Ed H. Chi
Google DeepMind, USA
Title: TBD
Workshop: The 3rd Workshop on Personal Intelligence with Generative AI
Link: https://generative-rec.github.io/workshop-www25/
Abstract:
TBD
Bio:
Ed H. Chi is VP of Research at Google DeepMind, leading machine learning research teams working on large language models (from LaMDA leading to launching Bard/Gemini), and universal assistant agents. With 39 patents and ~200 research articles, he is also known for research on user behavior in the web and social media. As the Research Platform Lead, he helped launch Bard/Gemini, a conversational chatbot experiment. His research also delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >950 product landings and ~$10.4B in annual revenue since 2013.
Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center's Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.
Jian Pei
Duke University, USA
Title: Data and AI Markets in a Nutshell
Abstract:
Data and AI model services, often regarded as the driving force of the digital and AI economy, are powering a wide range of applications and creating significant business opportunities. Data and AI model service pipelines are frequently enabled by data and AI markets, yet the landscape of these markets remains relatively unfamiliar to many professionals in data science, machine learning, and AI. This tutorial provides an intuitive, concept-driven, and examplebased introduction to this emerging domain. Through practical examples, we will explore the motivations, fundamental principles, key techniques, major challenges, and research and development opportunities in data and AI markets. The tutorial will cover core concepts, market models, and operational mechanisms—such as privacy, security, and deployment—as well as market administration, offering a comprehensive foundation for understanding and advancing this transformative field.
Bio:
Jian Pei is the Arthur S. Pearse Distinguished Professor and Chair of the Department of Computer Science at Duke University. A leading researcher in data science, machine learning, big data, data mining, and database systems, he has published extensively, garnering more than 130,000 citations with an H-index of 114. He is a fellow of the Royal Society of Canada, the Canadian Academy of Engineering, ACM, and IEEE. A recognized leader in data and AI markets, he has made tangible contributions to data-driven decision-making, responsible AI, and the economics of data exchange. His contributions have been recognized with prestigious honors, including the ACM SIGKDD Innovation Award, the ACM SIGKDD Service Award, and the IEEE ICDM Research Award.
Jie Lu
University of Technology Sydney, Australia
Title: AI for Women’s Health: Framework and Focus Areas
Abstract:
AI for women’s health is a growing challenge that focuses on integrating and adopting AI across key aspects of women’s health, with the goal of practical implementation in everyday clinical practice. There have been significant delays in finding innovative ways to address prevalent and burdensome health issues that disproportionately affect women throughout their life courses. This study presents our initial development, including a comprehensive framework, methodology, and focus areas for the application of advanced AI, particularly machine learning, in women’s health. It emphasizes personalized healthcare for women through genomic association analysis and early-stage prediction for key women’s health issues, such as fertility, pregnancy, breast cancer, and postmenopausal concerns, in a genetics-informed and individually tailored manner. The research aims to provide societal benefits, improve the efficiency of women’s health systems, and advance personalized medicine.
Bio:
Distinguished Professor Jie Lu is a scientist in the field of computational intelligence, primarily known for her work in fuzzy transfer learning, concept drift, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, and Australian Laureate Fellow. Professor Lu is the Director of the Australian Artificial Intelligence Institute (AAII) at University of Technology Sydney (UTS), Australia. She has published six research books and over 500 papers in leading journals and conferences; won 10 Australian Research Council (ARC) Discovery Projects and over 20 industry projects as leading chief investigator; and has supervised 50 PhD students to completion. Prof Lu serves as Editor-In-Chief for Knowledge-Based Systems and International Journal of Computational Intelligence Systems. She is a recognized keynote speaker, delivering over 40 keynote speeches at international conferences. She is the recipient of NeurIPS Outstanding Paper Award (2022), two IEEE Transactions on Fuzzy Systems Outstanding Paper Awards (2019 and 2022), Australasian Artificial Intelligence Distinguished Research Contribution Award (2022), Australian NSW Premier's Prize on Excellence in ICT (2023) and the Officer of the Order of Australia (AO) in the Australia Day 2023.
Olga Jurasz
Centre for Protecting Women Online (The Open University)
Title: Women in online spaces: violence, tech bros and why law (alone) is never enough
Workshop: Towards a Safer Web for Women: First International Workshop on Protecting Women Online
Link: https://tsww25.github.io/
Abstract:
The Internet today is far from the vision invented by Tim Berners-Lee: an open and creative space “for everyone”. In the past decade, online spaces have shown to be increasingly unsafe, hostile and unwelcoming to women. The rise and hypervisibility of online misogyny, tech-facilitated violence against women, gendered misinformation have raised a global concern about the future of online safety. However, this is juxtaposed with fragmented legal responses that lack ambition to improve women’s online safety, tech companies scaling back content moderation and new geopolitical context which sees a close alignment between ‘big tech’ leaders and global political actors.
In this context, how do we move the dial for online gender equality and safety? Can law and regulation (ever) offer solutions?
