Army Cadet Force

Officer

Was the Commandant of Sussex Army Cadet Force. Reduced through all the ranks to Lieutenant (now Acting Captain) and am an additional Detachment Officer.

Previous ACF service history is:

Additional Detachment Officer as an ACF Acting Captain (2014-15);
Reversion to ACF Acting Captain (2014);
Temporary acting rank of ACF Acting Major to lead international exchange (2014);
Additional Detachment Officer as an ACF Lieutenant (2013-14);
Voluntary reduction in rank to ACF Lieutenant (2013);
ACF Captain: HQ, Sussex Army Cadet Force (2012-13);
Voluntary reduction in rank to Acting Major and then Acting Captain (2012);
Supernumerary Acting Lieutenant Colonel wearing rank of Colonel (2011);
Cadet Commandant (2010).

British Army

Assistant Director Manning (Reserves)

Colonel in the Army Reserves and currently serving as Assistant Director Manning (Reserves).

Paid voluntary work.

As a Colonel, I have served as NATO Chief Coalition Logistics in Afghanistan, Deputy Brigade Commander for 104 Log Sp Bde and 2 Infantry Brigade (Now 2 (SE) Bde) and as Commander 2 Logistic Support Group. As a Lieutenant Colonel I served as CO, 165 Port regiment, SO1 ROCC, Defence Academy and as a Senior DS, Joint Services Command and Staff College.

British Army

Associate Head of Manning in the Army

Colonel in the Army Reserves and currently serving as Assistant Head of Manning for the Army.

Paid voluntary work.

As a Colonel in the Army Reserve.

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Technical Director

The company provides a range of software products and services and has been involved in several European Commission projects aimed at improving the competitiveness of small firms.

The company also provides expertise and advice on European Research & Development projects.

University of Portsmouth

Reader and Research Group Leader

Reader and Research Group Leader at the University of Portsmouth:

- PhD Supervisor;
- Member of Academic Council & Research Committees.

PCR

Managing Director

PCR is a small privately owned property company, consultancy and not-for-profit publisher.

Director since 1988 and currently Managing Director.

Consultancy & industrial collaboration has taken place with: KMP Ltd and Nautical Data Ltd, Ford Motor Company, Quest Enabling Designs Ltd, EffTech Ltd and SewTech Ltd, Anglian Water and Zeneca Bioproduct, PS Communications Ltd / BlueMT and Counterpoint MTC Ltd.

I am the first named inventor for US patents concerning biometric recognition titled “Recording writing movements” and second named inventor for UK patents for biometric recognition. The systems are now successfully being used to capture electronic signatures in Nationwide Building Society (UK) and in various US Banks.

Lions Clubs International

Trustee for Fareham Lions and Chair of the District Governors Advisory Committee.

Lions Clubs are a network of people who work together to answer the needs that challenge communities across the world.

Awarded a Melvin Jones Fellowship in 2005 by the LCI Foundation in recognition of commitment to humanitarian work and a Certificate of Merit in 2004 by the Manila District of the Foundation Business Association for training them in advanced business communication techniques.

Rotary International in Great Britain & Ireland

Voluntary Worker and committee member.

Awarded a Paul Harris Fellowship by Fareham Rotary Club for high professional and personal standards.

Inferring Learning Style from the Way Students Interact with a Computer User Interface and the WWW

IEEE Transactions on Education

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity, and recent significant advances in the research are described.

Inferring Learning Style from the Way Students Interact with a Computer User Interface and the WWW

IEEE Transactions on Education

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity, and recent significant advances in the research are described.

A caught in the act filter to assist in using the Internet

Proceedings of the 4th International Conference on Information Communication Technologies in Education

Inferring Learning Style from the Way Students Interact with a Computer User Interface and the WWW

IEEE Transactions on Education

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity, and recent significant advances in the research are described.

A caught in the act filter to assist in using the Internet

Proceedings of the 4th International Conference on Information Communication Technologies in Education

Intelligent browser-based systems to assist Internet users

IEEE Transactions on Education

New client-based systems that filter Web pages, infer user learning styles, and recommend relevant pages are described. The systems provide easy, structured, focused, and controlled access to the Internet. A first system, called iLessons, is embedded within Microsoft Internet Explorer 6 and provides teachers with tools to create lesson Web pages, define zones of the Internet that can be accessed during a lesson, and enforce these settings in a set of computers. A second system enables students to investigate and collaborate using the Internet. The system filters Web pages based on the relevance of their contents and assists students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity.

Inferring Learning Style from the Way Students Interact with a Computer User Interface and the WWW

IEEE Transactions on Education

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity, and recent significant advances in the research are described.

A caught in the act filter to assist in using the Internet

Proceedings of the 4th International Conference on Information Communication Technologies in Education

Intelligent browser-based systems to assist Internet users

IEEE Transactions on Education

New client-based systems that filter Web pages, infer user learning styles, and recommend relevant pages are described. The systems provide easy, structured, focused, and controlled access to the Internet. A first system, called iLessons, is embedded within Microsoft Internet Explorer 6 and provides teachers with tools to create lesson Web pages, define zones of the Internet that can be accessed during a lesson, and enforce these settings in a set of computers. A second system enables students to investigate and collaborate using the Internet. The system filters Web pages based on the relevance of their contents and assists students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity.

Inferring Learning Style from the Way Students Interact with a Computer User Interface and the WWW

IEEE Transactions on Education

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity, and recent significant advances in the research are described.

A caught in the act filter to assist in using the Internet

Proceedings of the 4th International Conference on Information Communication Technologies in Education

Intelligent browser-based systems to assist Internet users

IEEE Transactions on Education

New client-based systems that filter Web pages, infer user learning styles, and recommend relevant pages are described. The systems provide easy, structured, focused, and controlled access to the Internet. A first system, called iLessons, is embedded within Microsoft Internet Explorer 6 and provides teachers with tools to create lesson Web pages, define zones of the Internet that can be accessed during a lesson, and enforce these settings in a set of computers. A second system enables students to investigate and collaborate using the Internet. The system filters Web pages based on the relevance of their contents and assists students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity.