Keynote Speakers
Ronjon Nag
AI: where did it come from and where is it going
Adjunct Professor in Genetics, Stanford School of Medicine – AI and Longevity papers here
Fellow, Stanford Center for the Study of Language and Information
President, R42 Group
Presentation Abstract
Artificial intelligence is in the news daily. Where did it come from, and where is it going ? The field arguably dates back to Alan Turing in 1936 and perhaps even earlier. Modern AI is based on neural networks popularized in the 1980s and now have been morphed to generative AI and large language models. Large language models have commoditised the accessibility of AI to all, resulting in exponential creativity and applicability. With possibility that AI will be clverer and quicker than humans in every request, we will discuss the societal and ethical issues surrounding the real-world applications of AI and consider what the future of AI can hold and what barriers need to be overcome with current AI models.
Dr. Ronjon Nag is a renowned inventor, entrepreneur, and educator, with over 40 years of experience in developing AI technologies for smartphones, neural networks, and biotechnology. He is the Founder and President of R42 Group, a family office and venture group that invests primarily in AI, biotechnology, and science, and supports pre-seed stage companies in their growth journey.
He is also an Adjunct Professor in Genetics in the Stanford School of Medicine and a Visiting Fellow at the Stanford Center for Language and Information (CSLI), where he teaches popular courses on AI, Genes, Ethics, Longevity Science, and Venture Capital. He is a Fellow and Trustee of the Institution of Engineering and Technology (IET) and a Lifetime Member of the ACM. He has won multiple prestigious awards, including the IET Mountbatten Medal, the Verizon Powerful Answers Award, the COGX AI Lifetime Achievement Award, the MIT Great Dome Award and is the 2024 Silicon Valley Engineering Council Hall of Fame Inductee. He is a founder, advisor, board member, and part owner of some 100 AI and Biotech start-ups, and has sold his companies to Apple, BlackBerry, and Motorola. He is passionate about inventing, informing, and investing in the future of humanity. He has a BSc (Birmingham), an MS (MIT), and a PhD (Cambridge).
• First laptop with speech recognition built-in (UK: Apricot Computers, 1984)
• First selling cursive handwriting recognition system (with Lexicus, 1992)
• First Chinese speech recognition dictation system (Lexicus/Motorola, 1996)
• First speech recognition phones (Lexicus/Motorola, 1995)
• First Chinese predictive text system on a phone (Lexicus/Motorola, 1997)
• First predictive text systems in large volume (40 languages) on phones (Lexicus/Motorola, 1997)
• First touch screen phone with apps, HTML browser, speech & handwriting recognition (Lexicus/Motorola, 1999)
• First combined mobile app store and search engine (with Cellmania, 2000-2010)
• First operator billable BlackBerry App Store (2011)
• First Neural Network Artificial Intelligence System in the Cloud (Ersatz Labs, 2014)
• First Wireless Throwable Camera (Bounce Imaging, 2015)
• First Android powered smarthome lightswitch (Brightswitch, 2017)
• First continuous blood pressure wearable (GT Cardio 2019)
• First holographic phone call (R42 – Vivid-Q 2021)
• First AutoML AI platform for biologists requiring no code (Superbio.ai 2021)
• Proposed the first vaccine for aging developed using AI (Agemica.ai 2023)
Wei-Tek Tsai
Data Assets as Digital Assets in Web3
Fuzhou University, Fuzhou, China
Recently two concepts, data assets and digital assets, have received significant attention. However, a close examination of these concepts show that while both are considered as “digital” assets, but these two concepts have not encountered each other with respect to underlying technology, protocols and infrastructure.
Data assets currently emphasize on the “data” aspects, such as related to database technology like data collection, cleansing, analytic, valuation, IP and copyrights, and artificial intelligence. The tough problems of data assets are related to valuation, ownership, protection, and privacy.
Digital assets currently focus on Real-World Assets (RWA), smart contracts, blockchain, transactions, exchanges, wallets, regulations such as KYC (Know Your Customer) and AML (Anti-Money Laundering), and face a different sets of tough problems such as transaction models, settlement mechanisms, new Internet architecture and protocols.
In this talk, we propose data assets to be treated as digital assets, and use the protocols and infrastructure initially designed for digital assets for data assets. In this way, the tough problems of data assets can be addressed, and data assets and other digital assets can be treated in an uniform manner on the Web.
Currently, there are multiple definitions for the next-generation Internet, or Web3. A specific definition of Web3, as proposed by EU Commission in 2017, is an Internet of human, or an Internet of trust, and EU has initiated large Web3 projects. Following the EU Web3 definition, data assets must be protected and trusted like digital assets. As the EU version of Web3 is fundamentally different from the current Internet (Web2），if we treat data assets as digital assets, the market and transaction model for data assets will be fundamentally changed.
