DSRC Talk Series

Abstract: Psycho-Social-Neuro Landscape of Technology on Adolescents: Building Personal Agency with a Narrative Perspective
  The rapid advancement of technology has brought about unprecedentedchanges in the lives of young people, presenting both opportunities andchallenges. In this keynote presentation, we will explore the dynamicrelationship between technology and adolescents, exploring the psycho-social-neuro impact both globally and within the United States. We willexamine the psychological, social, and neurological effects of technologyon adolescent development, considering factors such as social media,online gaming, and screen time. Narrative practice offers a transformativeapproach to empower youth in re-authoring their stories of technologyexperiences. By harnessing the power of storytelling, narrative practiceprovides a unique framework for adolescents to reflect on their digitalexperiences, externalize challenges, and re-author their narratives.Drawing on research, case studies, and practical examples, the potentialof narrative practice in promoting positive digital well-being andempowering youth to have personal agency in their digital lives will beexamined.

Biography:

     Dr. Simon Chan is an esteemed scholar currently serving as an Associate Professor in the Department of Social Work at the University of Vermont in the United States. With a wealth of experience in the field, Dr. Chan has previously held teaching positions at both Hong Kong Baptist University and City University of Hong Kong. He has also pursued master's programs in the United Kingdom and Australia, and a doctoral degree from the University of Hong Kong. Throughout his career, Dr. Chan has demonstrated a diverse range of expertise, encompassing clinical counseling, private practice, social work, and curriculum administration. While initially dedicated to men's studies during his formative years, his focus has since shifted towards the integration of Multiple Family Therapy and Narrative Therapy in supporting children with learning disabilities and their families, which gained solid evidence for its effectiveness. In response to the emergence of the Digital Native generation, Dr. Chan has further his research endeavor centered around blended learning, leveraging multimedia technology, gamification, and digital interactive interfaces to augment learning experience. Dr. Chan's scholarly pursuits epitomize his commitment to advancing the field of social work and education, creating an optimal educational environment conducive to growth and development.

Abstract: Supporting Children Whose Parents Have Mental Illness Through Online Interventions.
        Children and young people are now more comfortable than everaccessing online information, with the catchphrase “digital natives”dominating in literature on technology and youth development. However,in areas where children and young people experience poor outcomes,discourses around internet and online presence are often construed interms of risks instead of opportunities. During the COVID-19 pandemic, Ico-authored an opinion piece where we explored the potential foronline/web-based interventions to promote positive outcomes for childrenwhose parents have mental illness which was a much-needed reflectionconsidering the limited face-to-face interactions at the time. Whileordinary lives have resumed, post-pandemic effects may have increasedchildren’s familiarity with the internet and use of mobile devices.Consequently, I will present how online interventions can support childrento deal with the impact of parental mental illness including the need tomake these online spaces child/youth-friendly..

Biography:

     Dr Ebenezer Cudjoe is a Lecturer in Childhood Studies with the Department of Psychosocial and Psychoanalytic Studies at the University of Essex. He completed his PhD at City University of Hong Kong where he investigated what it is like for children to live with a parent with mental illness with a methodology informed by Husserl’s phenomenological philosophy. Eben’s research interests are focused on areas related to improving outcomes for children in families facing difficulties. To date, he has 43 publications including 42 journal articles in high impact journals/SSCI indexed and one book chapter in areas such as child protection, children in residential care, kinship care, children whose parents have mental illness and Husserlian phenomenology. He has worked on research supported by UNICEF-Ghana child protection grant and the International Impact Fund from the University of Essex. He is currently leading on the International Impact Fund project which involves collaborating with social workers in Ghana to co-produce a child maintenance assessment checklist to be used by practitioners in their work with children and families.

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Abstract:

        Networks are effective for representing relationships between entities across a range of disciplines, and network analysis techniques are widely used for understanding various types of complex networks, e.g., social networks, biological networks, transportation networks. Network analysis tasks, such as community detection, centrality analysis, and network visualization, play important roles in many disciplines. Existing network analysis tools, however, lack efficiency in analyzing massive network data or may not provide comprehensive analysis functions, which limits their practical applicability. We present EasyGraph, an open-source library that supports many network data formats and covers important functions like structural hole spanner detection and network embedding. Notably, we have optimized several key functions for enhanced efficiency. We believe that EasyGraph is a powerful tool for dealing with major analytical tasks in complex networks across various domains.

