Keywords

• Type 1 Diabetes
• Congenital Hyperinsulinism
• Insulin Dependant Type 2 Diabetes
• Profound Blindness
• Parkinson's Disease
• Human Computer Interaction (HCI / CHI)
• Human Computer Systems
• Web Accessibility
• World Wide Web

• ACM: http://dl.acm.org/author_page.cfm?id=81100139139
• Google: http://scholar.google.co.uk/citations?user=SQEdResAAAAJ
• ORC-ID: http://orcid.org/0000-0001-9301-5049
• Research Gate: https://www.researchgate.net/profile/Simon_Harper/

Human Computer Systems Group
Interaction, Analysis, and Modelling Lab
Data Science Institute

ACM President Alexander Wolf described my work as having “singular impacts on the vital field of computing” and my “achievements have had a significant influence on the social, economic and cultural areas of daily lives all over the world”.  And commended as “instrumental to the development of the Web” by Sir Tim Berners Lee.

Metrics (2025) for my full career can be summarised as follows: Papers - over 189: Citations - 6458: h-index - 43: i10-index - 127. Ranking: Both Google Scholar and Microsoft Research rank me in the top 5% for HCI and WWW; while Scholarometer ranks me 98th for HCI (internationally) using their career and domain normalized hs-index measure (based on Google data).  I have been successful in 26 grants since 2007 resulting in total funding over five years of £1.40 Million. In addition, I have 14 externally funded PGRs (of a 37 total). Finally, ACM Bibliometrics indicate the scope and influence my work has and of the 126 papers available for download I have achieved: 47,131 cumulative downloads with an average downloads per article of 524.

I am a Fellow of the British Computer Soc., a Fellow of the Institute of Engineering Technology, and an Association of Computing Machinery Distinguished Scientist.  I was the Chair of the ACM (largest Professional CS Organization in the world) SIGWeb for 4 years, 2 years as development office and 2 years as Chair of the Reproducibility and Replication Interest group. I was the Chair of the W3C (Standards Body for the Web) for 5 years making substantial contributions to Web standards and technology.

I am Associate Editor of IJHCS (Elsevier), Transactions on the Web (ACM), and PLOS One. I am a Reviewer for the Natural Sciences and Engineering Research Council of Canada (2007-present); Responsive Mode Panel Member for the UK Engineering and Physical Science Research Council (EPSRC), (2012-present); Reviewer for the NWO, the Dutch National Research Council, to review for grants in Computer Science (2006-Present).

My most recent public contributions are with 60 citizen scientist beekeepers involved in honey sampling of their hives which we then analyze for heavy metal pollutants and used to form a bee-sourced pollution map of Greater Manchester. These maps form part of the collection at the Manchester Museum of Science and Industry (MOSI) evolving from the ‘Bee in the City’ showcase and a paper in Elsevier ‘Environmental Advances’ in collaboration with DAL University was published in 2024 - Jillian Shaw et al. “Biomonitoring of honey metal (loid) pollution in Northwest England by citizen scientists”. In: Environmental Advances 13 (2023), p. 100406. - accompanied by a widely read “The Conversation” article 

I view public and community involvement is very important and have conducted outreach lectures at both Cafe Scientifique and Pint of Science on my research. My most recent public contributions are with 60 citizen scientist beekeepers involved in honey sampling of their hives which we then analyse for heavy metal pollutants and use to form a bee-sourced pollution map of Greater Manchester. These maps and summaries will (post COVID 19) form part of the collection at the Manchester Museum of Science and Industry (MOSI) evolving from the `Bee in the City' showcase we presented with our colleagues in Electrical Engineering. I also make a number of press releases via the central University machinery, netting over eighty news stories in the national and local press,  including BBC news interviews, MEN stories, and RNIB radio promotion of my work. Further, I have direct and sustained outreach to the public via links with Manchester City Art Gallery, The Whitworth, and National Museums Liverpool (all three via funded outreach projects), and via various science events and fares.

