Artificial Intelligence Research Group
The Artificial Intelligence group develops innovative digital technologies for building and applying intelligent information systems to real-world problems.
Our research addresses the question of how to extract context-aware knowledge from data including sensor, text or multimedia, for decision support. Similarity and semantic knowledge discovery algorithms are developed to enable rich content representations that can transform experiential content into actionable knowledge for decision support systems.
This group has a strong international record in applied research that is highly relevant to industry; e.g. oil & gas, satellite incident management, medical decision support and sensor technologies, pharmaceutical product design, on-line recommenders, and tourism information systems. Group members’ interests are in the fields of: machine learning, deep learning, case-based reasoning, data and text mining, social media mining, sentiment analysis, recommender systems, digital health and wearable sensors.
- Machine Learning
- Deep Learning
- Case-Based Reasoning
- Data, Text and Web Mining
- Digital Health and Wearable Sensors
- Sentiment Analysis and Data Analytics
- Information Retrieval and Recommender Systems
- Selfback: Decision support system to improve self-management of low back pain
- FitSense: Fall prediction in technology-enabled ‘FIT Homes’
- Personalised Recommender for Self-Management of Diabetes during Exercise
- Patient Support System for Inflammatory Bowel Disease
- PROEML: a Mark-Up Language for Online Patient Data Collection
- Actionable Insights for Effective Customer Experience Management
- Semantic Information Retrieval in Geoscience Domain
- Adaptive Engineer Site Support System
- SocialSensor: Sensing user generated input for improved media discovery and experience (EU FP7)
- Smart Beacons (Horizon Smart Tourism with Museums Galleries Scotland & Neatebox)
- Living History (Horizon Smart Tourism with Historic Scotland & AmbieSense)
- AHRC - STA(r) Accessing Implicit Knowledge of Textiles and Design (AHRC with Heriot-Watt & Johnstons)
- Survey response data analytics (KTP with Pexel Ltd)
- Classification and sentiment analysis of #ACC tweets (project with Aberdeen city council)
- Audio Feature Extraction for Music Recommendation
- Textual Revision Applications in NLG and Textual CBR (NRP with Aberdeen University)
- Knowledge Acquisition for Textual CBR (UKIERI with IIT Chennai and IBM India)
- Project Planning for Well Engineering (KTP with XCD)
- CBR for Remote Patient Health-Care Monitoring (KTP with AxSys Ltd)
- CBR for Anomaly Report Processing (with European Space Agency)
- Recipe recommendation with Food nutrient analytics (with EatBalanced)
- Hybrid User Profiling and Adaptation (NRP with Yahoo!)
- Context-sensitive High-order Language Model (RSE/NSFC with Tianjin)
Professor Craw’s research in Artificial Intelligence develops innovative data mining technologies to discover knowledge to embed in case-based reasoning, recommender, and other intelligent systems. Her recent research develops smart information systems that allow intelligent interaction and engagement with information, including recommendation of on-line music, browsing textile archives, and context-aware interpretations for tourist locations.
Susan is a Research Professor in Case-Based Reasoning, Data/Text Mining, Knowledge Discovery
Professor Nirmalie Wiratunga
Professor Wiratunga currently leads the AI research theme. Her research interests include both theoretical and practical aspects of machine learning with focus on Case-based Reasoning (CBR), Text Mining and Machine Learning. Her recent projects include: an EU H2020 Selfback project on mHealth and wearables; InnovateUK projects on Sentiment Analysis for Product recommendation, Diabetes Self-Management, Knowledge Discovery from ePatient Records and industry funded projects with BT and British Geological Survey.
Professor in Computing. Her research interests are in knowledge discovery from data and text and Case-based Reasoning (CBR) Systems
Rob has a background in mathematics and physics and has applied mathematics to a wide range of problems. His main research area is applications involving natural language processing, with a recent focus on sentiment analysis. He also has a broad interest in machine learning and data science.
Research interest focuses on Textual Case-based Reasoning and related areas.
Dr Stewart Massie
Dr Stewart Massie has more than 10 years research experience in Artificial Intelligence developing improved machine learning, information retrieval and data mining technologies with a focus on the application of introspective and other learning techniques. His main expertise includes case-based reasoning and recommendation, with recent funded projects developing health and tourism applications.
Active researcher in Case-Based Reasoning, Data/Text Mining, Knowledge Discovery and Personalisation
Dr Sadiq Sani
Dr Sani is a Research Fellow who specialises in machine learning, case-based reasoning and natural language processing. He has experience working on diverse projects, from search personalisation to computer vision and more recently, human activity recognition. Sadiq presently works on the SelfBACK project, developing activity recognition algorithms to improve self-management of low back pain.
Dr. Anil specializes in data science, natural language processing, machine learning, and statistics. He has research experience in the areas of sentiment analysis, emotion analysis, search, and personalization. Anil is currently working with a start-up, Sentisum, where he is applying natural language processing, machine/deep learning to extract insights from real-world customer experience data.
Researcher in Data Sensor Analytics
- Jeremie Clos
- Kyle Martin
- Blessing Mbipom
- Yoke Yie Chen
- Ikechukwu Nkisi-Orji
- Anjana Wijekoon