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Dr Funmi Obembe

Job: Senior Lecturer in Information Systems

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: 0116 201 3871

E: funmi.obembe@dmu.ac.uk

W: https://www.dmu.ac.uk

Social Media: https://www.linkedin.com/in/funmi-obembe/

 

Personal profile

Funmi Obembe is a Senior Lecturer in Information Systems in the De Montfort University School of Computer Science and Informatics. Funmi is also Programme Leader for Business Data Analytics, Business Information Systems, Computing for Business and Information & Communication Technology.

Prior to transitioning into full time academia, Funmi has over 18 years’ experience working in industry and has in-depth knowledge of data analytics, warehousing, and processing and has carried out research with various technologies such as SQL Server, Microsoft Analytics Platform System, and Apache Spark.

She is a passionate teacher with in-depth technical experience and knowledge, adept at bringing together in teaching and research both theoretical aspects of Computing and their practical applications.

Her research interests are in technology enhanced learning, data science, big data analytics,  open data, data and digital transformation, knowledge management and machine learning/deep learning algorithms for knowledge representations.

Funmi is also Commercial and Industry outreach lead for the Centre for Computing and Social Responsibility (CCSR) and Career Chair for ACM-W UK.

To discuss PhD opportuntities please email Dr Obembe (See below for research interests/expertise)