I have conducted many industrial talks, most recently to PricewaterhouseCoopers (PWC), Pro-Manchester, Nomura Bank, and IBM TRL. Further,  my work requires Public and Patient Involvement involving interaction with the public-at-large for data collection, however I can use these interactions to humanise and 'open', what can seem like, `distant' and elitist work. Indeed, I have direct and sustained relationships with (1) ChangePeople; (2) Henshaws Society for Blind People (HSBP); (3) Deaf Blind Services (Walthew House); (4) Macclesfield Eye Society; (5) Access SUMMIT; (6) the UoM Disability Support Office; (7) Age Concern (Trafford); and the (8) Christopher Grange Rehabilitation Centre; as well as (9) the Disability Information Bureau, enable me to engage with sectors of the general public often forgotten, ignored or excluded.

Over my career at Manchester:

• Years of Teaching: 24 (2001-present)
• Courses Taught: 4 major courses as sole/lead lecturer
• Students Taught: 3,000+ undergraduate students
• PhDs Supervised: 26 completed, 13 current
• Programme Leadership: 13 years directing CS(HCI)
• External Examining: 18 years across 2 institutions

My teaching has been recognized through:

• Consistently high student satisfaction scores (4.60-4.70 vs 3.77 dept average)
• Published teaching scholarship in premier venues (CHI)
• Faculty 4/4 teaching assessment
• External examiner appointments
• Adoption of teaching innovations by colleagues
• National consultation on programme design

GTA Lead for Computer Science (Current)

• Initiated comprehensive GTA reform
• Created CS-specific GTA training
• Implemented GTA wellbeing events
• Reformed allocation process
• Added praise mechanisms for references

School of Engineering GTA Lead (Current)

• Deputy for Head of School Education on GTA matters
• Instigated SoE GTA Open Forum

Director of CS(HCI) (2012-present)

    Programme director for the interdisciplinary HCI undergraduate programme

UG Applicants Tutor (2012-2017)

• Created concept of making applicants feel like “our students” before arrival
• ~1,000 communications with ~600 applicants per year
• Coincided with marked increase in student acceptance

Faculty & University Roles

• Learning Enhancement Officer (2012-2015)
• Teaching Innovation Group (2012-2015)
• G7 Curriculum Review Committee (2005/2006)

Push Feedback System

My most significant teaching innovation is the “Push Feedback” system – a direct personal email feedback approach that has transformed student engagement. This system:

    Delivers timely, personalised feedback directly to students
    Reduces friction in accessing feedback
    Creates two-way communication channels
    Has increased feedback scores from ~3.8 to 4.70

This innovation has been adopted by colleagues and featured in teaching practice discussions.

Open Educational Resources

I have created extensive open educational resources:

    YouTube lectures: 2,368 views, 14,495 minutes watched
    Slideshare presentations: 11,000 views
    Custom textbook: Using LeanPub model for continuous improvement
    All materials openly accessible

Published Teaching Scholarship

Harper, S. (2016). “The User Experience in Zen and the Art of Motorcycle Maintenance”. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 317–327. https://doi.org/10.1145/2851581.2892566

This work addresses teaching UX/HCI concepts to students encountering the domain for the first time.

SIMON HARPER - POSTGRADUATE RESEARCH PROJECTS University of Manchester, Department of Computer Science

PROJECT 1: Diabetes Tamagotchi's for Training Clinical Endocrinologists and Diabetologists

Primary Supervisor: Simon Harper Project ID: 38001 Funding: Competition Funded Project (Students Worldwide)

Description: Clinicians working in Type 1 Diabetes do not often have a real-world appreciation of the difficulties of staying in the healthy blood glucose range that people with diabetes have to maintain throughout the day. This project investigates if combining simulation, adaptive systems, and glucose monitoring formed into a Tamagotchi-like device can help clinicians appreciate the difficulties experienced by people with diabetes as part of their training.