Biography:

     Yang Chen is an Associate Professor within the School of Computer Science at Fudan University, China. He leads the Big Data and Networking (DataNET) Group since 2014. Before joining Fudan, he was a postdoctoral associate at the Department of Computer Science, Duke University, USA, where he served as a Senior Personnel in the NSF MobilityFirst project. From September 2009 to April 2011, he has been a research associate and the deputy head of Computer Networks Group, Institute of Computer Science, University of Goettingen, Germany. He received his B.S. and Ph.D. degrees from Department of Electronic Engineering, Tsinghua University in 2004 and 2009, respectively. His research interests include interdisciplinary network analysis (for example, online/mobile social networks), Internet architectures, and federated learning. He is serving as an Associate Editor-in-Chief of the Journal of Social Computing, an Editorial Board Member of the Transactions on Emerging Telecommunications Technologies (ETT) and an Associate Editor of Computer Communications. He served as an OC / TPC Member for many international conferences, including SOSP, SIGCOMM, WWW, MobiSys, ICDCS, IJCAI, AAAI, ECAI and IWQoS.

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Abstract:

       Various popular online social platforms have gathered a colossal amount of user data related to some target topic, including user-generated contents and user interactions representing the written expressions of users and the user-to-user interactivity, respectively. These data can potentially be exploited to learn user stances towards important topic such as vaccination. In this talk, we first address a new task called conversational stance detection which is to infer the stance towards a given target in conversations. To tackle the task, we propose a benchmarking conversational stance detection (CSD) dataset with annotations of stances towards C19 vaccination and the structures of conversation threads among the instances based on six major social media platforms in Hong Kong, and a proposed model called BranchBERT that incorporates contextual information in conversation threads. We will also introduce another benchmarking dataset CTSDT that consists of a large number of annotated conversations from English based online social platform, and a new contextual target-specific stance detection model termed ConMulAttn. We observe that there are many users who prefer not to reveal their stances in the online posted contents. Their stances can be typically embedded in their interactions with other users. Therefore, in the second part of the talk, we present a large-scale and novel dataset that consists of an interaction graph of vaccination stances based on the data we collect from Twitter, and investigate an application of the proposed dataset, stance detection based on the interaction graph. To the best of our knowledge, this is the first to study the task of automatic target-specific stance detection based on the interaction graph. To tackle the problem, we propose a new graph neural network-based solution. Our study can aid policymakers in effectively utilizing conversation data from online social platforms to understand the real-time trend of the public stance towards target topics, such as vaccination, which is crucial for successful health management initiatives during the pandemic. Additionally, our findings may inform the development of an academia-government cooperation framework for responding to health crises.

Biography:

    Dr. Yupeng Li received his PhD degree in computer science at The University of Hong Kong. He was a postdoctoral research fellow at the University of Toronto. He is an assistant professor with the Department of Interactive Media at Hong Kong Baptist University. He is interested in studying trustworthy machine learning in networking and social computing. He is excited about interdisciplinary research that applies robust algorithmic techniques to edging problems. Dr. Li takes advantage of powerful techniques in learning, algorithm, and game theory to design and analyze networked systems, such as information and social networks. He has been developing robust online and decentralized machine learning techniques with performance guarantees with applications to networking and social computing. Dr. Li has been awarded the Rising Star in Social Computing Award by CAAI and the distinction of Distinguished TPC Member of the IEEE INFOCOM 2022. He serves on the technical committees of some top conferences in computer science. For example, TPC Track Chair of ICDCS 2024 and TPC Chair of the SocialMeta 2023. His works have been accepted in prestigious venues, such as MobiHoc, INFOCOM, ICDCS, JSAC, ToN and ICA. He has awarded national, provincial, municipal and industry competitive research fundings from agencies such as RGC, NSFC, GDSTC, and CCF Research Fund.

