Speakers

Haohuan Fu is a Professor and Vice Dean at Tsinghua Shenzhen International Graduate School. He also serves as Deputy Director of the National Supercomputing Center in Shenzhen. He received his Ph.D. in Computing from Imperial College London in 2009. His research focuses on high-performance computing, supercomputing architectures, software optimization, and AI for Earth system science. He has received three ACM Gordon Bell Prizes for work in atmospheric modeling, earthquake simulation, and random quantum circuit simulation.

High-performance computing has become essential infrastructure for modern Earth system science. This session introduces the basic ideas of supercomputing and explains why large-scale simulation matters for weather, climate, and Earth observation. We will discuss how HPC supports numerical models, data-intensive workflows, and emerging HPC-AI approaches, drawing on recent work from Tsinghua SIGS and the National Supercomputing Center in Shenzhen. The session will show students how computing power, scientific models, software optimization, AI methods, and real-world decision needs come together in Earth system applications such as flood-season forecasting and remote-sensing data compression

Moxian joined Tencent in 2016. He is currently a member of Tencent’s Hunyuan joint project team and WeCityX expert group, focusing on frontier technologies, AI applications and urban technology. He previously served as Ecosystem Operations Director for Tencent Cloud’s Architecture, Engineering and Construction industry sector and participated in several landmark projects, including Tencent’s Binhai Headquarters project. He has led and contributed to Tencent research publications on emerging digital technologies and future technology trends, including as chief editor of 2023 Top Ten Frontier Digital Technology Application Trends. Prior to Tencent, he held product and R&D leadership roles at leading technology companies. He holds a PhD in Computer Science from the UK and has over 15 years of experience across the ICT and internet sectors.

This session will provide an overview of the latest developments in generative AI and large language models, including key technology trends, emerging applications, and their impact on industries and society. Drawing on Tencent’s research and practical experience, the session will explore how AI is reshaping products, enterprises and the broader digital economy

Prof. Dr. Ercan Engin Kuruoğlu is a Full Professor and Ph.D. Supervisor at the Tsinghua University Shenzhen International Graduate School (SIGS). He serves as the Principal Investigator of the Time-Varying Data Science Group, focusing his research on statistical signal processing, machine learning, and information theory. After earning his Ph.D. from the University of Cambridge, he spent two decades as a Chief Scientist at the Italian National Council of Research (ISTI-CNR) before joining Tsinghua University. An IEEE Fellow and Alexander von Humboldt Fellow, his work on non-Gaussian data and causal machine learning is widely applied across remote sensing, telecommunications, and computational biology.

The talks will summarise the current status of AI research and applications and identify the challenges and make a proposal for a new framework for the next generation of AI.

Zihan Geng is an Associate Professor at the Tsinghua Shenzhen International Graduate School (SIGS), specializing in Computational Imaging and Optical Signal Processing. He earned his Ph.D. from Monash University in 2018, later working as a Senior Engineer at Huawei and an Assistant Professor at the Harbin Institute of Technology. His research fuses artificial intelligence with advanced optics to build breakthrough technologies, including a coin-sized 3D camera and ultrafast 3D tracking systems that process data 200 times faster than traditional video methods.

The deep integration of artificial intelligence and optics is reshaping the boundaries of imaging technology. This report will introduce our team's recent explorations and progress in the interdisciplinary field of "AI + Optics". By integrating optical coding devices with artificial intelligence, we aim to overcome the challenges of high complexity, high energy consumption, and high cost in conventional imaging systems, targeting high-speed, high-definition, and hyperspectral spatial perception. In the area of 3D imaging, we propose a high-precision 3D imaging system that combines metalenses with an optical cue fusion network, achieving accurate and robust 3D reconstruction by integrating physically derived absolute depth measurements with machine-learning-estimated relative depth information. For high-speed perception, we realize a 32× super-resolution in imaging rate through dual-channel asymmetric coding. In phase imaging, we present a single-exposure phase imaging method that significantly reduces acquisition time, addressing the challenge of observing dynamic samples. In summary, we propose a new paradigm of optics–computation fusion to advance high-dimensional imaging technologies toward dynamic, intelligent edge-side tools and their practical deployment.