You will gain an understanding of the models required to drive Type 1 diabetes in a physiological and clinical setting, user modelling, and adaptive and personalised systems. Type 1 Diabetes is an autoimmune disease that causes the insulin-producing beta cells in the pancreas to be destroyed, preventing the body from being able to produce enough insulin to regulate blood glucose levels adequately. Treatment with insulin is required for survival, and is usually given by injection just under the skin but can also be delivered by an insulin pump.

When left untreated, or when poorly controlled, these raised blood sugar levels can cause both microvascular and macrovascular damage. Essential to reducing these clinical risks is an understanding of how to accurately predict glucose concentration in the blood. You will contribute to broader Type 1 work by formulating studies, algorithms, and research prototypes to accurately predict blood glucose levels from the food we eat.

PROJECT 2: Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs

Primary Supervisor: Simon Harper Additional Supervisor: Paul Nutter Project ID: 35795 Funding: Competition Funded Project (Students Worldwide)

Related Publication: HYPO-CHEAT's aggregated weekly visualisations of risk reduce real-world hypoglycaemia (doi.org/10.1177/20552076221129712)

Description: Investigate if Artificial Intelligence/Machine Learning can beat our current Adaptive algorithms which identify repeated hypo/hyperglycaemia events in people with Type 1 Diabetes. Hypo-Cheat is groundbreaking research which helps to prevent lows in children with Congenital Hyperinsulinism. You will take this work forward and apply it to Type 1 Diabetes and extend this to look for highs too.

You will gain an understanding of AI/ML, the models required to drive them, Type 1 diabetes in a physiological and clinical setting, user modelling, and adaptive and personalised systems. However, you will not be building new AI/ML models but rather using those that exist to apply to solving this problem. You will therefore help people with Type 1 Diabetes better manage their condition. Better management reduces clinical risks.

Type 1 diabetes is an autoimmune disease that causes the insulin-producing beta cells in the pancreas to be destroyed, preventing the body from being able to produce enough insulin to regulate blood glucose levels adequately. When left untreated, or when poorly controlled, these raised blood sugar levels can cause both microvascular and macrovascular damage. This is a wicked problem, and there is, likely, no perfect solution—we are 'simply' trying to get as close as possible. In this case, you will contribute to broader Type 1 work by formulating studies, algorithms, and research prototypes to accurately predict blood glucose levels from the food we eat.

PROJECT 3: Generative Artificial Intelligence as a Personalised and Adaptive Bolus Advisor

Primary Supervisor: Simon Harper Project ID: 37920 Funding: Competition Funded Project (Students Worldwide)

Description: Investigate if generative Artificial Intelligence can give better real-time bolusing/meal advice to people with Type 1 Diabetes using their personal blood glucose readings than can strictly following the National Institute for Health and Care Excellence (NICE) and Dose Adjustment For Normal Eating (DAFNE) guidance.

This will mean that you need to focus your studies around linking blood glucose readings to health guidance, and training different generative models to help give advice that may not be NICE/DAFNE guidance but is more valid for this particular patient based on their own physiological reactions.

You will gain an understanding of generative AI, the models required to drive them, prompt engineering and analysis, Type 1 diabetes in a physiological and clinical setting, user modelling, and adaptive and personalised systems. However, you will not be building new AI/ML models but rather using those that exist to apply to solving this problem.

Type 1 Diabetes is an autoimmune disease that causes the insulin-producing beta cells in the pancreas to be destroyed, preventing the body from being able to produce enough insulin to regulate blood glucose levels adequately. When left untreated, or when poorly controlled, these raised blood sugar levels can cause both microvascular and macrovascular damage. Essential to reducing these clinical risks is an understanding of how to accurately predict glucose concentration in the blood. This is a wicked problem, and there is, likely, no perfect solution—we are 'simply' trying to get as close as possible. In this case, you will contribute to broader Type 1 work by formulating studies, algorithms, and research prototypes to accurately predict blood glucose levels from the food we eat.

