Richard
Allmendinger
Senior Lecturer in Decision Sciences
What are your research interests?
Development and application of decision sciences and AI methods to real-world problems arising in areas such as manufacturing, healthcare, management, forensics, sports and music.
What is the focus of your current research?
I have many different projects ongoing; here are some topics that I am currently supervising in form of a PhD project or research project:
Bayesian optimisation for problems with dynamic resourcing safety constraints; sonifying multi-objective optimization algorithms; mathematical models and efficient algorithms for optimizing development and delivery of personalised medicine; fairer reimbursement systems for burn care services; explainable AI for forensics; optimization methods for synthetic data generation.
What are some projects or breakthroughs you wish to highlight?
Thanks to working closely with engineers and scientists, I have introduced new problem challenges (motivated and formally defined them, and had a first go at tackling them) to the area of resource-constrained optimisation, such as dynamic resourcing constraints (I coined the term ephemeral resource constraints), multi-objective problems with varying per-objective evaluation times, and problems with changing decision variables.
What memberships and awards do you hold/have you held in the past?
- Alan Turing Fellow, since 2021
Editorial Board Member of several high-impact journals in the area of applied AI and optimisation, since 2016
Vice-Chair, IEEE Bioinformatics and Bioengineering Technical Committee, since 2021
Member, CBI North West Future Leaders Forum, since 2020
Member of several societies, eg IEEE, ACM, MCDM
What is the biggest challenge in Data Science and AI right now?
There are several, eg (i) the need for AI and data scientists to work more closely with practitioners and end users in order to tackle problems of relevance, (ii) to develop algorithms able to generate accurate, explainable, trustworthy, unbiased, and safe outputs, and (iii) ethics of AI.
What real world challenges do you see Data Science and AI meeting in the next 25 years?
Generally, a much better understanding about the benefits, weaknesses, and dangers of AI. AI will hopefully be used more responsibly in real-world to drive various applications, from autonomous driving to interactive decision making (Industry 5.0 and beyond).