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Abstract:

       The real-world big data are largely dynamic, interconnected, and unstructured texts. It is important to transform such massive unstructured text into structured knowledge. Many researchers rely on labor-intensive labeling and annotation to extract knowledge from text data. Such approaches, however, are not scalable. We envision that massive text itself may disclose a large body of hidden structures and knowledge. Equipped with pre-trained language models and data mining/machine learning methods, it is promising to transform unstructured text into structured knowledge without extensive human annotation. In this talk, we overview a set of annotation-free text mining methods developed recently in our group for such an exploration, including discriminative topic mining, taxonomy construction, text classification, and taxonomy-guided text analysis. We show that weakly supervised, annotation-free approach could be promising at transforming massive text into structured knowledge.

Biography:

    Jiawei Han is Michael Aiken Chair Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), Japan's Funai Achievement Award (2018), and being elevated to Fellow of Royal Society of Canada (2022). He is Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by NIH Big Data to Knowledge (BD2K) Initiative. Currently, he is serving on the executive committees of two NSF funded research centers: MMLI (Molecular Make Research Institute)—one of NSF funded national AI centers since 2020 and I-Guide—The National Science Foundation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) since 2021.

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Abstract:

       We set up the Data Science Research Center in 2022, with the mission to promote and help colleagues in doing interdisciplinary data science research. In this talk, I will first make some commentary about interdisciplinary research and data science. What are the advantages and challenges with interdisciplinary research? What is Data Science? We all have our methods to analyze data, what’s new in data science? What are the new opportunities? I will then share various projects we are currently doing at the Data Science Research Center, and reflect on what we are learning.

Biography:

    Dah Ming Chiu received his first degree from Imperial College London and his Ph.D. degree from Harvard University. He worked in industry for several high tech companies of his time: Bell Labs, DEC and Sun Microsystem Labs. He returned to Hong Kong in 2002 to become a professor in the Department of Information Engineering at the Chinese University of Hong Kong. He served as department chairman from 2009 to 2015. After retiring from CUHK, he is now an emeritus professor. Since beginning of 2022, he has been a research professor at CIHE and is serving as director of the Data Science Research Center.

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Abstract:

       In this talk, I will describe how modeling, simulation, and data science played a crucial role in policymaking during the COVID-19 pandemic in the United States across local, state, and federal levels. I will use a series of real-world case studies to elucidate the need for various modeling frameworks, while also noting the data and knowledge gaps that had to be tackled along the way. I will also highlight the progress in areas of high-performance computing, human judgment integration, scalable data pipelines, and collaborative workflows that made this possible. Through this, I hope to describe a holistic approach to pandemic preparedness and real-time response for future outbreaks.

Biography:

    Srini Venkatramanan is a Research Assistant Professor at the Biocomplexity Institute & Initiative, University of Virginia. He works with the Network Systems Science and Advanced Computing Division and specializes in computational epidemiology and data science for real-time decision making. He received his Ph.D. from the Indian Institute of Science, Bangalore, and completed postdoctoral research at Virginia Tech, United States. His research involves building and analyzing multi-scale models for epidemic dynamics and integrating diverse datasets for situation assessment, forecasting, and assisting policymaking. For his work supporting the COVID-19 response in the United States, he was recognized among the Top 50 Innovators in Intelligent Health 2020 and was a co-recipient of the Collaborative Excellence in Public Service Award from the University of Virginia in 2022. His work has been supported by National Science Foundation (US), Centers for Disease Control and Prevention (US), as well as through industry partnerships with companies like Google and AccuWeather.

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Abstract:

       Although meritocracy is a key organizing principle of the international scientific community, science is also a highly structured endeavor, and much remains unclear about how to interpret either individual and institutional differences in scholarly outputs or impact. For instance, how much is the reach of a good idea determined by who has it? And, which is a better predictor of early-career productivity and prominence, where a scientist was trained or where they currently work?