Fang Liu previously served as Vice President, Chief Legal Officer, and Chief Compliance Officer at NIO and ZEEKR. In these roles, she was closely involved in shaping corporate strategy across all stages of development, building global legal and compliance teams from the ground up, and establishing comprehensive governance frameworks. She supported nearly US$20 billion in financing and successful listings on the New York Stock Exchange, and led legal efforts enabling expansion into more than 60 countries and regions. At ZEEKR, she also developed and implemented the company’s sustainability strategy from scratch, contributing to strong investor interest following its IPO.
With over 26 years of experience, Fang Liu has also worked at leading international law firms and Fortune 500 companies, advising multinational corporations, private enterprises, and state-owned entities on cross-border investment and financing, private equity fund-raising, joint ventures, and major infrastructure projects.
She is qualified to practice law in China and New York and holds an executive MBA. Her recognitions include Chambers Global Market Leaders GC Ranking (2026), multiple China Business Law Journal awards, Asian Legal Business China Female In-House Counsel of the Year (2020 and 2021), and inclusion in the Legal 500 China GC Powerlist (2019).

This presentation systematically analyzes global AI regulatory rules, cross-field legal impacts and forward-looking governance trends in four core sections:

1. It sorts out the complete legislative framework of artificial intelligence in China, EU and US covering relevant domestic laws, regulations and targeted AI regulatory provisions.
2. It compares the mainstream AI legal systems of the PRC, the EU and the US, and reviews landmark enforcement cases released recently to reflect practical regulatory enforcement logic in these jurisdictions.
3. It elaborates the multi-dimensional legal and social risks brought by AI technology, covering intellectual property rights, data privacy protection, international trade compliance, as well as social order and ethical norms.
4. It provides illustrative outlooks on the evolving direction of global coordinated AI governance and future regulatory optimization paths.

Overall, this presentation builds a panoramic view of AI regulation: starting with regional legislative frameworks, comparing transatlantic regulatory practices, dissecting cross-domain compliance challenges, and concluding with directions for long-term AI governance development.

Yi joined Tencent Music Entertainment Group in 2019 and served as Vice President of Tencent Music, leading initiatives across new media, intelligent IoT, consumer devices and innovation businesses. His recent work focuses on AI foundation models, new media applications and next-generation intelligent devices. Prior to Tencent, he held management and business leadership positions at UTStarcom, Microsoft, Lenovo, Sohu, Huawei and Qingting FM. He has more than 20 years of experience in business development and organizational management across the media, telecommunications, manufacturing and internet industries.

This session will explore how AI is evolving from standalone tools into integrated systems embedded within enterprise operations. Drawing on Tencent’s practical experience, it will examine AI applications across functions such as R&D, marketing, customer service, data and knowledge management, and discuss implementation approaches, organizational transformation and future trends in enterprise AI adoption.

Jean Herelle is the Co-Founder and CEO of Crunch Lab Inc. and CrunchDAO, a platform dedicated to crowdsourcing institutional-grade AI research from a global network of 12,000+ data scientists, including 1,200+ PhDs. CrunchDAO's work spans quantitative finance and biomedical AI.

