IEEE ICA 2023

Aparna Taneja

Google Research

Biography: Aparna Taneja is a researcher at the Multi-agent systems for societal impact (MASSI) team in Google Research India. She collaborates with several NGOs and academic partners in the fields of public health and conservation, and her primary focus is the collaboration with ARMMAN, an NGO focused on improving maternal and child health outcomes in India. Aparna joined Google in 2015 and worked with the Google Maps team in Zurich to enhance the quality of search for Maps. She received her Ph.D. in Computer Science at ETH Zurich under the supervision of Prof. Marc Pollefeys. She then pursued a postdoc at Disney Research Zurich. Aparna received her Bachelor's and Master's in Computer Science from IIT Delhi in 2006.

Ayumi Igarashi

University of Tokyo

Biography: Ayumi Igarashi is an associate professor at the University of Tokyo, Japan. Previously, she was an assistant professor in the Principles of Informatics Research Division at the National Institute of Informatics (NII) and a JSPS postdoctoral fellow at the University of Tokyo, Japan. She completed her PhD in the Department of Computer Science at the University of Oxford, UK. Her research focuses on fair division, algorithmic game theory, and computational social choice. Recently, she was selected on the Innovators Under 35 Japan 2021 list by MIT Technology Review.

Jimmy Cao

University of South Australia

Title: Enhancing decision-making with deep reinforcement learning: fundamentals and applications

Biography: Dr Jimmy Cao is a Senior Lecturer/Associate Professor in AI and leads the research in human-machine interaction and learning at STEM/Industrial AI, University of South Australia (UniSA) in Adelaide. He obtained his PhD degree from the Australian AI Institute at the University of Technology Sydney in 2017 and has published over 100 outputs in top-quality journals and conferences. His exceptional contributions have been recognised with prestigious awards and fellowships, such as being ARC DECRA Fellowship (2022), Baidu AI Chinese Young Scholar (AI+X category, 2022), DAAD AInet Fellow (2022), ACM’s Distinguished Speaker (2022), UniSA Inaugural Enterprise Fellowship (2022), UniSA STEM ECR Award (2022), Stanford-Elsevier Top 2% Global Scientist (2022, 2023) and JSPS Invitational Fellowship (2023). He serves as Associate Editor in various journals such as IEEE TNNLS and TFS, and as PC member for various conferences including AAMAS, AAAI, IJCAI, and NeurIPS. Furthermore, he has been successful in obtaining competitively funded projects, totalling over $3 million from the government and industry, such as ARC, CRC, FWPA, DSTG, ONRG, DAAD and Google. His research interests encompass reinforcement learning, brain-computer interfaces, and decision-making.

Fenghui Ren

University of Wollongong

Biography: Dr Fenghui Ren received his PhD from the University of Wollongong in 2010. Between 2010 and 2012, he worked on an ARC Linkage project (LP0991428) as a research fellow and was awarded the UOW Vice Chancellor’s Postdoctoral Fellow Award in 2013. Between 2014 and 2017, he worked on an ARC DECRA project (DE14010007) as a fellow. Since 2018, he has worked in the School of Computing and Information Technology at UOW as a lecturer. Dr Ren’s major research areas include agent and multi-agent systems, decision support systems, agent-based modelling and simulation, data mining and pattern discovery, and agent-based learning, negotiation and coordination. He has over 100 publications, and his h-index is 16 with 1090 citations by Google Scholar and 13 with 696 citations by Scopus. He has serviced up to 50 international conferences/workshops/journals as a PC member, and jointly organised over 20 international conferences/workshops.