Bio:
Professor Olga Jurasz is professor of law at the Open University (UK) and Director of the Centre for Protecting Women Online - an interdisciplinary unit focusing on research, policy engagement and creating social impact in relation to women's online safety. Her research expertise is in international law, human rights, legal responses to violence against women (including online violence) and feminist approaches to governance of online spaces and online safety. Since 2024, she serves as an Independent Expert to the Council of Europe Committee of Experts on combating technology-facilitated violence against women and girls.
Professor Jurasz is a leading voice in the field of law & violence against women. She published her research widely, including two books: Online Misogyny as a Hate Crime: A Challenge for Legal Regulation(Routledge 2019) and Violence Against Women, Hate and Law: Perspectives from Contemporary Scotland (2022). In 2023, she led the project 'Online Violence Against Women: A Four Nations Study', the largest empirical study in the UK gathering data about societal attitudes towards online violence against women and their experiences of such violence.
Her expertise has influenced governments, international organisations, and third-sector bodies in shaping law and policy regarding online violence against women, criminal law, online communications, and state obligations. She has provided expert advice to the Council of Europe on the development of the first digital recommendation on preventing and combating violence against women.
Maria Nguyen
eSafety, Australia
Title: Technology facilitated gender-based violence: An Australian perspective
Workshop: Towards a Safer Web for Women: First International Workshop on Protecting Women Online
Link: https://tsww25.github.io/
Abstract:
While the rapid growth of technology has led to many benefits in society, it has also opened new channels for online harm. 1 in 2 Australians have experienced some form of technology-facilitated abuse. This abuse occurs across all ages, genders and socioeconomic and demographic groups, however, it is gendered in its nature and impact.
The eSafety Commissioner (eSafety) is Australia’s independent regulator, educator, and coordinator for online safety. Our aim is to safeguard Australians from online harms, including technology facilitated gender-based violence (TFGBV), and to promote safer online experiences.
This presentation will share eSafety’s perspective on the emerging trends and challenges related to TFGBV. It will also provide an overview of our programs, projects and resources which aim to tackle this issue, including:
- how we are preventing TFGBV from occurring in the context of the domestic and professional lives of women through community awareness and education
- eSafety’s Safety by Design principles, which guide technology companies to minimise online threats by anticipating, detecting, and eliminating online harms before they occur
- our Preventing Tech-based Abuse of Women Grants Program and the innovative projects developed by NGOs being funded
- how we are collaborating with other online safety regulators and organisations from around the world.
Bio:
Maria Nguyen is a Project Lead at the eSafety Commissioner (eSafety), the Australian Government’s independent online safety regulator. As part of the Gender and Tech Section of eSafety, Maria contributes to policies and programs that promote safe, inclusive, and gender-equal online spaces. She has been leading eSafety’s contribution to a new National Law Enforcement Training Solution being implemented by the Attorney-General’s Department, aimed at enhancing police responses to family, domestic, and sexual violence, including technology-facilitated coercive control.
Lun-Wei Ku
Institute of Information Science, Academia Sinica, Taiwan
Title: TBD
Workshop: Beyond Facts: 5th International Workshop on Computational Methods for Online Discourse Analysis
Abstract:
TBD
Bio:
She received her Ph.D. degree in Computer Science and Information Engineering from National Taiwan University, Taipei, Taiwan, in 2009. She joined the Institute of Information Science, Academia Sinica as an assistant research fellow in Aug., 2012, and was promoted to be an associate research fellow in Aug., 2018.
Her research expertise lies in natural language processing and information retrieval, especially in sentiment analysis and opinion mining. She often publishes papers in top conferences including ACL, AAAI, SIGIR, WWW, NAACL, and EMNLP. She is very active in the research community, and the international professional activities she involves include the general chair and the program chair of StarSem 2021, StarSem 2019 and AIRS 2019, and the area chair of ACL, NjAACL 2021, ACL, EMNLP, COLING 2020, EMNLP 2019, ACL 2017, CCL 2016, NLPCC 2016, ACL-IJCNLP and EMNLP 2015. Her research is internationally recognized and has been served as the AFNLP Member-at-Large and ACL SIGHAN Asia Information Officer.
She is very experienced in academic and industrial collaborations. Her research collaborators come from US, Singapore, Sweden and Israel, and she is currently working with data companies and banks. Her current research topics focus on recommendation, visual storytelling, sensational text generation, fake news intervention, knowledge-based question answering, lie detection and social media analysis.
Gianluca Demartini
The University of Queensland, Australia.
Title: TBD
Workshop: Web of Data Quality Workshop
Abstract:
TBD
Bio:
Dr. Gianluca Demartini is a Professor in Data Science and an ARC Future Fellow at the University of Queensland, Australia. His main research interests include Information Retrieval, Semantic Web, and Human Computation. His research is currently funded by the Australian Research Council, the Swiss National Science Foundation, Meta, Google, and the Wikimedia Foundation. He received Best Paper Awards at the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) in 2023, AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018, at the European Conference on Information Retrieval (ECIR) in 2016 and 2020, and the Best Demo award at the International Semantic Web Conference (ISWC) in 2011. He has published more than 200 peer-reviewed scientific publications including papers at major venues such as WWW, ACM SIGIR, VLDBJ, ISWC, and ACM CHI. He is an ACM Senior Member, ACM Distinguished Speaker, and a TEDx speaker.
