Dr. Kulvinder Panesar

Biography

Dr Kulvinder Panesar was initially appointed as Lecturer to strengthen the Applied Artificial Intelli­gence programme team and related teaching and research activities. In August 2022, she was appointed to Assistant Professor of Applied Artificial Intelligence and has been involved in Natural Language Processing (NLP) Data Scientist related activities. 
Her teaching interests are in the branches of AI more  specifically NLP (statistical and linguistic - (semantic)), AI project design and development with project management, computer vision (object detection), data mining, analytics (databases to AI), multidisciplinary issues, innovations and ethics, intelligent agents, knowledge representation and employability/placement input.  
She has other roles such as Programme Lead of BSc (Hons) Applied AI, and Outreach, Marketing and Placement Lead of the Department of Media Design and Technology and academic reviewer.  She is also involved in cross faculty research projects and external university collaborative projects. Her  current  research project is motivated by the  grand challenges of healthcare, with the pre-screening of dementia via an intervention in the form of a conversational  agent hybridisation solution with a  person-centred design with a submitted application (ongoing review) with UKRI (EPRSC).   
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 PhD was titled ‘a linguistically centred text-based conversational software Agent’.  Her research interest is NLP (Natural Language Processing) in AI (Artificial Intelligence), meaning and knowledge representation (KR), conversational software agents (CSAs) and more recently conversational AI. 
Kulvinder is a MBCS member of the British Computer Society. STEM ambassador,  and an AI ambassador and Ethics Adviser for AI Tech North and WeAreTechWomen100 - 2019 winner listed  awarded by J P Morgan.  

Research

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 s  forward-thinking  hybrid approach to conversational AI.  To explore and apply this research the healthcare domain is selected. Here the focus on language and cognition in mild cognitive impairment and patients living with dementia is to be explored based on a   language phenomenon  and further explored for  automation as an intervention embeded into a  conversational agent with automatic speech recognition. 

Teaching

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

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)

Professional activities

Publications