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< Back Dr Tony Stockman Queen Mary University of London Supervisor Dr Stockman is an interaction designer/researcher who investigates how technology can enhance accessibility and improve human performance. He is particularly interested in technology to support spatial cognition and wayfinding, health monitoring and improve performance levels in sport and music. This includes the role of games in simulating these domains and supporting skill acquisition and enhanced performance. He is a Board member and former president of the International Community for Auditory Display ( www.icad.org ). He has organised 6 international workshops on a range of HCI topics, and has been on the organising committee of 10 international HCI-related conferences. Topics on which he has recently published include participatory design and prototyping, auditory overviews for route guidance, self monitoring of biological signals and accessible collaborative working. He is particularly interested in supervising students with a Computer Science, Electrical Engineering, HCI, or behavioural sciences background on the following topics: Simulation to support accessibility and skill acquisition in team sports Intelligent audio mostly games to support learning Intelligent Audio or audio-haptic approaches to health monitoring and biofeedback Intelligent systems to support individual or collaborative music making Research themes: Intelligent simulation systems Interaction design for simulated sports Game Audio and Music Game Design Games with a Purpose t.stockman@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Applied Games Design & Development Esports Game AI Game Audio Player Research - Previous Next

< Back Prof. David Adger Queen Mary University of London Supervisor Inventing new languages for in-game communications; studying their effects on game play and character development. d.j.adger@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Creative Computing - Previous Next

< Back Dr Anthony Constantinou Queen Mary University of London Supervisor Anthony Constantinou’s research is on Bayesian Artificial Intelligence for causal discovery and intelligent decision making under uncertainty. He applies his research to a wide range of areas, including gaming, sports, medicine and finance. He is the founder of the Bayesian Artificial Intelligence research lab at Queen Mary University of London. He is interested in supervising students who are interested in working with machine learning algorithms that discover causal relationships from data (applied to game data), or building intelligent decision-making models using Bayesian networks (applied to game data). Please note that these projects focus on working with game data. Students interested in these projects should have skills that are relevant to: Machine learning for causal discovery Bayesian networks Statistics and probability theory a.constantinou@qmul.ac.uk Email Mastodon https://www.constantinou.info Other links Website https://www.linkedin.com/in/anthony-c-constantinou-728b6b49/ LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Luca Rossi Queen Mary University of London Supervisor Luca Rossi is a Lecturer in Artificial Intelligence at Queen Mary University of London. His research expertise lies in the areas of structural pattern recognition, machine learning, data and network science. Within the context of IGGI, he is interested in applying graph machine learning techniques, particularly graph neural networks, to the modelling and analysis of games. He is also interested in supervising projects related to behavioural analytics and privacy issues in online gaming. luca.rossi@qmul.ac.uk Email Mastodon https://blextar.github.io/luca-rossi/ Other links Website LinkedIn BlueSky Github Themes Game AI Game Data - Previous Next

< Back Dr Ahmed M. A. Sayed Queen Mary University of London Supervisor Ahmed Sayed is a Lecturer (Assistant Professor) of Big Data and Distributed Systems at the School of EECS, QMUL and leads the Scalable Adaptive Yet Efficient Distributed (SAYED) Systems Lab. He has a PhD in Computer Science and Engineering from the Hong Kong University of Science and Technology. His research interests lie in the intersection of distributed systems, computer networks and machine learning. He is an investigator on several UK and international grants totalling nearly USD$1 million in funding. His work appears in top-tier conferences and journals including NeurIPS, AAAI, MLSys, ACM EuroSys, IEEE INFOCOM, IEEE ICDCS, and IEEE/ACM Transactions on Networking. He is interested in supervising students with a background in game AI, machine learning, distributed systems, and/or creative computing, Ahmed is interested in working with students at the intersection of artificial intelligence, machine learning, and creative computing. He aims to leverage AI/ML methods, game data and player research to design intelligent game agents by creating systems that enable game agents to learn better gaming strategies, thus enhancing the gaming experience. He is open to any research proposals in that space and currently is keen on exploring solutions that are based on leveraging the emerging distributed privacy-preserving ML ecosystems on large-scale game data. If you are interested in working with him on this, please reach out to him. ahmed.sayed@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/~ahmed/ Other links Website https://www.linkedin.com/in/ahmedmabdelmoniem/ LinkedIn BlueSky https://github.com/ahmedcs Github Themes Creative Computing Design & Development Game AI Game Data Player Research - Previous Next