Short Bio
Wei-Tek Tsai received S.B. in Computer Science & Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, and M.S. and Ph.D. in Computer Science, University of California at Berkeley, Berkeley, CA, USA. He has been a professor at several universities including University of Minnesota, Arizona State University, and Fuzhou University. He has authored more than 600 papers and 9 books, H-index 67. He has done research in blockchain, sandbox systems, distributed systems, computer networks, and software engineering.
Jie Xu
Optimizing Training for Large Language Models: Balancing Various System Forces
Chair of Computing at the University of Leeds
In this presentation, we will share our recent experience in designing and implementing a system for training large language models (LLMs) with billions of parameters. Our focus will be on balancing various system requirements and design objectives, such as effectiveness and efficiency.
We introduce a theorem of “Impossible Trinity of System Sm” where Sm is a system designed and implemented for training LLMs. This theorem provides guidance for balancing different design requirements and informs various design options to make LLM training more cost-effective and efficient. The theorem formalizes three desirable objectives of LLM training systems and asserts that it is impossible to achieve all three simultaneously within Sm. Therefore, system designers must prioritize two out of the three properties based on the specific requirements and constraints of their application. We will use several practical examples to illustrate how to design and implement a well-balanced LLM training system.
Short Bio
Professor Jie Xu is Chair of Computing at the University of Leeds, Director of the UK White Rose Grid e-Science Centre, involving the three White Rose Universities of Leeds, Sheffield and York, a co-Leader of the EPSRC-funded UK National Hub in Clouds and Distributed Computing, and Head of the Distributed Systems and Services (DSS) Theme at Leeds. Xu has worked in the field of Distributed Computing Systems for over thirty-five years, engaging closely with industrial leaders in the field. He received a PhD in Computing Science from the University of Newcastle upon Tyne, and was Professor of Distributed Systems at the University of Durham before joined Leeds in 2003.
Professor Xu is an executive member of UKCRC (UK Computing Research Committee) and a Turing Fellow in AI and Data Science. He has served as an academic expert for numerous governments and industries, such as Singapore IDA, Lenovo, UK EPSRC, and UK DTI (InnovateUK). In addition, he has extensive editorial experience, having served as an editor for IEEE Distributed Systems from 2000 to 2005, and currently acting as an associate editor of IEEE Transactions on Parallel and Distributed Systems and ACM Computing Surveys. Professor Xu is a Steering Committee member for several prestigious IEEE conferences, such as SRDS, ISORC, HASE, SOSE, JCC, and CISOSE, as well as serving on the steering board of IEEE TC on BIS. He has also been a General Chair/PC Chair for various IEEE international conferences. With over 300 academic publications, including papers in top-ranked IEEE and ACM Transactions, Professor Xu has received international research prizes, such as the BCS/AT&T Brendan Murphy Prize, and led or co-led more than 20 research projects worth over £30M. He is also the co-founder of two university spin-outs that specialize in data analytics and AI software for optimizing data centre performance and in co-simulation and digital twins.
Hong Zhu
Scenario-based Testing and Evaluation of Large Language Models for Code Generation
Professor of computer science at the Oxford Brookes University, Oxford, UK
One of the most valuable capabilities of large language models (LLM) like GPT and Gemini etc. is to generate program code with natural language input. However, it remains an open question that how well such LLMs performs in real-world practical uses. In this talk, we report our ongoing efforts in the development of benchmarks and automated test systems, our experiments with testing ChatGPT’s capability of generating Java and R programs, and the evaluations of its usability using a scenario-based methodology. We will share our novel technology that enables effective and efficient scenario-based testing of machine learning (ML) through benchmarks marked-up by metadata, new quality attributes and metrics of ML usability, and a datamorphic test system to achieve test automation, etc. We will also report our discoveries and the main results, discuss the directions for future research.
Hong Zhu is a professor of computer science at the Oxford Brookes University, Oxford, UK, where he chairs the Cloud Computing and Cybersecurity Research Group. He obtained his BSc, MSc and PhD degrees in Computer Science from Nanjing University, China, in 1982, 1984 and 1987, respectively. He was a faculty member of Nanjing University from 1987 to 1998. He joined Oxford Brookes University in November 1998. His research interests are in software development methodologies, including software engineering of cloud-native applications, software engineering of AI and machine learning applications, formal methods, software design, software testing, programming languages, software modelling, and automated software engineering tools and environments, etc. He has published 2 books and more than 200 research papers in journals and international conferences. He is a senior member of IEEE, a member of British Computer Society, and ACM.
