Meng Xiaoxiao is an Assistant Professor at the Department of Communication, School of Art and Media, Tongji University , where he joined in February 2023 as a master's supervisor. His research focuses on Risk Communication , Intelligent Communication , Privacy Protection , Human-Computer Interaction , and Digital Governance . PhD in Communication from Shanghai Jiao Tong University Joint doctoral training at National University of Singapore (CSC-sponsored) Master of Journalism and Communication from Huazhong University of Science and Technology His scholarly work examines privacy boundary turbulence in digital environments, algorithmic governance, and trust dynamics in political communication. Recent publications analyze ACGN addiction , governance-oriented privacy practices , and platformized youth subcultures . He has presented at top-tier conferences like ICA and AEJMC , including developing measurement scales for privacy distress. Meng serves as a reviewer for SSCI journals ( New Media & Society , International Journal of Public Opinion Research ) and international conferences. His research is supported by grants from the National Ministry of Science and Technology and Tongji University , focusing on AI content governance and user privacy preferences . Award highlights: 2022 Shanghai Outstanding Graduate 2022 AEJMC Best Student Paper (Chaffee-McLeod Top Student Paper)
Wang Jing-Tong is a Lecturer at the School of Art and Media, Tongji University, specializing in Social Media and Information Dissemination. He holds a PhD in Journalism from Fudan University (2015) and has taught Advertising since 2003. Education: BEng (1999) and MA (2003) from Tongji/Huazhong University of Science and Technology, PhD (2015) from Fudan University His research focuses on brand management, digital marketing, and cross-cultural communication studies, with publications on topics like time-honored brand modernization and privacy in digital advertising. Wang's recent articles (2014-2019) analyze structural functionalism in communication theories, media dependency in Otaku culture, and privacy frameworks. His 2016 book The Right to Privacy Not Being Explored: Research on Consumer Privacy Protection in Internet Targeted Advertising expands these themes.
Shangfei Wang is a full Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). His research focuses on pattern recognition, affective computing, and probabilistic graphical models, with significant contributions to facial expression analysis, emotion recognition, and multimodal human-robot interaction. He leads the Key Laboratory of Computing and Communication Software of Anhui Province and has received multiple international competition awards including placements in the OMG Empathy Prediction Challenge and Detecting Depression with AI Sub-Challenge. PhD in Computer Science, USTC (2002) MSc in Electronic Science and Technology, USTC (1999) BSc in Electronic Engineering, Anhui University (1996) His work combines domain knowledge with advanced machine learning techniques, including adversarial learning, dual learning, and multimodal deep regression Bayesian networks. Research themes include: Emotion-aware medical consultation systems Thermal image-based facial recognition Privileged information learning for emotion detection Spontaneous vs. posed expression differentiation EEG and physiological signal integration AI applications in mental health support Key projects include multiple National Nature Science Foundation of China grants and international collaborations with French institutions. He serves as Associate Editor for IEEE Trans. on Affective Computing and ACM Trans. on Multimedia Computing, and organized several international conference tracks.
Zhuozhao Li is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in August 2021 after serving as a Postdoctoral Scholar at the University of Chicago from July 2018 to July 2021. His research focuses on high-performance computing, distributed systems, and cloud/edge computing, with applications across scientific domains. Ph.D. in Computer Science, University of Virginia, May 2018 M.S. in Computer Science, University of Southern California, May 2012 B.E. in Computer Science, Zhejiang University, July 2010 Dr. Li's research spans several critical areas in modern computing infrastructure. His primary focus is on High Performance Computing , where he develops novel approaches to optimize computational efficiency in scientific applications. In Distributed Systems , his work addresses challenges in large-scale system coordination and resource management. His contributions to Cloud/Edge Computing focus on improving virtualization techniques and resource allocation strategies. Additionally, he explores applications in the Internet of Things , examining how distributed computing paradigms can enhance IoT infrastructure. Dr. Li's publication record demonstrates a consistent focus on advancing distributed computing systems and their scientific applications. His recent work shows a clear progression from foundational research on data-parallel frameworks and job scheduling toward more applied systems for scientific computing. His contributions to projects like funcX and DLHub represent significant advancements in making distributed computing more accessible for scientific workflows. A notable trend is the increasing interdisciplinary nature of his work, with applications spanning from computational biology (SARS-CoV-2 research) to privacy-preserving spatial crowdsourcing. ACM HPDC Best Paper Nominees, 2019 IEEEMASS Service Award, 2019 Outstanding Graduate Research Assistant, University of Virginia, 2018 Dr. Li actively mentors students and researchers, currently recruiting PhD students, Master's students, postdocs, and research assistants for his research group at SUSTech. His research has been supported through various collaborative projects, including significant contributions to the U.S. Department of Energy National Virtual Biotechnology Laboratory Project about COVID-19, which was awarded the Secretary of Energy Achievement Award. His work on funcX, DLHub, and Parsl represents major software infrastructure development efforts with broad scientific impact. Dr. Li is affiliated with multiple research initiatives, including the Globus Lab (during his postdoc at University of Chicago) and various collaborative projects involving high-performance computing resources. At SUSTech, he leads a research group focused on distributed systems and high-performance computing. His work often involves collaboration with researchers across institutions, particularly in projects related to scientific computing infrastructure like funcX and DLHub.