Dr. Chao REN is a Professor at the University of Hong Kong. She is the Director of the Msc in Sustainable Environment Design and the Associate Director of the HKU Jockey Club Enterprise Sustainability Global Research Institute. She specializes in applied climatology and climate design. Chao’s multi-dimensional, cross-disciplinary research has transferred scientific data into new knowledge to address social needs, enhance policy-making and support evidence-based designs in China, Taiwan, Singapore, The Netherlands and France since 2006. She is the Awardee of the 2023 University-level Knowledge Exchange Excellence Award and the 2022 Rosie Young 90 Medal Outstanding Young Woman Scholar at HKU. She is also the Recipient of the Timothy Oke Award for early- & mid-career scientists given by the International Association for Urban Climate in 2020.
Chao’s publication with focuses ranging from examining the relationship between urban climate and urban morphological characteristics, developing an urban climatic mapping system and a wind corridor plan, to analysing human thermal comfort and public health risk for subtropical high-density cities. Her latest book is ‘Local Climate Zone Application in Sustainable Urban Development’ published in 2024. She has been named in the World’s Top 2% of Scientists List by Stanford University (2023-25) and Top 1% Scholars by Clarivate (2025).
Chao serves as a co-Chief Editor for Urban Climate and an Editorial Advisor for Cities & Health (2018-), and is a member of Urban Climate Expert Team and the Study Group on Greenhouse Gas Monitoring and of the World Meteorology Organization. She also serves as the steering committee member of the Global Heat Health Information Network and Southeast Asia Heat Health Hub. She has also been elected as a Board Member of the International Association for Urban Climate (2017-2021). She has been involved in several international collaborative research reports, including the IPCC AR6 (Contributing Author of Chapter 6 Cities, Settlements and Key Infrastructure), Climate Change and Cities ARC3.3 (Lead Author of Chapter 2), the China report of the Lancet Countdown on Health and Climate Change (Lead Author of WGII) and the 7th Global Environment Outlook (GEO-7) (Lead Author of Chapter 2) of UN Environment Programme. Locally she serves as a Member of the Strengthen Emergency Preparedness and Response Strategic Committee of the Hong Kong Red Cross.

Urbanisation has been bringing extensive land use and land cover change over the last several decades, which has an impact on the urban climate and also further affects public health and energy consumption due to human activities. The local climate zone (LCZ) classification system was proposed in 2012 to depict the complexity of urban morphology. The study of local climate zones (LCZ) links urban morphology, land use and land cover types, human activity, and thermal properties, and provides a standard framework for studying urban climatic issues. In recent years, the LCZ scheme has attracted more and more attention from climatologists, urban planners, environmental engineers, as well as architects due to its combination of urban climatic scientific research outputs and urban planning and morphology language. In this guest lecture, machine learning will be used to automatically classify and detect LCZs.  Then, various applications of LCZ are demonstrated in practice, including applications to urban heat islands, land use and land cover analysis, wind environment, energy consumption, thermal comfort studies and so on

Dr. Yi Zhang is an Associate Professor and Doctoral Supervisor at the Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School. She holds a Ph.D. from the University of Cambridge, UK. She serves as the Chinese Representative Expert on the International Energy Agency Committee on Low-Carbon Cities.
Dr. Zhang leads a research team focusing on high spatiotemporal resolution simulation and planning for AI-enhanced zero-carbon cities. Her research output includes over 100 peer-reviewed papers, with 36 published as First or Corresponding Author in top-tier SCI journals such as Nature Communications, Applied Energy, Sustainable Cities and Society, and Energy. Several of her papers have been designated as Highly Cited Papers. She has chaired or participated in 8 national and provincial or ministerial-level research projects, and is engaged in international collaboration through IEA-EBC Annex 82, “Energy Flexible Buildings for Resilient Low Carbon Energy Systems.” Dr. Zhang has received multiple major awards, including the First Prize in the Technological Invention Award of the Guangdong Provincial Science and Technology Awards, and was recognized as one of the World’s Top 2% Scientists in 2025.