PROJECT 4: Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics

Primary Supervisor: Simon Harper Additional Supervisor: Paul Nutter Project ID: 35777 Funding: Competition Funded Project (Students Worldwide)

Description: Help people with Type 1 Diabetes better manage their condition. Better management reduces clinical risks.

Type 1 diabetes is an autoimmune disease that causes the insulin-producing beta cells in the pancreas to be destroyed, preventing the body from being able to produce enough insulin to regulate blood glucose levels adequately. Treatment with insulin is required for survival, and is usually given by injection just under the skin but can also be delivered by an insulin pump.

When left untreated, or when poorly controlled, these raised blood sugar levels can cause both microvascular and macrovascular damage. This damage will eventually lead to deterioration in health and ultimately early death/or disability. Essential to reducing these clinical risks is an understanding of how to accurately predict glucose concentration in the blood. This concentration is related to the food consumed and the efficiency of the body at metabolising this food. This is a wicked problem, and there is, likely, no perfect solution—we are 'simply' trying to get as close as possible. In this case, you will contribute to broader Type 1 work by formulating studies, algorithms, and research prototypes to accurately predict blood glucose levels from the food we eat.

You will investigate the effect of body temperature as it relates to physiological issues such as menstruation, illness along with the effects of climate and altitude to make more refined predictions as to variances in insulin requirements. These models will typically be adaptive systems which enable personalised outcomes.

PROJECT 5: OPTIMAL-EM: An AI-Driven Framework for Systematic Web Accessibility Evaluation

Primary Supervisor: Simon Harper Additional Supervisor: Yeliz Yesilada Project ID: 38080 Funding: Competition Funded Project (Students Worldwide)

Related Publications: • OPTIMAL-EM: A Software Tool for Optimised Web Accessibility Evaluation (doi.org/10.5281/zenodo.14238535) • OPTIMAL-EM: Accessibility and Complexity Analysis Pipeline (doi.org/10.5281/zenodo.14197633) • Optimising web accessibility evaluation: Population sourcing methods (doi.org/10.1016/j.ijhcs.2025.103472) • OPTIMAL-EM: Optimised Population Sourcing for Web Accessibility Evaluation (doi.org/10.1145/3587281.3587962) • Web Structure Derived Clustering for Optimised Web Accessibility Evaluation (doi.org/10.1145/3543507.3583508)

Description: Web accessibility means people with varying capabilities can easily access web pages and applications. Website Accessibility Conformance Evaluation Methodology (WCAG-EM) is a de-facto methodology for evaluating accessibility. OPTIMAL-EM aims to address the limitations of this methodology by optimising the sampling of web pages to provide a scientific approach to the evaluation process.

OPTIMAL-EM comprises six key metrics: coverage, complexity, accessibility, representativeness, popularity, and freshness. It allows the sampling of web pages using artificial intelligence techniques, mainly unsupervised learning, to decide how to choose pages.

This PhD project aims to focus on researching and developing OPTIMAL-EM further, in particular by addressing the following:

1. Investigating the effects of the metrics such as popularity and freshness
2. Researching heuristics and parameter tuning techniques for unsupervised learning algorithms for parameter selection on different websites
3. Developing an integrated application to support OPTIMAL-EM
4. Empirically validating OPTIMAL-EM

CONTACT INFORMATION

Simon Harper: [email protected] Admissions: [email protected] Phone: +44 (0)161 306 6000

APPLICATION REQUIREMENTS (ALL PROJECTS)

• Minimum upper Second Class UK Honours degree or international equivalent in relevant science/engineering discipline • Meet School's minimum English Language requirement • Comply with University's equality, diversity and inclusion policies • Capable of performing at a very high level • Self-driven interest in uncovering and solving unknown problems • Ability to work hard and creatively without constant supervision • Good time management • Determination

Apply: https://www.manchester.ac.uk/study/postgraduate-research/admissions/how-to-apply