< Back Dr Mona Jaber Supervisor Mona Jaber is a lecturer in Internet of Things (IoT) who’s research is centred at the intersection of IoT and machine learning for sustainable development goals. In particular, she is interested in harnessing IoT data to model mobility trends in a digital twin platform that allows users to test future measures in a verisimilar virtual environment. Her research is grounded in privacy-preserving measures for capturing and analysing IoT data. She is the winner of a new investigator award research grant (DASMATE £500K) in which she examines distributed acoustic sensors systems and a privacy-preserving alternative data source to model active travel. She is interested in supervising students on the topic of serious game building that engages the public in shaping their neighbourhood through interventions in the virtual environment towards sustainable 15 minutes city goals. m.jaber@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/profiles/jabermona.html Other links Website https://www.linkedin.com/in/mona-jaber/ LinkedIn BlueSky Github Themes Accessibility Applied Games Game AI - Previous Next

< Back Dr Mathieu Barthet Queen Mary University of London Supervisor Dr Mathieu Barthet is a Senior Lecturer in Digital Media at Queen Mary University of London (QMUL). He is the Programme Coordinator of the MSc in Media and Arts Technology by Research and oversees Industry Partnerships for the Centre for Doctoral Training in AI & Music. He received an MSc degree in Electronics and Computer Science in 2003 (Paris VI University/Ecole Polytechnique de Montréal), and an MSc degree in Acoustics in 2004 (Aix-Marseille II University/Ecole Centrale Marseille). He was awarded a PhD in Acoustics, Signal Processing and Computer Science applied to Music from Aix-Marseille II University and CNRS-LMA in 2008, and joined the Centre for Digital Music at QMUL in 2009. Mathieu conducts research in the fields of Music Information Research, New Interfaces for Musical Expression and Music Perception, in which he published over 100 peer-reviewed academic papers. His research interests include music and emotions, audio-visual interfaces and extended reality, AI-based musical interfaces, music recommendation, and musical timbre. He is particularly interested in supervising students with an HCI and/or AI background combined with musical skills or a sensitivity to music, to investigate how games can be used for musical education and production, or how game audio can be enhanced using AI and new interfaces. Research themes: Game Audio and Music Games for Music Education or Production Audio in Sports Games m.barthet@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/profiles/barthetmathieu.html Other links Website https://www.linkedin.com/in/mathieu-barthet-9519ab17/ LinkedIn BlueSky https://github.com/mabara Github Themes Game AI Game Audio - Previous Next

< Back Dr Changjae Oh Queen Mary University of London Supervisor Changjae Oh joined Queen Mary University of London (QMUL) in September 2019 as a Lecturer at the School of Electrical Engineering and Computer Science (EECS). He was a postdoctoral researcher at QMUL EECS from 2018 to 2019. He received a PhD in Electrical and Electronic Engineering in 2018 at Yonsei University, South Korea. His research expertise spans a range of researches that are based on visual signals, such as image processing, computer vision, and vision-based machine perception, combined with machine/deep learning. Within the topics with IGGI, he is particularly interested in students who want to investigate the topics about vision-based AI perception in a game environment and game engines for real-robot perception. c.oh@qmul.ac.uk Email Mastodon https://eecs.qmul.ac.uk/~coh/ Other links Website https://www.linkedin.com/in/changjae-oh-42a36685 LinkedIn BlueSky Github Themes Applied Games Game AI - Previous Next

< Back Dr Athen Ma Queen Mary University of London Supervisor Athen Ma is an innovator in interdisciplinary approaches to the study of communities and networked ecosystems. She is particularly interested in finding out how the structure and dynamics of communities evolve over time and what kind of mechanics that help underpin cohesion in communities. Her research has been published in world-leading journals, with recent works revealing the organisation of collaborative science in the UK (in PNAS highlight), uncovering how ecological networks rewire under drought (front cover of Nature Climate Change ), and how agricultural ecosystems are resilient to changes in farming management (in Nature Ecology and Evolution ). Online multiplayer games naturally form a platform for social relationships to develop, and deciphering the social structure and dynamics of the communities formed will provide insights into many aspects in games, ranging from users engagement and retention to team formation. For example, matchmaking enables users to find other players who share similar profiles, interests as well as skills and personality; has been seen as an important tool for establishing and maintaining a thriving gaming community. Athen is keen to explore novel ways to use advances in social network analysis to investigate player communities in games across multiple network scales, so as to better understand their formation and evolution. Findings from this research will help identify/predict the type of social interactions that will promote the level of engagement among players and community cohesion, paving the way for designing in-game activities that will foster long-time engagement and retention. athen.ma@qmul.ac.uk Email Mastodon https://sites.google.com/site/athenma2015/ Other links Website LinkedIn BlueSky Github Themes Game Data Player Research - Previous Next