With the rapid integration of high shares of zero-carbon energy into urban power systems, cities are facing increasing challenges caused by the temporal and spatial mismatch between fluctuating renewable generation and dynamic energy demand. This challenge is particularly critical in high-density energy-consuming cities, where buildings, electric vehicles, rooftop photovoltaics, battery storage, and flexible loads represent substantial demand-side resources. This lecture introduces an AI-powered framework for the simulation, planning, and operation of zero-carbon energy-use cities. It will present high spatiotemporal resolution methods for identifying urban demand-side resources, modelling electric vehicle travel and charging behaviours, evaluating rooftop PV potential and battery storage capacity, and analysing the cross-scale interactions among users, buildings, and cities. The lecture will further discuss how AI can support integrated investment, design, and operation of power-interactive communities, with applications in PV-storage-direct-current-flexibility systems and real-world low-carbon urban energy projects.

Professor Junjie Chen is an Assistant Professor in the Department of Real Estate and Construction and Deputy Director of iLab. His research sits at the intersection of construction management, artificial intelligence, robotics, and social science, with a focus on robotics in the built environment, smart facility management, and human-robot interaction.
He has secured more than HK$5.2 million in research funding and published extensively in leading journals, with his work earning multiple international awards, including a Geneva Invention Gold Medal. His research has been translated into real-world applications, including AI-powered building inspection and assessment systems deployed in public housing and university facilities. In addition to his research and industry collaborations, Professor Chen is an active educator, teaching across several courses, delivering international guest lectures, and serving as an external examiner for doctoral programs at leading universities.

Architects and construction scientists have long been intrigued by a future where robots become pervasive in the built environment. Recent technological advancement has made this once utopian vision appear more feasible than ever. However, in the discourse of construction robotics, there seems to be insufficient discussion on the relation between robots and buildings, the two key entities involved. This talk delves into the robot-building dyad by examining how their interactive dynamics evolves over time: (a) from an objectified dualism in the past, (b) to an increasingly blurred boundary in the present, and (c) to a prospective symbiosis with mutual benefits. The presenter argues that this shifting dynamic is underpinned by large-scale digitalization and the empowerment of artificial intelligence (AI). He will share his experience and lessons learnt in driving this shift, including AI-driven inspection agents, automatic condition assessment, etc. The talk will conclude by outlining exciting future opportunities such as robot-building co-adaptation, robot charging stations, and human-robot co-existence. The goal is to stimulate further discussion on the critical relation between robots and buildings, gaining a clearer view of which could help overcome the theoretical dilemma hindering wide-spread robotization in the built environment.

Dr. Huazhu Fu is a Principal Scientist at the Institute of High Performance Computing (IHPC), A*STAR, Singapore. His research focuses on medical image analysis, AI for healthcare, and trustworthy AI. With over 200 publications in leading conferences and prestigious journals, including Nature Communications, Cell Reports Medicine, and IEEE TPAMI. His works have garnered more than 33,000 citations on Google Scholar. Dr. Fu has received numerous accolades, including Best Paper Awards at ICME 2021, MICCAI-OMIA 2022, MICCAI-DeCAF 2023, and MICCAI-OMIA 2024. He has been recognized as a "Highly Cited Researcher" by Clarivate and among the "Top 2% Scientists Worldwide" by Stanford. He serves as an Associate Editor for several distinguished journals: IEEE TMI, IEEE TNNLS, and IEEE JBHI.

Artificial intelligence (AI) has shown transformative potential in healthcare, particularly in medical imaging and clinical decision support. However, real-world deployment of AI systems remains hindered by two fundamental challenges: lack of trustworthiness and limited clinical usability. In this talk, I will discuss recent advances aimed at bridging these gaps. First, I will also present Federated learning (FL) is an emerging distributed machine learning paradigm that leverages decentralized data from multiple clients to jointly train a shared global model under the coordination of a central server, without sharing the individuals’ data. Second, I will introduce methodologies for uncertainty quantification and out-of-distribution detection, enabling AI models to recognize when their predictions may be unreliable—a critical feature for patient safety. Together, these efforts demonstrate a pathway toward developing AI systems that are not only technically robust but also aligned with the needs and workflows of frontline healthcare professionals.