James Ong
Aligning AI with “Sustainable AI for Humanity”
Founder & Managing Director, Artificial Intelligence
International Institute (AIII) and Adjunct Professor, Singapore University of Technology & Design (SUTD) & AI Mega Centre
AI Alignment is one of the most controversial and urgent challenges faced in AI today. With the dissolution of the SuperAlignment project at Open AI with the departure of key executives leading the project, it shows that the complexity and uphill tasks faced. James will share his perspective how to tackle AI Alignment with the proposed holistic approach of “Sustainable AI for Humanity” balancing AI technology, commercialisation and governance.
Dr. James Ong has 38+ years of experience as an entrepreneur, tech executive, venture builder, author and professor and enjoys his journey of ecosystem building for impactful outcomes, bridging scientific research, startups, and impact investment. He founded Artificial Intelligence International Institute (AIII), a think tank advocating Sustainable AI for Humanity and is co-author of “AI for Humanity: Building A Sustainable AI for the Future” (ISBN: 9781394180301), to be published by Wiley on June 4, 2024. He is CEO of Origami Frontiers, a venture building firm, Venture Partner at Delight Capital, Partner at Hashtaqs and Adjunct Professor at SUTD and AI Mega Centre. He received his PhD from University of Texas at Austin specializing in AI for Governance and Business Process Automation.
Jerry Gao
Smart Agriculture Machine Learning for Today and Tomorrow Using Satellite, Remote Sensing UAV, and IOT Big Data
Professor, Computer Engineering Department and Applied Data Science Department, Research Center Director, San Jose State University
Presentation Abstract
Recent fast advance of artificial intelligence techniques brings many great opportunities and applications in smart cities. Smart agriculture is one of hot topics. In this talk, Dr. Gao first review the current state-of-the-art intelligent technologies and approaches for smart agriculture in three perspectives, including smart crop-farming, smart livestock farming, and smart fruit ranches. Dr. Gao discusses and reports the integrated data-driven machine learning approaches and machine learning models based on 3D big data, including satellite, remote sensing, UAV, and IOT data. The proposed intelligent solutions support smart agriculture in smart crop farming supporting crop identification, soil analysis, crop monitor, disease detection and progress prediction, and product estimation as well as crop trading. In addition, a smart agriculture cloud is presented to implement the presented intelligent solutions for smart crop farms and precising farming.
Jerry Zeyu Gao is a professor at the Department of Computer Engineering at San Jose State University. Now, he is the director of SJSU research center on Smart Technology, Computing, and Complex Systems. He had over 20 years of academic research and teaching experience and over 10 years of industry working and management experience on software engineering and IT development applications. He has published three technical books and over hundreds (320) publications in IEEE/ACM journals, magazines, international conferences and workshops. His Google Scholar citation is over 8.9K and his ResearchGate reads is over 330K. His current research areas include smart cities, intelligent system test automation, AI and machine learning, cloud computing and mobile clouds, smart cities, and cyber physical systems. In 2010, Jerry Gao has been recognized by University of Texas at Arlington as a distinguished Alumna for College of Engineering at its 50th anniversary. In 2011, he was award as a KSI Fellow in SEKE2011. In 2013, Dr. Gao has received the College of Engineering Faculty Award for Excellence in scholarship, Dr. Gao served as an editorial board member and an associate editor for several international journals in software engineering and electronic commerce, such as IEEE Software, MDPI Software Journal, Journal of AI and Technology, Service Oriented Computing and Applications, Journal of Smart Cities and Society, and Scientific Publisher’s Journal of IJSEKE. Recently, Dr. Gao has been included and listed in Marquis Who’s Who 2020-2021.
In last 10 years, Dr. Gao has played as one of leaders in organizing many international conferences and workshops as a conference co-chair, program co-chair, and workshop co-chair. Now, he is the steering board chair for IEEE CISOSE congress and the steering board member for IEEE Smart World Congress, The IEEE CISOSE congress include six IEEE conferences: IEEE BigDataService, IEEEAITest, IEEE AI Mobile Cloud, IEEE SOSE, and IEEE DAPPS. In addition, he served as a chair for many other conferen.
Jane Wu
AI: The Unsung Hero of Energy Efficiency
Chair, Industry Alliances
The global energy landscape urgently needs a clean and sustainable transformation. However, integrating renewable sources and optimizing energy use comes with signiffcant challenges. Artiffcial intelligence (AI) is emerging as a game-changer, accelerating this transition. By analyzing vast datasets and identifying complex patterns, AI applications are transforming how we generate, manage, and consume energy.
