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Dr Kulvinder Panesar

Lecturer-Applied Artificial Intelligence

Faculty/Dept/School Department of Media, Design and Technology
(Faculty of Engineering and Informatics)


Dr Kulvinder Panesar has been appointed as Lecturer to strengthen the Applied Artificial Intelli­gence programme team and related teaching and research activities.

Kulvinder worked previously as a Senior Lecturer in Computer Science at York St University.  She has been an academic for over twenty years, and a strategically focused senior computing professional wearing different hats including programmer, research scientist, computational linguistic, software and website developer, database designer and developer, systems analyst, project manager and technical consultant.    

Her research interest is NLP (Natural Language Processing) in AI (Artificial Intelligence), and meaning and knowledge representation (KR) in conversational software agents (CSAs) and conversational AI. 

Her teaching interests are in the branches of NLP (statistical and lingustic (semantic), computer vision (object detection), data mining, analytics (databases to AI), intelligents agents and knowledge representation and reasoning.  

Kulvinder is a MBCS member of the British Computer Society. STEM ambassasor,  and an AI ambassdor for AI Tech North and WeAreTechWomen100 - 2019 winner listed  awarded by J P Morgan.  


Teaching interests

Teaching related:

  1. AI  Project Design and Development
  2. Machine Learning Methods and Models
  3. Data Science for AI
  4. Discipline-specific AI Project
  5. The Applied AI Professional
  6. Industrial AI Project

Research related: 

  1. Natural Language Processing (NLP) in AI 
  2. Conversational AI
  3. Intelligent Agents
  4. Conversational Software Agent Development
  5. Computational Linguistics
  6. Knowledge Representation and Reasoning (KRR)


Dr Kulvinder's  research interest is Natural Language Processing (NLP) in AI (Artificial Intelligence), and meaning and knowledge representation (KR) in conversational software agents (CSAs) and conversational AI.    

This research area is multi-disciplinary spanning AI, data science, agent thinking, linguistics, computational linguistics, NLP, KR, and the Semantic Web.  She has conceptually designed and developed  a linguistically text based conversational software agent (LING-CSA) framework, addressing the integration, intersection, and interface of language, knowledge, and speech act constructions (SAC).  LING-CSA is a Java based prototype developed in Eclipse. 

Her research contributions included: (i) extending the theoretical and computational adequacy of the linguistic theory - Role and Reference Grammar (RRG); (ii) integrating the RRG language model  with concept of speech act constructions (SAC) as the linguistic engine; (iii) motivating an agent framework  intersecting with the  linguistic engine,  an agent cognitive and dialogue model to facilitate conversation implemented as a proof-of-concept; (4) insights into the language/knowledge representation interface.

Current research interest involves demystifying statistical vs lingustic NLP for conversational software agents, with the goal to investigating linguistic NLP with statistical  NLP learning and support – for forward-thinking  hybrid approach to conversational AI.