< Back Dr Yongxin Yang Queen Mary University of London Supervisor Dr Yongxin Yang is a lecturer in financial technology at Queen Mary University of London, UK and he is also a part-time professor in finance at Southwestern University of Finance and Economics, China. His research is in the area of meta learning and its interactions with other machine learning paradigms like reinforcement learning. He has broad interests in applied machine learning, esp. for finance problems, for example, portfolio optimization and financial derivatives pricing. For the project of meta reinforcement learning, we want to explore the learning algorithms that can transfer an existing RL agent into a new task (e.g., a new game episode) with the minimal effort on retraining it. For the project of AI Economist, we are going to create a multi-agent system, where each agent behaves like a human being who will interacts with the environment and other agents (e.g., produce and trade), then we study how a certain policy (e.g., monetary and tax) affects the economy. yongxin.yang@qmul.ac.uk Email Mastodon https://yang.ac/ Other links Website LinkedIn BlueSky https://github.com/wOOL/ Github Themes Applied Games Game AI Game Data - Previous Next

< Back Dr Zoe Handley University of York Supervisor Zoe Handley is a Senior Lecturer (Associate Professor) in Language Education. She is an interdisciplinary researcher, with a background in language technology, who recognizes the value of quantitative as well as qualitative work in this area. Her earlier work focused on the evaluation of speech synthesis for use in language learning and teaching. Since then she has carried out a systematic review of evidence for the use of technology to support English language learning in primary and secondary schools and supervised a number of theses evaluating applications of technology for language learning. These have typically explored the use of web 2.0 and Computer-Mediated Communication (CMC) technologies. Further to this she is interested in how learners autonomously use technology to support their learning in contexts such as study abroad. Zoe is currently particularly interested in teacher thinking in relation to the integration of technology to support language learning and developing and evaluating training to support teachers in making decisions about what technologies to integrate into their teaching, for what purposes and how. Zoe welcomes applications from PhD students interested in designing and evaluating educational activities that harness the affordances of digital technologies to create conditions and engage learners in processes that are known to support language learning. zoe.handley@york.ac.uk Email https://sites.google.com/york.ac.uk/pivotal-group/about Mastodon https://www.york.ac.uk/education/our-staff/academic/zhandley/ Other links Website https://www.linkedin.com/in/zoe-handley-a730b58/ LinkedIn BlueSky Github Themes - Previous Next

< Back Dr Adrian Bors University of York Supervisor Adrian G. Bors is an Associate Professor at the University of York and has published more than 150 papers in international journals and conferences in the areas of his research interests. He is interested in supervising projects related to the application of novel artificial intelligence methods and computer vision in Game AI. One of the areas of interest is in the modelling of game characters (intelligent agent) continuously learning from their environments, able to transfer their knowledge from one stage to the next, while accumulating the information, like human/animal beings and enabling to continuously adapt to their environments. Another topic of interest is represented by conditional image and video generation for developing game environments. The conditional video/image generation will depend on certain factors that can be pre-established or be the result of self-learning by an (intelligent agent). Most existing games relying on no movement representation lack in representing realistic and continuous movement. In this direction of research, we will aim to generated video which would be consistent with realistic movement of game characters. Specific attention will be paid to modelling the interaction of the generated movement with the environment or other actors (game characters). In another direction of research, Adrian G. Bors will supervise projects in digital watermarking of 3D graphical characters. Codes will be invisible embedded and retrieved from the 3D graphics representations. The code embedded, like the DNA in human/animals, will enable the character to act in specific ways, defining behavioural traits in similarly looking graphics characters. adrian.bors@york.ac.uk Email https://www.researchgate.net/profile/Adrian-Bors Mastodon https://www-users.cs.york.ac.uk/adrian/ Other links Website https://www.linkedin.com/in/adrian-bors-32a3668/ LinkedIn BlueSky https://github.com/AdrianBors Github Themes Game AI - Previous Next