Farah Shamout is an Assistant Professor of Computer Engineering at NYU Abu Dhabi, where she leads the Clinical Artificial Intelligence Lab. She is also affiliated with NYU Tandon School of Engineering (Computer Science and Biomedical Engineering) and NYU Langone Health (Radiology).
At the Clinical AI Lab, her research focuses on developing machine learning methods and systems using heterogeneous real-world data for applications in precision health, including electronic health records and medical imaging. Her work emphasizes multi-modal learning, foundation models, and trustworthy AI, with the goal of improving performance and clinical utility in real-world settings.
Dr. Shamout completed her DPhil (PhD) in Engineering Science at the University of Oxford as a Rhodes Scholar, where she was a member of Balliol College. She holds a BSc in Computer Engineering (cum laude) from NYU Abu Dhabi.

Healthcare data is inherently multimodal, generated through heterogeneous and often asynchronous data streams, such as longitudinal electronic health records, medical imaging, and unstructured clinical notes. Recent advances in AI and machine learning have enabled new approaches for integrating these diverse modalities to improve clinical prediction, risk stratification, and patient monitoring. This talk will present our recent research in multimodal AI for healthcare, highlighting both the opportunities and technical challenges of learning from diverse data modalities. We will discuss key methodological advances in multimodal representation learning, cross-modal alignment, and data fusion using deep learning, as well as emerging opportunities at the intersection of multimodal learning and agentic AI. The talk will also examine the role of benchmark datasets and robust evaluation frameworks for assessing model generalization, calibration and clinical utility across real-world healthcare settings.

Mr. Miao is the CFO of Ant Health. He also leads the Globalization Office of Ant Health. Mr. Miao joined Ant Group in Oct 2018 and has held the position of Finance Director. He is appointed as CFO of Ant Health in 2024. Prior to joining Ant Group, Mr. Miao worked at KPMG, Microsoft and Royal Dutch Shell. Mr. Miao obtained his Bachelor’s degree from Renmin University of China and his MBA degree from Darden Business Scholl of University of Virginia.

Ant Health is part of Ant Group, which is the one of the largest tech firm focusing on digital lifestyle service, digital finance and digital healthcare. Cooperating with more than 1000 physicians, Ant Health have deeply invested in medical AI. In 2025, Ant Health launched a AI native health App, called AFU, in China. With the power of AI, AFU is helping users to have more accessible medical services, whomever they are and wherever they are. The registered users reached more than 100 million in less than 9 months since it was launched. In this session, we will talk about the development of AFU and how AI is changing the medical service in China.

Dr. Jun Jiang is a Professor at the University of Science and Technology of China specializing in theoretical, physical, and materials chemistry. His research focuses on multi-scale modeling and machine learning methods for simulating charge kinetics in complex systems, with applications spanning photocatalysis, photochemistry, biochemistry, molecular electronics, and photonics.
He has published more than 50 papers in leading journals, including Nature Energy, Journal of the American Chemical Society, Angewandte Chemie, Physical Review Letters, and Advanced Materials. Dr. Jiang is a recipient of the National Science Fund for Distinguished Young Scholars (China) and has received several prestigious honors, including the Young Theoretical Chemistry Investigator Award of the Chinese Chemical Society and the Distinguished Lectureship Award of the Chemical Society of Japan.

The realization of automated chemical experiments by robots unveiled the prelude of artificial intelligent laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. We have recently built a robotic AI-chemist system that is capable of proposing scientific hypothesis after reading/digesting existing literature, executing a full set of experiments (synthesis, characterization, and performance testing) for multiple chemical tasks, and building predictive models utilizing theoretical calculations with experimental data feedback, allowing to propose new hypothesis for next iteration. With the help of computations, AI chemist has the ability to find the optimal result beyond the chemical space covered by the experiments. It means that we have created a robotic AI chemist that is capable of executing all-round chemical research with data driven intelligence. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to chemical R&D.