Research group affiliations

Centre for Computing & Social Responsibility

Publications and outputs

  • An Open Data Academic Portal: A Preliminary Study
    An Open Data Academic Portal: A Preliminary Study Obembe, Funmi The use of appropriate data in education is crucial and the current deluge of Open data portals provide numerous opportunities to marry the right datasets with relevant research and teaching. However, for academics finding the right datasets can be challenging, this is the gap that the Open Data Academic Portal (ODAP) looks to bridge. A data portal that points to numerous datasets available in various Open data portals whilst also classifying and grouping them into datasets based on discipline areas and research type categories. For instance, categories such as machine learning, data analytics and further subcategories under these (such as collaborative filtering under machine learning) would be provided. The grouping and matching of datasets from various portals will be done using algorithms developed to harvest meta data from data portals and categorise and classify them using information available in these files. For instance, for systems running CKAN (the standard open source software for Open Data Portals) the algorithm will make use of the tags and group fields in the meta data for the various datasets. The expected outcomes are the provision of an application that provides a means of easy access to relevant data for use in academia for effective teaching and research using real world data. This would also facilitate results that are of benefit not just in the academic sphere but also in society as a whole.
  • Deep Learning and Tacit Knowledge Transfer – An Exploratory Study
    Deep Learning and Tacit Knowledge Transfer – An Exploratory Study Obembe, Funmi; Obembe, Demola In 1966, Michael Polanyi wrote his seminal piece on the ‘tacitness’ of knowledge, essentially bringing to the fore, the non-codifiability of knowledge and the possibility for individuals to know more than they are able to express. Nearly thirty years later Nonaka and Takeuchi (1995) popularised the possibility for knowledge conversion between the tacit and explicit dimensions of knowledge. They proposed that organisations are able to create knowledge through a spiral of interactions between socialisation, externalisation, combination and internalisation of knowledge. Since then, various attempts have been made to develop mechanisms for codifying tacit knowledge including; storytelling, modelling, and more recently, various artificial intelligence/machine learning algorithms. In this study we examine the use of deep learning for representing, codifying and eventually transferring tacit knowledge. We draw on existing research on the role of artificial intelligence in Knowledge Management as well as current works on Deep learning to explore the potential role that deep learning can play in the learning, representation and transfer of tacit knowledge. Deep learning, as a subset of machine learning in artificial intelligence which provides algorithms that mimic the way the brain works and offers significant prospects for knowledge externalisation. Specifically, it can provide a means for representing knowledge in a different manner to human representation. This alternative machine representation is premised on the notion that if tacit knowledge can be learned and represented in a way that can then be codified, the knowledge modelled in such a way is then transferable. Arguably, where deep learning is able to capture and represent tacit knowledge, the ability for knowledge to be codified and externalised will increase exponentially and invariably constitute a significant breakthrough in the ability for both individuals and organisations to access and combine existing knowledge as well as to create new knowledge.
  • The Impact of Digital Transformation on Knowledge Management During COVID-19
    The Impact of Digital Transformation on Knowledge Management During COVID-19 Obembe, Funmi; Obembe, Demola Technology and digital transformations are increasingly important in today’s world. The COVID-19 pandemic that the entire world has been grappling with for the last year has made this even more so. The speed at which different organisations across various sectors have had to embrace digital transformations has been unprecedented. In some sectors this has been driven by the need to simply survive during this pandemic. However, beyond just responding to crisis, digital transformation, and the use of data to drive it has over the years brought about disruptions which have led to great innovations and progress. In many instances these innovations have not only been driven by digital transformations but by a merging of digital transformations and intelligent/adaptive knowledge management systems that have arisen from it. Even before the emergence of the pandemic, digital transformations, AI, machine learning techniques and various innovative technologies had started to be used to design intelligent and adaptive knowledge management systems. COVID-19 has greatly accelerated the uptake of these technologies across a wide range of sectors. Organisations that would successfully navigate these times and be ready for the future need their knowledge management systems to be intelligent and highly adaptive. Digital transformations and innovative technologies are increasingly making this possible. In this work in progress paper, we start to explore the impact of digital transformation and innovative technologies on organisations’ knowledge management systems and the changes in the factors that contribute to whether organisations adopt these innovative technologies/digital transformations in times of crisis such as during the COVID-19 pandemic. Knowledge management systems that can respond to inevitable changes that arise in crisis situations such as COVID-19 are invaluable. These systems are positioned to naturally produce actionable intelligence resulting in competitive advantage.
  • Developing a Probabilistic Graphical Structure from a Model of Mental-Health Clinical Risk Expertise
    Developing a Probabilistic Graphical Structure from a Model of Mental-Health Clinical Risk Expertise Obembe, Funmi; Buckingham, Christopher D This paper explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. The Galatean Risk Screening Tool [1] is a psychological model for mental health risk assessment based on fuzzy sets. This paper details how the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. These semantics are formalised by a detailed specification for an XML structure used to represent the expertise. The component parts were then mapped to equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements.
  • Graphical Modelling in Mental Health Risk Assessments
    Graphical Modelling in Mental Health Risk Assessments Obembe, Funmi; Buckingham, Christopher D Probabilistic models can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty. In this paper, we present the development of a chain graph for assessing the risks associated with mental health problems, which is a domain that has high amounts of inherent uncertainty. The Galatean mental health Risk and Social care Tool, GRiST, has been developed to support mental-health risk assessments by using a psychological model to represent the expertise of mental-health practitioners. It is a hierarchical knowledge structure based on fuzzy sets for reasoning with uncertainty. This paper describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise.
  • Covid-19 and the tourism industry: an early-stage sentiment analysis of the impact of social media and stakeholder communication
    Covid-19 and the tourism industry: an early-stage sentiment analysis of the impact of social media and stakeholder communication Obembe, Demola; Kolade, Oluwaseun; Obembe, Funmi; Owoseni, Adebowale; Mafimisebi, Oluwasoye This paper examines tourist public responses to crisis communications during the early stages of Covid-19. Using the social-mediated crisis communication model, the paper explores the key factors that influence public sentiments during nascent periods of the crisis. The choice of data collection dates was determined by key milestones events with significant implications in relation to UK tourism. Sentiment analysis of data sets of public tweets and news articles were done in order to interrogate how the trends and performance of the airlines and the tourism sector have been shaped by the sentiments of the tourism publics, the crisis communication interventions from key institutional actors, and the news sentiments about tourism organizations, particularly airlines. Sentiment analysis, also known as opinion mining, falls under natural language processing (NLP) and is used to identify different sentiments and polarities in texts. Our findings indicate that institutional actors have a significant impact on the sentiments of tourism publics. Our study contributes to existing research on crisis communication by illuminating how public narrative about, and stakeholder responses to, crisis are shaped not just by organizational communication strategies but also institutional actors, on the one hand, and the interested publics too. open access journal

Click here to view a full listing of Funmi Obembe's publications and outputs.

Key research outputs

See sections on Projects and Conferences

Research interests/expertise

PhD applications in the areas below are welcome. For more information please email Dr Obembe.