We have been exploring the key technical mechanisms of AI in clean energy. AI is optimizing smart grids, ensuring grid stability, and reducing reliance on fossil fuels by balancing supply and demand from variable renewable sources. AI algorithms are also predicting renewable energy generation with greater accuracy, allowing for smoother integration into the grid. Predictive maintenance powered by AI analyzes sensor data to identify potential equipment failures in wind turbines, solar panels, and power lines. This proactive approach minimizes downtime and maximizes energy production. Additionally, AI is empowering consumers to adjust energy use based on real-time pricing and grid conditions, promoting efficiency and cost savings. These advancements, along with optimized energy storage systems using AI, contribute to a more reliable and sustainable energy future.
Short Bio
Her tenure at TD Growth Capital further highlights her prowess in guiding cleantech sector companies through strategic planning and identifying optimal exit strategies. Her strategic acumen was also evident in her President role at JA Solar, where she forged vital global alliances and spearheaded the company’s marketing and supplier chain strategies. Her industry experience is further enriched by her 11-year tenure at Applied Materials, where she held various technical and managerial positions, and her initial foray into device process engineering at Rockwell Semiconductor.
Jane’s entrepreneurial journey includes co-founding CiWest Corporation, which successfully merged with a major semiconductor foundry. Her academic foundation is as impressive as her professional journey, with a degree in Physics from the prestigious California Institute of Technology (Caltech). Her extensive experience and dynamic leadership have consistently generated innovative strategies and growth, marking her as a transformative leader in her feld.
Fei Wu
LLM for domain-specific tasks
Presentation Abstract
In recent years, some large language models (e.g., OpenAI’s ChatGPT, and Google’s PaLM) have been shown to exhibit more general intelligence than previous AI models across a variety of domains and tasks. These LLMs can generate novel and unexpected responses—a significant departure from earlier routine models that were limited to generating predictable and formulaic responses. In this talk, I will introduce how to train domain-specific LLM for certain tasks such education and legal domains. The main topics consist of domain-specific SFT and the integration of data-driven and knowledge-guided techniques.
Fei Wu received his B.Sc., M.Sc. and Ph.D. degrees in computer science from Lanzhou University, University of Macau and Zhejiang University in 1996, 1999 and 2002 respectively. From October, 2009 to August 2010, Fei Wu was a visiting scholar at Prof. Bin Yu’s group, University of California, Berkeley. Currently, He is a Qiushi distinguished professor of Zhejiang University at the college of computer science. He is the deputy dean of Shanghai Institute for Advanced Study of Zhejiang University, and the director of Institute of Artificial Intelligence of Zhejiang University. He is the chairman of IEEE CAS Hangzhou-Chapter since Oct, 2018.
He is group leader of artificial intelligence innovation action plan of the Ministry of Education, the Section Executive Editors-in-Chief of Engineering, editorial members of Frontiers of Information Technology & Electronic Engineering. He has won various honors such as the Award of National Science Fund for Distinguished Young Scholars of China (2016). His research interests mainly include Artificial Intelligence, Multimedia Analysis and Retrieval and Machine Learning.
Haibo Chen
Efficient Serverless Computing with Novel OS Primitives: Characterization, Optimization and Reflection
Distinguished Professor of Shanghai Jiao Tong University
Presentation Abstract
Serverless computing promises cost-efficiency and elasticity for high-productive software development. To achieve this, the serverless computing platform must address two challenges: strong isolation between function instances, and extremely low startup latency. In this talk, I will first present a characterization of state-of-the-art serverless platform and derive several key metrics, which collectly forms a systematic methodology and a benchmark called severlessbench (v1 and v2). Then, I will show how serverless platform can be made efficient with novel OS primitives for both normal and confidential serverless computing on CPU-only and CPU-XPU platforms. Finally, I will give a reflection on the gap between serverless research and real-world systems and present an outlook on future serverless computing.
Haibo Chen is a Distinguished Professor of Shanghai Jiao Tong University, where he directs the Institute for Parallel and Distributed Systems (IPADS). His main research areas are operating systems and distributed systems. He received Best Paper Awards from SOSP, ASPLOS, EuroSys and VEE, Test of Time Award from DSN, Best Paper Honorable Mention and Research Highlight Award from SIGMOD, Honorable Mention of The Dennis M. Ritchie Thesis Award (Advisor) from SIGOPS. He currently chairs ACM SIGOPS, serves on the editorial board member and co-chair of Special Sections of Communications of the ACM, Program Committee of SOSP 2023/OSDI 2024, PC co-chair of EuroSys 2025, and the inaugural technical steering committee chair of OpenHarmony, an open-source operating system deployed on hundreds of millions of devices. He is an ACM Fellow and IEEE Fellow.
