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Dr Funmi Obembe

Job: Senior Lecturer in Information Systems

Faculty: Technology

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: http://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.

She 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, open data, data analytics, data science, knowledge management and machine learning/deep learning algorithms for knowledge representations.

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.
  • 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.

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

Research interests/expertise

Big data analytics

Data science

Digital health 

Health data

Open data

Data warehousing (including parallel and distributed warehousing)

Correlations between data and knowledge management

Knowledge engineering and expert systems

Graph modelling and knowledge representation

Areas of teaching

Big Data/Big Data Analytics

Project Management

IT Service Management

Qualifications

PhD in Computer Science (Aston University)

MSc Advanced Computing (Imperial College)

PGD Advanced Computing (Imperial College)

BEng Electronics and Software Engineering (University of Leicester)

Courses taught

Postgraduate: Big Data Analytics (Module Leader)

3rd Year Undergraduate: Project Management (Tutor)

2nd Year Undergraduate: IT Service Management (Tutor)

Membership of professional associations and societies

Associate Fellow of the Higher Education Academy (AFHEA)

Professional Member of Association for Computing Machinery (ACM)

PRINCE2 Practitioner

AgilePM Foundation

Microsoft Certified Technology Specialist

Microsoft Certified Professional

Sun Certified Programmer for the Java 2 Platform

QlikView Designer Diploma

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