Big data analytics

Data science

Open data

Correlations between data and knowledge management

Data and digital transformation

Graph modelling and knowledge representation

Technology enhanced learning

Areas of teaching

Big Data/Big Data Analytics

Project Management

IT Service Management

Database Systems and Design

Qualifications

PhD in Computer Science (Aston University)

MSc Advanced Computing (Imperial College)

PGDip Advanced Computing (Imperial College)

BEng(Hons) Electronics and Software Engineering (University of Leicester)

Courses taught

Postgraduate: Big Data Analytics (Module Leader)

Database Systems and Design (Module Leader)

3rd Year Undergraduate: Project Management (Tutor)

3rd Year Undergraduate: Final Year Project (Module Leader)

2nd Year Undergraduate: IT Service Management (Tutor)

Membership of external committees

 Career Chair, ACM-W UK (2020 - till date)

Membership of professional associations and societies

Fellow of the Higher Education Academy (FHEA)

Professional Member of Association for Computing Machinery (ACM) 

Professional licences and certificates

PRINCE2 Practitioner

AgilePM Foundation

Microsoft Certified Technology Specialist

Microsoft Certified Professional

Sun Certified Programmer for the Java 2 Platform

QlikView Designer Diploma

Projects

Development of an Open Data Academic Portal Repository (ODAP)

RE-skilling Businesses for new OpportUNities in the Covid-19 lanDscape (REBOUND)

Centre for Computing and Social Responsibility Commercial Outreach Project (CCSR-CO)

Forthcoming events

See section on Organised Events

Conference attendance

Obembe, F. and Obembe, D. (2021) ‘The Impact of Digital Transformation on Knowledge Management During COVID-19’, European Conference on Knowledge Management, University of Coventry, 2-3 September.

Obembe, F. and Obembe, D. (2020) ‘Deep Learning and Tacit Knowledge Transfer: An Exploratory Study’, European Conference on Knowledge Management, University of Coventry, 3-4 December. 

Obembe, F. (2020) ‘Promoting the use of gamification and digital tools to develop online learning communities and enhance student engagement’, Teaching, learning and personal tutoring: A virtual mini-conference, De Montfort University Centre for Academic Innovation, 11th September.

Obembe, F. (2020) ‘An Open Data Academic Portal: A Preliminary Study’, The 6th IEEE International Conference on Information Management, Imperial College, 27-29 March.

Obembe O., Buckingham C.D. (2010) Developing a Probabilistic Graphical Structure from a Model of Mental-Health Clinical Risk Expertise. In: Setchi R., Jordanov I., Howlett R.J., Jain L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science, vol 6279. Springer, Berlin, Heidelberg.

Obembe O., Buckingham C.D. (2010) Graphical Modelling in Mental Health Risk Assessments. COGNITIVE 2010: The Second International Conference on Advanced Cognitive Technologies and Applications, pp 72-77.

Recent research outputs

See sections on Projects and Conferences

Key articles information

See sections on Projects and Conferences

Internally funded research project information

Principal Investigator: The development of an Open Data Academic Portal (ODAP) (2020-2021). CEM Internal Fund. Value: £2,925

Principal Investigator: CCSR Commercial Outreach (CCSR-CO) (2020-2021). Higher Education Innovation Fund (HEIF). Value: £4,340

Principal Investigator: The use of gamification as an innovative practice pedagogy to enhance student engagement and outcomes in a covid-19 era (2021). CEM Teaching Innovations Fund. Value: £250

Co-Investigator: RE-skilling Businesses for new OpportUNities in the Covid-19 lanDscape (REBOUND) (2020-2021). Higher Education Innovation Fund (HEIF) Knowledge Exchange Grant (+ CEI support). Value: £8,750

Professional esteem indicators

External Examiner - MSc Computing and MSc Computing & IT Management, Cardiff University, Sept 2019 - till date

Career Chair, ACM-W UK, Sept 2020 - till date

ORCID number

0000-0003-3375-9147

Organised events

Organiser of ACM-W UK  Careers in Tech Webinar Series (5th Feb, 5th March and 30th April 2021)

A Day in the Life of a Data Professional (26th May 2021)

Talks/ Outreach

Talk on Advantages of using Gamification – 28th Jun 2021 (Polish-Japanese Academy of Information Technology)

Talk on Communication for CPD Day for Applied A-Level Business Students  - 16th Dec 2020 (USP College)

Talk on Professions of the future: The Data Professional – 11th Dec 2020 (DMU in collaboration with egitimAL, Ankara Turkey) 

Talk on Professions of the future: The Data Professional – 24th Nov 2020 (DMU International office outreach event on Data Science and Technology - Russian International Schools)

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