Dr Adam Stanton

BSc, PhD

Information School

Lecturer in Data Science and AI

a.j.stanton@sheffield.ac.uk

The Wave

Full contact details

Dr Adam Stanton
Information School
The Wave
2 Whitham Road
Sheffield
S10 2AH

Profile: I joined the Information School as Lecturer in Data Science and AI in July 2024. My last position was as a Research Engineer at TUoS and the Alan Turing Institute, and before that I held lectureships in Computer Science at Aston University in Birmingham and at Keele University in Staffordshire. I have also been a director at Larchwood Research, an R&D consultancy company, since 2017.

Qualifications

BSc Artificial Intelligence and Computer Science (University of Birmingham)

PhD (University of Keele)

Research interests: My research background is in bioinspired autonomous systems, and I have contributed to interdisciplinary projects across biosciences, social sciences and arts and humanities research. I am interested in developing techniques for nature-inspired autonomy from the perspective of Artificial Life, including exploring the algorithmic nature of evolutionary processes. I am also keen to develop and apply DS/AI/ML approaches to improve outcomes in sustainability and health as well as enhance research taking place in other fields. I have other interests in sustainable computing and VR/XR for transcendental ritual.

Publications

Journal articles

Stanton A & Moore JM (2022) Lexicase Selection for Multi-Task Evolutionary Robotics. Artificial Life, 28(4), 479-498.
Andras P & Stanton A (2021) Where do successful populations originate from?. Journal of Theoretical Biology, 524, 110734-110734.
Stanton A, Harris LM, Graham G & Merrick CJ (2016) Recombination events among virulence genes in malaria parasites are associated with G-quadruplex-forming DNA motifs. BMC Genomics, 17(1).
Taylor T, Bedau M, Channon A, Ackley D, Banzhaf W, Beslon G, Dolson E, Froese T, Hickinbotham S, Ikegami T , McMullin B et al (2016) Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artificial Life, 22(3), 408-423.
Warmbrod KL, Patterson EI, Kautz TF, Stanton A, Rockx-Brouwer D, Kalveram BK, Khanipov K, Thangamani S, Fofanov Y & Forrester NL () Viral RNA-dependent RNA polymerase mutants display an altered mutation spectrum resulting in attenuation in both mosquito and vertebrate hosts. PLOS Pathogens, 15(4), e1007610-e1007610.
Claessens A, Harris LM, Stanojcic S, Chappell L, Stanton A, Kuk N, Veneziano-Broccia P, Sterkers Y, Rayner JC & Merrick CJ () RecQ helicases in the malaria parasite Plasmodium falciparum affect genome stability, gene expression patterns and DNA replication dynamics. PLOS Genetics, 14(7), e1007490-e1007490.

Conference proceedings papers

Moore JM & Stanton A (2021) Objective Sampling Strategies for Generalized Locomotion Behavior with Lexicase Selection. The 2021 Conference on Artificial Life, 19 July 2021 - 23 July 2021.
Moore JM & Stanton A (2020) When Specialists Transition to Generalists: Evolutionary Pressure in Lexicase Selection. The 2020 Conference on Artificial Life, 13 July 2020 - 18 July 2020.
Moore JM & Stanton A (2019) The Limits of Lexicase Selection in an Evolutionary Robotics Task. The 2019 Conference on Artificial Life, 29 July 2019 - 2 August 2019.
Moore JM & Stanton A (2018) Tiebreaks and Diversity: Isolating Effects in Lexicase Selection. The 2018 Conference on Artificial Life, 23 July 2018 - 27 July 2018.
Stanton A (2018) Stochastic Ontogenesis in Evolutionary Robotics. The 2018 Conference on Artificial Life, 23 July 2018 - 27 July 2018.
Moore JM & Stanton A (2017) Lexicase Selection Outperforms Previous Strategies for Incremental Evolution of Virtual Creature Controllers. FOURTEENTH EUROPEAN CONFERENCE ON ARTIFICIAL LIFE (ECAL 2017) (pp 290-297)
Stanton A & Channon A (2016) Neuroevolution of Feedback Control for Object Manipulation by 3D Agents. Proceedings of the Artificial Life Conference 2016, 4 July 2016 - 8 July 2016.
Stanton A & Channon A () Incremental Neuroevolution of Reactive and Deliberative 3D Agents. 07/20/2015-07/24/2015 (pp 341-348)
Stanton A & Channon A () Heterogeneous complexification strategies robustly outperform homogeneous strategies for incremental evolution. Advances in Artificial Life, ECAL 2013 (pp 973-980)

Teaching interests

Data Science and AI for sustainability and health

Evolutionary robotics and nature-inspired autonomy

Teaching activities

INF112 - Data Modelling and Storage

INF320 - Data Science Portfolio

INF4002 - Introduction to Programming

INF6050 - Database Design

Professional activities and memberships

Member, International Society for Artificial Life (ISAL)

Member, Association for Computing Machinery (ACM)