Jinfeng Zhou
Ethical AI with Ecology Perspective
Secretary-General of China Biodiversity Conservation and Green Development Foundation (CBCGDF)
Fellow of the World Academy of Art and Science
Presentation Abstract
The integration of Artificial Intelligence (AI) into ecological conservation presents a transformative opportunity to address the escalating challenges facing global ecosystems. AI’s capabilities in real-time monitoring (e.g. the Anti-electrofishing Network and “RiverEye” APP, the Lifeline of Migratory Birds), species identification (e.g. underwater biodiversity monitoring by Australian scientists), and predictive modeling (e.g. GBIF and Citizen Science) offer unprecedented tools for safeguarding biodiversity and managing natural resources. However, the ethical and practical implications of AI’s role in conservation necessitate a nuanced understanding of its potential and limitations.
The speech explores the multifaceted impact of AI on ecological preservation, emphasizing the critical role of human values in shaping AI’s trajectory, including UNESCO Recommendation on the Ethics of Artificial Intelligence. While AI can enhance conservation efforts, it is not inherently benevolent towards nature or life; this disposition must be instilled by conscious human guidance. By listing specific applications of AI in conservation, such as real-time forest fire detection and wildlife population analysis, the efficiency gains in energy management and emissions reduction through AI-driven solutions can be highlighted. Yet, it also confronts the paradox of AI’s energy consumption, which, despite optimizing energy use elsewhere, demands substantial resources for its own operation. This raises questions about the sustainability of AI-driven conservation. Furthermore, the evolving relationship between ecology and AI should be examined, questioning whether self-replicating, self-repairing AI systems should be considered as life forms and subject to ecological laws. It posits that ecological principles should guide the development of AI, ensuring that technological advancement aligns with sustainable practices.
The ethical dimension of AI in conservation is brought to the fore when considering scenarios where AI-controlled machines or organisms created by AI-enhanced synthetic biology techniques could replace the ecological functions of certain species. The audience are to be challenged to contemplate whether and how we should protect such species in the face of technological substitution. It underscores the need for ongoing ethical reflection and strategic adaptation as AI technology advances. In conclusion, a balanced approach to AI in ecological conservation should be advocated, which leverages its capabilities while remaining vigilant to its potential pitfalls. It calls for a concerted effort to infuse AI with a reverence for nature and respect for life, ensuring that it serves as a tool for ecological stewardship rather than a substitute for human responsibility in the preservation of our planet’s biodiversity.
Dr. Jinfeng Zhou is the Secretary-General of China Biodiversity Conservation and Green Development Foundation (CBCGDF), who holds PhDs and Post Doctorates from Peking University and Purdue University. Zhou has held numerous significant positions, including Member of the Chinese People’s Political Consultative Conference (CPPCC) (9-11 sessions) Standing Committee Member of the All-China Federation of Industry and Commerce and Deputy President of China Non-Governmental Science Technology Entrepreneurs Association (CASTE-NG). He is a Fellow of the World Academy of Art and Science, an Executive Committee Member of the Club of Rome, and an expert in multiple global environmental initiatives. Zhou has pioneered several environmental theories and practical conservation strategies, notably “Equal Rights of Carbon” (ERoC), “Biodiversity Conservation in Our Neighborhood” (BCON), “Four Principles of Environmental Governance”, “Three Axioms of Ecological Restoration”, “Human-based Solutions” (HbS), and “Community Conservation Areas” (CCAfa). Zhou has also been instrumental in Environmental Public Interest Litigation (EPIL) in China, leading significant cases to protect wildlife and public health. He is a prolific author and advocate for holistic approaches to address biodiversity loss, climate change, and public health crises through books and papers. His efforts have earned him multiple awards and honors such as 2021 Most Influential People of The Year by China Newsweek, reflecting his commitment to environmental protection and sustainability.
