AI4ABM@ICML2022 Workshop - Organizing Committee

Christian is a postdoctoral researcher at the University of Oxford, working with Prof Jakob Foerster at Foerster AI Research, and supported by the Cooperative AI Foundation. During his Ph.D. Christian advanced research in deep multi-agent reinforcement learning, as well as pioneering one of the earliest applications of probabilistic programming to agent-based epidemiology simulators, and of multi-agent learning to analyse the net-zero energy transition pathways of fossil fuel majors in an agent-based model economic model. He received a 2022 EPSRC IAA Doctoral Impact Fund Award for his thesis work, and the Best Idea Prize from the Climate Change and AI (CCAI) Community in 2019. Past internships led him to Google AI, Armasuisse Cyber-Defense Campus, ManAHL, and the Frontier Development Lab. Christian has also worked in climate policy, being a research assistant with Prof. Myles Allen at Oxford Net Zero. Christian has extensive experience in organising large workshops, including a weekend workshop for members of the German Merit Foundation and a decarbonisation workshop at the Climate Transformation Summit 2021. He is also the chair of the AI4ABM community and organises and moderates its weekly events.

Yang is Director of the Model Development Division of the Canadian Economic Analysis (CEA) department, effective since October 2019. In this capacity, she leads efforts to develop and integrate state-of-the-art economic models for the analysis of the Canadian economy and for providing monetary policy advice. Her responsibilities include leading high-quality research that supports the renewal of the Bank’s monetary policy framework. In this position, Dr. Zhang also collaborates closely with the academic and the international central banking communities. She is involved in the development of the next generation of Bank’s monetary policy models. Yang has a PhD in Economics and has worked with Dr. Bengio on a few recent humanitarian projects including an AI-based COVID-19 tracing app developed at the onset of the pandemic. Since 2020, she has been advising on the intersections of modelling, machine learning and financial economics.

Prateek is a Ph.D. student at the University of Oxford under Prof. Pawan Kumar, Prof. Andrea Lodi, and Prof. Yoshua Bengio. His thesis focuses on developing machine learning methods for combinatorial optimization problems and developing a novel contact tracing framework using ABMs. Prateek’s research on the contact tracing framework won the second runner-up in the UK-wide Doctoral Researcher Award competition. Prateek is actively involved in developing novel applications of AI to solve societally relevant problems. He is also pursuing multi-disciplinary collaborations in developing a market-based mechanism for appropriately pricing in negative externalities of carbon emissions and understanding how global cooperation can be achieved to mitigate climate change mitigation.

Dylan is working as a solution analyst at McKinsey Sustainability Insights. Prior to this, he was a research assistant focusing on energy transition risks with Prof. Ben Caldecott (Oxford Sustainable Finance Programme). Dylan is a pioneer and expert in the field of energy-economic modeling, oil and gas industry in the energy transition, and deep multi-agent reinforcement learning engineering. Dylan holds a Masters of Science (with high distinction) in Sustainable Energy Futures at Imperial College London and a Bachelors of Science in Mechanical Engineering from Northwestern University. He has won awards for his MSc thesis, including 'Best Thesis Runner-Up' and 'Best Presentation of a Complex Topic,' on applying deep multi-agent reinforcement learning to an oil and gas energy transition simulator, spoke at global conferences, and was named Northwestern University's `Top Graduate to Watch in Energy & Sustainability.'

Ani is Associate Professor of Computer Science and Deputy Head of Department (Teaching), in the Department of Computer Science of the University of Oxford. She has a 5-year (MSc equivalent) Computer Science degree from the Technical University of Iasi, Romania, and a DPhil in Engineering Science from the University of Oxford. Ani's main research area is Modelling and Reasoning about Complex Systems. Her research interests are fundamentally interdisciplinary, and include: complex systems and complexity metrics; supply chains and financial systems; agent-based modelling; IoT-based Digital Twins; systemic risk. Her recent work includes applying Machine Learning techniques to identify behavioural patterns in supply chain and financial market data; and building, validating and calibrating large-scale agent-based models of complex systems. Ani is currently a Principal Investigator on "A demonstrator and reference framework IoT-based Supply Chain Digital Twin" Pitch-In project, in collaboration with Cambridge University and Schlumberger, and a Co-investigator on two projects funded by JP Morgan Chase AI Faculty Research Awards, as well as Co-investigator on the Turing AI Fellowship “The LARGE AGENT COLLIDER: Robust agent-based modelling at scale”.

Jakob received a CIFAR AI chair in 2019 and is starting as an Associate Professor at the engineering department of the University of Oxford in the fall of 2021. During his PhD at Oxford he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move back to Oxford. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since. He is also a co-organizer of the Open-Ended Learning Workshop at ICLR 2022 and of the Cooperative AI workshop at NeurIPS 2022. His past work addresses how AI agents can learn to cooperate and communicate with other agents, most recently he has been developing and addressing the zero-shot coordination problem setting, a crucial step towards human-AI coordination.