Before joining the University of Queensland, he was a Lecturer at the University of Sheffield in UK, post-doctoral researcher at the eXascale Infolab at the University of Fribourg in Switzerland, visiting researcher at UC Berkeley, junior researcher at the L3S Research Center in Germany, and intern at Yahoo! Research in Spain. In 2011, he obtained a Ph.D. in Computer Science at the Leibniz University of Hannover in Germany focusing on Semantic Search.
Dinh Phung
Monash University, Australia.
Title: TBD
Workshop: Optimal Transport for Structured Data Modeling and Generation
Abstract:
TBD
Bio:
Professor Dinh Phung is the Head of the Department of Data Science and AI at Monash University. His research interest includes machine learning, deep learning, generative AI, robust and trustworthy AI, optimal transport, Bayesian and graphical models. He has published 250+ papers in these areas and application domains such as natural language processing (NLP), computer vision, digital health, cybersecurity and autism.
Amit Jaspal
Meta, USA.
Title: TBD
Workshop: The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Abstract:
TBD
Bio:
Seasoned Machine Learning Engineer and Leader with 14 years of experience building cutting-edge
recommendation and information systems. Proven track record of leveraging state-of-the-art ML to solve critical business problems in recommendation systems (Privacy Compliance, GPU training & serving, sequence modeling, cross-stage consistency, popularity debias) and unlock business value.
Possesses strong Leadership expertise with 5 years as a tech lead and 3 years in a management role, successfully orchestrating cross-functional collaboration, mentoring and hiring talent.
Lili Pan
University of Electronic Science and Technology of China, China
Title: TBD
Workshop: LLM-UM: The 1st Workshop on Large Language Model Using Multi-modal Data for User Modeling
Abstract:
TBD
Bio:
Dr. Lili Pan is an associate professor at the University of Electronic Science and Technology of China (UESTC), where her recent research focuses on multi-modal large language models (MLLMs), LLM-driven scientific innovation, and continual learning. She has led several significant national research projects funded by the National Natural Science Foundation of China (NSFC). With over 30 publications in prestigious journals and at conferences such as ICML, CVPR, and ECCV, Dr. Pan has made notable contributions to her field. She obtained her Ph.D. in Information Engineering from UESTC and was a visiting Ph.D. student at the Robotics Institute, Carnegie Mellon University (CMU), from 2009 to 2011.
Scientific innovation drives societal progress, while the rapid advancement of large language models (LLMs) is profoundly transforming research paradigms, ushering in a new era of AI-powered scientific inquiry that transcends traditional methodologies. In this talk, I will introduce our latest work, Nova—an LLM-driven scientific innovation system that enhances the novelty and diversity of generated research ideas through a planned retrieval-augmented generation framework. Nova operates through a three-stage pipeline: seed idea generation, idea refinement via PlannedRAG, and proposal generation. This process integrates principles from scientific innovation theory with dynamic knowledge exploration to generate research ideas systematically. Both automated and human evaluations demonstrate that Nova significantly improves the novelty and diversity of generated outputs. Notably, in a Swiss Tournament evaluation conducted on 170 input papers, Nova outperformed state-of-the-art baselines, producing at least 2.5 times more top-rated ideas.
Jieming Zhu
Huawei, China.
Title: TBD
Workshop: The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Abstract:
TBD
Bio:
Dr. Jieming Zhu is currently a lead researcher at Huawei Noah's Ark Lab (AI Lab). Before that, he obtained his Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong in 2016, supervised by Prof. Michael R. Lyu. He received the B.Eng degree from Beijing University of Posts and Telecommunications. His recent research focus is on building and applying practical machine learning algorithms (especially ranking, NLP and multimodal learning) for industrial-scale recommender systems, with a goal to help better discover users' interests and serve their needs. His team has launched many self-designed ML algorithms on Huawei's products like News Feeds, Microvideo Stream, Music App, App Store, PPS Ads, etc.
Angela Dawson
University of Technology Sydney, Australia.
Title: TBD
Health Day:
Abstract:
TBD
Bio:
Professor Angela Dawson is a public health social scientist with expertise in maternal and reproductive health service delivery to priority populations in Australia and low and lower-middle-income countries. Angela was a NHMRC Translational research fellow examining approaches to counselling women with female genital mutilation (FGM) at the point of care and the recipient of the Sax prize for research impact. She has undertaken research into the delivery of reproductive health services in humanitarian emergencies, the management and referral of women who have experienced domestic violence, and access to abortion and emergency contraceptive pills in Australia and internationally.
Her early work sparked her passion for public health in visual arts in disadvantaged communities in Cape Town, South Africa. Her career has focused on applied public health research that focuses on interventions to enhance health service delivery. Angela is the co-chair of the Australasian Sexual and Reproductive Health Alliance (ASRHA), a Fellow of the Public Health Association of Australia, a member of the Interagency working group of reproductive health in crisis and an Associate Editor of the journal BMC Pregnancy and Childbirth. Angela has a special interest in Indigenous health and innovative approaches to delivering drug and alcohol services. She has been involved in evaluating Aboriginal child health programs across NSW.