Lei Xing
ESG and Sustainability
Director-General of the World Green Design Organization
Chairman of Beijing Dragon Design Foundation
With public welfare and branding as a means, Xing Lei has been committed to promoting innovation and green development. He initiated the establishment of public welfare organizations, such as Beijing Dragon Design Foundation, International Design Federation, World Green Design Organization, Beijing Green Design Promotion Association, and Beijing Service Design Promotion Association. Mr. Xing Founded lots of pilot Projects, like DDF Award (a National Award in China), Green Design International Contribution Award/Green Design International Award, Dragon Design Festival, China Design Hall of Fame, World Green Design Forum, China Service Design Conference, Green Haven, Green Leaf Mark, Global Green Development Think Tank. And Promotes the construction of social-enterprise platforms such as Design Valley and Green Research Institute. He is the editor and author of “China Youth Design Innovation Ceremony”, “Entrepreneurship in China”, “Design Wins the World”, “China Green Design Report” and so on.
He joined the China Democratic Construction Association in 1998 and served as a member of the Qingdao Municipal Committee of the China Democratic National Construction Association, a member of the Qingdao Municipal Committee of the Chinese People’s Political Consultative Conference, a member of the Beijing Youth Federation, and a member of the Standing Committee of the Beijing Daxing District Committee of the Chinese People’s Political Consultative Conference for two terms. Under warm invitation, He is now a member of the China Committee of the Club of Rome.
John Yu
Smart Machine in Traditional Chinese Medicine
Co-founder/CEO of Beijing Smart Health Technologies Co.
Ph.D. in Electrical Engineering from California Institute of Technology (Caltech);
Former Technical Director/Architect of AT&T (a Fortune 500 company).
Presentation Abstract
Traditional Chinese Medicine (TCM) represents a holistic healthcare paradigm cultivated over millennia in China. It perceives the human body as a multifaceted network of interconnected systems, prioritizing the equilibrium between Yin and Yang, central tenets of Taoist philosophy, which symbolize opposing forces pervasive in existence.
TCM integrates a sophisticated array of diagnostic and therapeutic approaches honed through extensive empirical observation, theoretical elucidation, and clinical application. Through comprehensive assessment encompassing symptoms, medical history, tongue examination, pulse analysis, and other clinical indicators, TCM practitioners discern patterns of disharmony, known as Pattern Differentiation, categorized according to Eight Principles: Yin/Yang, Exterior/Interior, Cold/Heat, and Deficiency/Excess. By discerning the interplay of these principles, practitioners delineate the patient’s unique Pattern Differentiation, elucidating the nature and location of imbalances within the body. Subsequently, they apply diverse therapeutic modalities, including herbal medicine, acupuncture, massage, and dietary interventions, to reinstate equilibrium and harmony. Moreover, TCM encompasses diverse schools of diagnostic and therapeutic methodologies, delineating practitioners’ orientations based on their education and training. Tailoring treatments to individual needs, TCM adopts a holistic perspective, addressing not only physical manifestations but also emotional, mental, and environmental factors. This comprehensive approach endeavors to target the root causes of illness and foster overall well-being, solidifying TCM as an enduring healthcare system.
In this presentation, we introduce the TCM Smart Machine, an AI-driven system designed to simulate TCM diagnostic and therapeutic methodologies. Comprising a foundational core brain, specialized application models, and an architecture facilitating brain-agent interactions, this system forms a complex adaptive system (CAS) primed for large-scale TCM practice. Leveraging three-level integrated machine learning—encompassing feature learning, enhancement learning, and adaptive system learning—the TCM Smart Machine can assimilate various TCM inheritance schools and refine its performance in real-world service settings. This innovation facilitates TCM practice, preserves its heritage, modernizes its methodologies through the integration of AI and complex system science, and shed the light toward the advancement of future systems medicine.
Short Bio
Dr. John Yu is the co-founder and CEO of Meridian Smart Health Technologies Co. in Beijing, overseeing the development of the AI Brain of Traditional Chinese Medicine (TCM) for diverse healthcare applications. He obtained his PhD in Electrical Engineering from California Institute of Technology (Caltech). He was a system architect and engineering director of AT&T for intelligent network system design, optimization and engineering. His R&D interests are in complex system and network, AI brain and ubiquitous AI agents for healthcare. He led his team developed the first TCM Virtue Doctor System (TCM AI Brain and Agents) in the industry. He is an expert of the Institute for AI International Governance of Tsinghua University, and the Chief Expert of the Working Committee of Comprehensive Reform of TCM in China.
Wu Wen
Digital Financing and Sustainable development
Professor of Computer Science and Technology
Professor of International Business School of Zhejiang University
Presentation Abstract
Climate change is becoming an ever-urgent challenge to mankind despite the past thirty years efforts by United Nations, scientists, and government. The division in geopolitics between east and west, and in economy between the north and the south is not helping with the current approach. Carbon monetary laboratory is a project originated by a group of people working in public money, green finance, and climate science with the goal of focusing the various efforts mentioned above under a public monetary approach using carbon credits as a sovereign asset. This requires global collaboration leveraging the governance infrastructure provided and proven by the past decade’s experience in blockchain based digital financial system.
