorithms and tools to extract computable information from Electronic Health Records (EHRs) and from the biomedical literature
 - Natural language learning
 - Natural language understanding (NLU) AI models
 - Language generation and conversational AI
 - Sentiment analysis (or opinion mining)
 - Sentiment analysis tools
 - Track brand sentiment
 - Language grounding with vision
 - Statistical relational learning
 - Transfer learning
 - Active learning
 - Auditory, speech and language processing
 - Text analytics and natural language processing (NLP)
 - Natural language processing and its connections with data mining, social science and vision
 - Spoken language understanding
 - Physical commonsense
 - Unsupervised speech processing
 - Automatic text summarization
 - Speech processing and synthesis
 - Voice synthesis
 - Text-to-Speech (TTS)
 - Speech recognition
 - Machine learning, natural language processing, and social media
 - Conversational search, recommendation, and QA systems
 - Online handwriting and gesture recognition technology
 
- The Themes of NLP (Theme extraction, Sentiment analysis, Named entity recognition, Text summarization, Text classification, Topic modeling, Keyword extraction, Information extraction)
 - Prompt and prompt engineering
 - LLM fine-tuning vs prompt engineering
 
- Large Language Models (LLMs)
- LLM training
 - Efficient training
 - Tokenization
 - Human collaboration
 - Chatbots and virtual assistants
 - AI Chatbots and Conversational AI
 - Healthcare applications
 - Limitations of LLMs, such as computational constraints, hallucinations, and limited knowledge
 - Reduce and measure hallucinations
 
- Optimize context length and context construction
 - Incorporate other data modalities
 - Make LLMs faster and cheaper
 - Design a new model architecture
 - Develop GPU alternatives
 - Make agents usable
 - Improve learning from human preference
 - Improve the efficiency of the chat interface
 - Retrieval-augmented generation (RAG)
 
- Generative AI
- Architecture and Model Design
 - Image generating and Manipulation
 - Natural Language Processing and Text Generation
 - Music and Audio Generation
 - Prediction and Video Generation
 - Unsupervised and Semi-Supervised Learning
 - Understanding and enhancing Latent Representations learnt by Generative Models
 - Multi-Modal and Cross-Modal Generation
 - Robustness and Fairness in Generative AI
 - Transfer Learning and Pre-Training
 - Generative Models That protect Data Privacy
 - Real-World Applications and Deployability
 - Multimodal Creativity and AI Collaboration
 - Agentic AI
 
10. Foundation Models
11. Computer Vision
- Optical character recognition (OCR)
 - Computer vision and speech understanding
 - Vision and Language in Computer Vision
 - Vision sensor technology
 - Computer vision applications (autonomous navigation, visual surveillance, or content-based image and video indexing)
 - Fusing 3D scene reconstruction
 - 3D perception
 - Vision, language, and cognition
 - Probabilistic graphical models
 - Computational photography
 - Medical vision
 - Computer vision and machine learning
 - Video understanding
 - Visual recognition and visual search
 - Large-scale image/video retrieval
 - Unsupervised visual discovery
 - Image and video segmentation
 - Vision and language
 - Video summarization.
 - Learning-based visual reconstruction
 - Understanding video contents
 - Visual sensing for ecology and conservation
 - Smart graphics
 - Graphics, human computer interaction, & user experience
 - People counting tool
 - Colors detection
 - Object tracking in a video
 - Pedestrian detection
 - Hand gesture recognition
 - Human emotion recognition
 - Road lane detection
 - Business card scanner
 - License plate recognition
 - Handwritten digit recognition
 - Iris Flowers Classification
 - Family photo face detection
 - LEGO Brick Finder
 - PPE Detection
 - Face mask detection
 - Vision-language models (VLMs)
 
12. Machine Vision, Robotics and Robot Learning
-- Machine Vision
- Human Computer Interaction
 
- Pattern Recognition
 
- Image/Video Processing
 
- Intrusion Detection
 
- Brain-Machine Interface
 
- Geographic Information Systems
 
- Signal Processing
 
- Medical Diagnosis
 
- Segmentation Techniques
 
- Augmented/Virtual Reality
 
 -- Robotics and Robot Learning
- Robot learning
 - Robotics and computational perception
 - Mobile robots
 - Robot navigation
 - Distributed robotics  
 - Wearable robotic systems
 - Networked robotic systems
 - Swarm robotics
 - Swarm intelligence
 - Microrobotics 
 - Soft robotics
 
-- Robotics and Robot Learning
- Polyfunctional robots
 - Bio-Inspired Robotics
 - Biomedical Robotics
Computational Intelligence in Robotics - Field Robotics
 - Haptics
Human-Robot Interaction - Humanoid Robotics
 - Industrial Robotics and Automation
 - Vision AI in Robotic Perception and Mapping
 - Learning-based Advanced Solutions for Robot Autonomous Computing
 
13. AI-driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP)
- AI-driven TSP (Traveling Salesman Problem) computing
 - AI-driven TSP computing in IoT/CPS/Edge/Fog/Cloud paradigms
 - AI-driven TSP computing smart data and Big data paradigm
 - AI-driven TSP computing with blockchain
 - AI-driven TSP computing in activity recognition, HCI
 - AI-driven TSP with wearable devices, embedded HW and SW, and applications
 - AI-driven TSP with agent-based computing
 - AI-driven TSP with nature- and brain-inspired computing
 - AI-driven TSP computing against AI-driven malware and fault injections
 - AI-driven TSP computing against AI-driven supply chain & hardware attacks
 - AI-driven TSP computing in big data capture, classification, and analytics
 - AI-driven TSP computing with nano & micro-systems and quantum computing
 - AI-driven TSP computing in OS, virtualization, database, and software systems
 - AI-driven TSP measures, metrics, verification, and validation
 - AI-driven sensing, detection, prevention, and recovery against potential threats
 - AI-driven applied cryptography and security protocols
 - AI-driven defense against AI-driven threats/attacks
 - AI-driven data trust, system trust, service trust, application trust, etc.
 - TSP with learning methods (ML/DL/DRL/FL)
 - TSP, anonymity, and resilience analysis on AI
 - TSP with AI-driven data mining and knowledge discovery
 - TSP concerns with AI-driven technologies, such as GAN
 - TSP with reactive distributed AI, AI tools, and applications
 - Trustworthy ML, DL, DRL, and FL methods and tools
 - Trustworthy AutoML, AutoDL, and automatic control
 - Trustworthiness with AI-driven authentication, access control, & monitoring
 - Theoretical studies on big data system trustworthiness, privacy, and security
 - Fairness, explainability, accountability, reliability, and safety with AI
 
14. The Popular DL Applications
- Virtual Assistants
 - Chatbots
 - Healthcare
 - Entertainment
 - News Aggregation and Fake News Detection
 - Composing Music
 - Image Coloring
 - Robotics
 - Image Captioning
 - Advertising
 - Self Driving Cars
 - Natural Language Processing
 - Visual Recognition
 - Fraud Detection
 - Personalisations
 - Detecting Developmental Delay in Children
 - Colourisation of Black and White images
 - Adding Sounds to Silent Movies
 - Automatic Machine Translation
 - Automatic Handwriting Generation
 - Automatic Game Playing
 - Language Translations
 - Pixel Restoration
 - Demographic and Election Predictions
 - Deep Dreaming
 