    In this talk, I'll begin with a structural overview of faculty hiring as a network, of who hires whose graduates as faculty. Using data on more than 240,000 U.S. faculty at PhD-granting institutions, I'll show that prestige is a central determinant of who wins permanent research positions in the U.S. I'll then show that prestige also acts as an idea amplifier, in which the dramatic inequalities in faculty hiring drive epistemic inequalities in the spread of scientific ideas. Finally, I'll describe a causal investigation of the factors that drive large differences in scholarly productivity and prominence, using a comprehensive dataset of publications and citations for nearly 2500 early-career computer science faculty in the U.S. and Canada. Via a matched-pairs experiment around initial faculty job placement, I show that the prestige of an individual's working environment is a causal driver of their productivity and prominence, not the prestige of their doctoral training. I'll then show how the productivity effect can largely be explained by a labor advantage, in which researchers at high-prestige institutions benefit from the productivity of large research groups. Hence, the scholarly output of early-career faculty is driven by where they work, not by where they trained, and their current productivity and prominence cannot be separated from their place in the academic system. I close with a brief discussion of the implications of these results for the emerging field of the science of science, and for academic policy.

Biography:

    Aaron Clauset is a Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute. In 2016, he was awarded the Erdos-Renyi Prize in Network Science, and since 2017, he has been a Deputy Editor responsible for the Social, Computing, and Interdisciplinary Sciences at Science Advances.

    Clauset is an internationally recognized expert on network science, data science, and machine learning for complex systems. His research program is around two general themes: identifying fundamental principles of the organization and behavior of complex social and biological systems, and developing approaches for using data and computation to illuminate those ideas. A recent major focus of this work has been on the "science of science," where he studies the shape, origins, and consequences of social and epistemic inequalities on scientific careers, productivity, the spread of ideas, and the composition of the scientific workforce. His research results have appeared in many prestigious scientific venues, including Nature, Science, PNAS, SIAM Review, Science Advances, Nature Communications, AAAI, and ICDM. His work has been covered in the popular press by Quanta Magazine, the Wall Street Journal, The Economist, Discover Magazine, Wired, the Boston Globe and The Guardian.

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Abstract:

    Recommendations have become a ubiquitous part of our daily online experience, for example on e-commerce or video streaming sites. Given their widespread use in industry and the multitude of algorithms that were invented during the last two decades, one might think that the problem of building a recommender system is solved. In reality, however, designing, engineering, and implementing a successful system that creates substantial business value for an organization for a given application use case can be challenging. In this talk, we discuss some of the reasons why.

Biography:

    Dietmar Jannach is a professor of computer science at the University of Klagenfurt, Austria. His main research theme is related to the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In recent years, he worked on various topics in the area of recommender systems. In this area, he also published the first international textbook on the topic.

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Abstract:

    In Hong Kong, the number of elderly citizens is estimated to rise to one third of the population, or 2.37 million, in year 2037. As they age and become more frail, the demand for formal support services (e.g., providing domestic or escort services will increase significantly in the coming years. However, there is a severe lack of manpower to meet these needs. Some elderly-care homes reported a 70% shortage of employees. There is thus a strong need of voluntary or part-time helpers for taking care of elders.
    In this talk, I will introduce HINCare, a software platform that encourages mutual-help and volunteering culture in the community. HINCare uses the HIN (Heterogeneous Information Network) to recommend helpers to elders or other service recipients. The algorithms that use HINs and Al technologies for matching elders and helpers are based on our recent research results. This is the first time that HIN is used to support elderly care.
    HINCare is now downloadable in Apple and Google Play Store, and has been serving more than a thousand of elders and helpers in NGOs (e.g., SKH and CSFC). The app is originally designed for elderly users, but has now expanded its services to support the Community Investment and Inclusion Fund (CIF) and 10 NGOs engaged in teenage and family services. The system won the HKICT Award 2021, two Asia Smart App Awards (2021 and 2020), and the HKU Faculty Knowledge Exchange Awards 2021 HKU.

Biography:

    Prof. Reynold Cheng is a Professor of the Department of Computer Science in the University of Hong Kong (HKU). His research interests are in data science, big graph analytics and uncertain data management. He was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005.
    Prof. Cheng received the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He received the Universitas 21 Fellowship in 2011, and two Performance Awards from HKPU Computing in 2006 and 2007. He is an academic advisor to the College of Professional and Continuing Education of HKPU. He is a member of IEEE, ACM, ACM SIGMOD, and UPE. He was a PC co-chair of IEEE IDE 2021, and has been serving on the program committees and review panels for leading database conferences and journals like SIGMOD, VLDB, ICDE, KDD, IJCAI, AAAI, and TODS. He is on the editorial board of KAIS, IS and DAPD, and was a former editorial board member of TKDE.

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