Short Bio
Dr. Wu Wen is currently a professor at the International Business School of Zhejiang University and the Secretary-General of the International Organization for Central Bank Digital Currency (CBDC). He graduated from the University of Oxford in the United Kingdom with a Ph.D. in Computer Science. Dr. Wu’s primary research areas include fintech, digital currency, blockchain, and cybersecurity.
Research and Teaching Experience: After obtaining his Ph.D. from the University of Oxford, Dr. Wu conducted postdoctoral research at NTT Communication Science Laboratories in Japan and NASA’s Software Institute in the United States. He later became the youngest associate professor to join Tokyo Institute of Technology. During his visit to Stanford University, he participated in the U.S. Department of Defense’s Common Access Card (CAC) project, serving as the Chief Security Architect responsible for managing the implementation of the U.S. federal government’s Personal Identity Verification (PIV) project. Dr. Wu also co-founded eCurrency in Silicon Valley, the first company to provide central bank digital currency solutions. He collaborated with the Digital Currency Research Institute of the People’s Bank of China to establish the International Telecommunication Union (ITU) Digital Legal Currency Focus Group, serving as its chairman. He co-authored the book “Legal Digital Currency” with Professor Chen Baoshan, published by the People’s Finance Publishing House. Dr. Wu holds multiple patents in digital processing, digital payment, and communication security.
Fanjing Meng
Responsible AI: Building Trust and Ethics in the Age of AI
Chief Technology Officer of IBM China System Development Lab
Presentation Abstract
The rapid advancement and integration of Artificial Intelligence (AI) into various aspects of our lives demand a heightened focus on responsible design, development, deployment, and use. This talk will delve into the core principles of Responsible AI, focusing on explainability, fairness, robustness, transparency, and privacy in AI systems. We will address the challenges and provide practical solutions for achieving these goals, including regulatory frameworks, technical methodologies, and organizational policies. Through case studies from diverse industries, we will highlight successful implementations and key lessons learned. Attendees will gain valuable insights into building trust with stakeholders, mitigating risks, and fostering innovation while maintaining high ethical standards in AI.
Short Bio
Dr. Fanjing Meng, Chief Technology Officer of IBM China System Development Lab, has more than 20 years of cutting-edge technology research, development and management experience, including sustainable computing and AI, AIOps, ITOA, cloud computing, software and solution engineering and etc. Currently, she is committed to the research and development of sustainable computing technologies by building a full-stack sustainable computing optimization and management platform based on IBM systems, software and services to accelerate the realization of sustainable computing and sustainable digital transformation of enterprises. She has published more than 30 academic papers in international conferences and journals, has more than 40 international patents and patent applications in many innovative fields, and has received more than 30 awards for technological innovation and contribution from IBM and IEEE. In addition, she is actively involved in the establishment and construction of technical and academic communities, serving as the General Chair(Co-Chair), Technical Program Committee Chair(Co-Chair), Technical Program Committee Member of International Conferences and reviewers of International Journals, as a founding member and project leader of the IEEE WIE (Women-in-Engineering) Beijing Affiliate, and as a member and invited speaker of IEEE Women in Services Computing (WISC).
Daniel Zhu
AI in transition towards silicon life
Honorary Chairman of FTS 2024 IEEE
Presentation Abstract
The philosophical thinking of AI in the transition from carbon-based to silicon-based life tries to answer that whether AI is dangerous and where we are heading to. It touches on fundamental questions of what it means to be alive, the nature of intelligence, and the role of technology in shaping life’s future. In essence, AI’s involvement in this transition forces us to confront deep philosophical questions about life, intelligence, consciousness、and our position in the history of universe.
Short Bio
Honorary Chairman of FTS 2024 IEEE；Mentor of the Global Challenge Laboratory at Imperial College London, UK; Senior Advisor, AIGC, National Science Innovation Think Tank, Chinese Academy of Sciences; Founder and 2015 Who’s Who of the Year of x’lab at Tsinghua University; China Committee of the World Wildlife Fund (www.wwf. org) – Ecological Environment; Former Project Director, China, The conference Board；Former General Manager of Waste Management inc EPC in China, a Fortune Global 500 company; Former investment partner of China Environment Fund initiated by Tsinghua University; Former partner of Enersize China, a Swedish industrial IoT listed company; Former economics faculty at Zhejiang university .