Dr. Zhang has more than 15 years of experience in drug discovery and translational medicine research. She has worked for domestic and multinational pharmaceutical companies including Hengrui Pharmaceuticals, Johnson & Johnson, and Novartis. Her research covers multiple disease areas including oncology, liver fibrosis, and hepatitis B. She contributed greatly to novel target discovery and mechanism research, as well as promoting novel drug discovery programs.
Prior to joining Insilico, Dr. Zhang served as CSO, Head of Discovery Biology and Translational Medicine at Shanghai De Novo Pharmatech, where she led a comprehensive preclinical team covering discovery biology, translational medicine, and IND-enabling research to advance multiple programs into clinical and IND-enabling studies. During her tenure at Novartis, Dr. Zhang played a critical role in delivering the first-in-class asset MAK683 currently at clinical phase 2.
Dr. Zhang graduated from the University of Texas at Austin with a PhD in 2003. Prior to that, she received her B.S. in Biochemistry and M.S. in Biochemistry and Molecular Biology from Peking University.

Drug discovery and development process remains costly and time-intensive with high attrition rates. Recent advances in generative artificial intelligence (AI), including large language models (LLMs) and bioinformatics, are transforming key stages of R&D including target identification, compound design, and biomarker discovery, et al.
In this presentation, I will provide a brief overview of the evolution of innovative AI technologies and their integration into drug discovery workflows in Insilico Medicine with specific case studies.

Currently serving as Chief Research Scientist at XtalPi, he has been leading collaborative research projects with major global pharmaceutical companies since joining the company in 2016. He also spearheads the development and application of advanced computational technologies for drug discovery and development.
He earned his bachelor's degree from the University of Science and Technology of China in 2006 and completed his Ph.D. at the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, in 2011. Between 2010 and 2016, he conducted postdoctoral research at The University of Hong Kong and the University of Maryland, focusing on cutting-edge computational approaches in chemistry and drug discovery.

This presentation introduces how an AI- and robotics-enabled technology infrastructure, originally developed for drug discovery, is being extended to accelerate materials innovation. It explores the convergence of artificial intelligence, automation, high-throughput experimentation, and digitalization to transform the traditional trial-and-error approach into a data-driven, closed-loop discovery process—enabling faster development of next-generation materials with greater efficiency and scalability.

Dr. Long Zeng is Doctoral Supervisor with a PhD from the Hong Kong University of Science and Technology. He leads the Intelligent Manufacturing and Machine Vision Research Group and serves as Director of the Tsinghua–Purdue Intelligent Service Robot Technology Joint Research Center.
His research focuses on Industrial Embodied Intelligence, i.e., new AI methods exploring the unique characteristics of industrial data to solve challenging problems in both industrial product design and robotic manufacturing scenarios. His team proposed a novel knowledge-driven technical framework for embodied intelligent industrial robotics and new deep learning models for industrial parts. He has published more than 90 papers in leading international journals and conferences, such as JMS, RCIM, RSS, ICRA, CVPR, ICCV.
His research achievements, encompassing over 40 invention patents, have been transferred to multiple startups, such as Pudu (普渡科技), Fuewi (富唯智能), Yulin (驭灵科技), and MetaForm (元型科技).

Embodied Intelligent Industrial Robotics (EIIR) is an integration of embodied intelligence and industrial robots. It aims to enable robots to work more efficiently, accurately, reliably, and safely in diversified industrial scenarios. This session will share a new knowledge-driven technical framework for EIIR and their progresses from Dr. Zeng’s research groups. Unlike existing embodied intelligent robotics, a successful EIIR robot satisfying abovementioned requirements is required to have at least general knowledge, working-environment knowledge, and operating-object knowledge, which is not supported well by existing LLMs. Thus, the knowledge-driven framework has great potential to grow into a new research field and provide a new paradigm for the next generation of intelligent manufacturing systems.