Panel Speakers
This session will explore the dynamic landscape of AI investments, highlighting emerging trends, key opportunities, and the challenges faced by investors in this rapidly evolving field. Our distinguished speakers will share insights on strategic funding, innovative startups, and the impact of AI advancements on various industries, offering valuable perspectives for investors looking to navigate and capitalize on the AI revolution.
Enqiang Wang
Founding Partner, TianDi Fund
Mr. Wang Enqiang, a visionary and trailblazer in the venture capital world. With a remarkable 25-year career in venture capital, Mr. Wang stands at the forefront of innovation and investment excellence. As a distinguished management partner at Shanghai TianDi Fund, he has successfully managed four prominent funds, including Venture TDF China, TDGC I, TDGC II, and Taishan Tianyi. His investment acumen is evidenced by his leadership and participation in managing 178 companies, including renowned names such as Alibaba, Baidu, Focus Media, Huating Power, and Hive Drones. His efforts have led to 11 companies going public and 4 being acquired, showcasing his exceptional ability to drive high-performance outcomes.
Mr. Wang’s expertise lies in early-stage investments and the commercialization of technological innovations. His career is marked by his ability to collaborate with cross-cultural teams and his rich experience in both international and local investments. Prior to his venture capital endeavors, Mr. Wang served as the Deputy Director of the Economic Development Office at the Shanghai Municipal Government’s Research Office and was a lecturer and deputy director of student affairs at Tongji University.
Mr. Wang holds a Master’s degree in Technology Economics and a Bachelor’s degree in Computer Science from Tongji University. He has also completed advanced training in corporate accounting at Shanghai University of Finance and Economics. His dedication to early-stage investment and his ability to bridge the gap between technology and market needs make him a highly respected figure in the venture capital community.
Bing Fu Wang
Director, 2012 Telecom Lab
Short Bio
Wang Bingfu is a distinguished leader in the field of telecommunications with a career spanning over three decades. He began his career as a lecturer at Northwestern Polytechnical University from 1989 to 1997, laying a strong foundation in academia. Transitioning to industry, he held various pivotal roles at Huawei Technologies, where he significantly contributed to technological advancements and standardization.
In the 2.0 era of his career (1998-2008), Wang served as a hardware manager and research department manager, and later as the first product manager for ATM/ESR, spearheading the IPD product strategy. He was instrumental in incubating the CPCI/ATCA and E-5000 core router platforms. As the director of Huawei’s central hardware department, he led the creation of the company’s pre-research department and formulated Huawei’s 2008 self-preservation strategy and pre-research standard patent linkage strategy. As general manager of the 707 server product line, he oversaw the development of the industry’s first flash-based storage disk, which supported big data searches.
During the 3.0 era (2009-2018), Wang held the position of director of the Communication Technology Laboratory at Huawei, where he played a key role in leading the IEEE 802.11.ax (Wi-Fi 6) standard and spearheading the 3GPP 5G standards initiative.
Wang’s extensive experience and contributions to the telecommunications industry make him a highly respected figure, and his insights continue to shape the future of global communication technologies.
De Ji Chen
Fellow of the International Society of Automation (ISA) and National High-Level Overseas Talent
Dr. Deji Chen graduated from the Department of Computer Science at the University of Texas at Austin in 1999. He began working at Emerson Process Management in 1995, where he was a core developer of the Distributed Control System and participated in the formulation of international standards such as OPC and WirelessHART (IEC62591).
In 2014, Dr. Chen returned to China and is currently the Technical Director of the Industry 4.0 Laboratory at Tongji University. He is also a Chinese expert in the ISO/IEC Internet of Things (IoT) Standards Committee and the chief editor of the “Real-Time IoT Framework” standard published in 2021. Additionally, he contributed to the Ministry of Industry and Information Technology’s 2017 “White Paper on Cyber-Physical Systems” and “White Paper on Industrial Internet Platforms.”
Dr. Chen has published over 70 papers, authored and translated 7 monographs, and holds 13 U.S. patents. His book “Wireless Control Foundation” received the ISA’s 2015 Raymond D. Molloy Award. He also holds developer certifications for Hadoop and HBase from Cloudera, a globally renowned big data services company.
Jeff Lin
iGlobe Partner
Short Bio
Jeff Lin is a Partner at iGlobe Partners, a Singapore based cross border VC firm focusing on SaaS, AI and deep tech. Prior to becoming a VC, he was with Roland Berger Strategy Consultants, apart from his various technical and commercial roles with Nokia, Hutchison Whampoa and Huawei in the earlier part of his career.
Jeff is an alumnus of University of Science & Technology of China, as well as INSEAD and the Wharton School.